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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-09 12:33:01
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11.7k
| library_name
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ramonmedeiro1/bertimbau-products-reviews-pt-br
|
ramonmedeiro1
| 2023-09-10T22:52:13Z | 117 | 5 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"feedback",
"products",
"pt",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-09-02T18:12:25Z |
---
language:
- pt
metrics:
- accuracy
tags:
- feedback
- products
---
# Introdução
Modelo treinado a partir do Bertimbau da Neuralmind (https://huggingface.co/neuralmind/bert-base-portuguese-cased)
em um dataset chamado B2W-Reviews01. Que é um corpus aberto de reviews de produtos.
Ele contém mais de 130 mil avaliações de clientes de comércio eletrônico, coletadas no site da Americanas.com (https://github.com/americanas-tech/b2w-reviews01)
O modelo rodou por apenas 50 minutos (3 épocas) numa instância do google com a GPU T4.
O propósito desse projeto é totalmente para fins didáticos, onde a ideia é mostrar como fazer fine tunning de modelos para outras
tarefas de NLP além da geração de textos. Encorajo quem encotrar esse repositório à rodar ele por muito mais tempo para conseguir melhores
resultados.
# Resultados
* Epoch 1:
- Training Loss: 0.863100
- Validation Loss: 0.873007
- Accuracy: 0.621733
- f1_score: 0.491815
* Epoch 2:
- Training Loss: 0.802800
- Validation Loss: 0.897009
- Accuracy: 0.620914
- f1_score: 0.554796
* Epoch 3:
- Training Loss: 0.692400
- Validation Loss: 0.966356
- Accuracy: 0.619210
- f1_score: 0.557672
# Github
No repositório (https://github.com/ramoonmedeiro/LLMTasks/tree/main/text-classification) pode ser encontrado o notebook na qual o fine tunning
foi realizado.
|
jennyc/bert-base-cased
|
jennyc
| 2023-09-10T22:37:14Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-09-10T22:30:53Z |
---
tags:
- generated_from_trainer
model-index:
- name: bert-base-cased
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-cased
This model was trained from scratch on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
hyungtak/ko-Llama2-7B-chat
|
hyungtak
| 2023-09-10T22:33:12Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-10T22:32:59Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.0.dev0
|
jennyc/bert-finetuned-squad
|
jennyc
| 2023-09-10T22:28:17Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-08-26T15:10:13Z |
---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
model-index:
- name: bert-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
moonlightnexus/wonder-anime
|
moonlightnexus
| 2023-09-10T22:24:07Z | 38 | 2 |
diffusers
|
[
"diffusers",
"text-to-image",
"en",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-09-10T18:45:12Z |
---
license: other
language:
- en
library_name: diffusers
pipeline_tag: text-to-image
---
|
Yntec/DucHaitenClassicAnime768
|
Yntec
| 2023-09-10T22:21:14Z | 500 | 3 |
diffusers
|
[
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"Classic Anime",
"DucHaiten",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-07-17T01:34:01Z |
---
license: creativeml-openrail-m
tags:
- stable-diffusion
- stable-diffusion-diffusers
- diffusers
- text-to-image
- Classic Anime
- DucHaiten
---
# DucHaiten Classic Anime
768 version of this model with the Waifu 1.4 VAE baked in for the inference API based on the Fp16NoEma checkpoint. Use (80s anime style) or (gtav style) to enhance the style.
If you like his content, support him at:
https://linktr.ee/Duc_Haiten
Original page:
https://civitai.com/models/8542?modelVersionId=16168
|
the-neural-networker/xlm-roberta-base-finetuned-panx-all
|
the-neural-networker
| 2023-09-10T22:03:23Z | 110 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-09-09T17:39:50Z |
---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-all
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-all
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5155
- F1: 0.8005
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.4866 | 1.0 | 438 | 0.3960 | 0.6932 |
| 0.2614 | 2.0 | 876 | 0.3196 | 0.7360 |
| 0.1845 | 3.0 | 1314 | 0.3218 | 0.7698 |
| 0.1353 | 4.0 | 1752 | 0.3439 | 0.7701 |
| 0.096 | 5.0 | 2190 | 0.3638 | 0.7885 |
| 0.0689 | 6.0 | 2628 | 0.3983 | 0.7991 |
| 0.0474 | 7.0 | 3066 | 0.4359 | 0.7962 |
| 0.0317 | 8.0 | 3504 | 0.4701 | 0.8032 |
| 0.0219 | 9.0 | 3942 | 0.5055 | 0.8032 |
| 0.0153 | 10.0 | 4380 | 0.5155 | 0.8005 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3
|
armstrongrichi/sdxl_picarmstrongrichi
|
armstrongrichi
| 2023-09-10T20:58:43Z | 1 | 1 |
diffusers
|
[
"diffusers",
"text-to-image",
"autotrain",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0",
"region:us"
] |
text-to-image
| 2023-09-10T20:58:37Z |
---
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: photo of a picarmstrongrichi person
tags:
- text-to-image
- diffusers
- autotrain
inference: true
---
# DreamBooth trained by AutoTrain
Text encoder was not trained.
|
JCTN/fast-repo
|
JCTN
| 2023-09-10T20:47:59Z | 0 | 1 | null |
[
"code",
"en",
"region:us"
] | null | 2023-06-03T00:13:50Z |
---
language:
- en
tags:
- code
---
# This is what powered almost all of my colab
Mostly uses LZ4 compression, which means you'll need a specialized program to extract it, especially in windows.
For Windows users, I recommend using [7zip-zstd](https://github.com/mcmilk/7-Zip-zstd/releases/latest) (it's 7zip but with lz4 support and more)
For Linux users, use tar with liblz4-tool like this: `tar -xI lz4 -f repo.tar.lz4`
|
s3nh/Undi95-MLewdBoros-L2-13B-GGUF
|
s3nh
| 2023-09-10T20:23:08Z | 108 | 2 |
transformers
|
[
"transformers",
"gguf",
"text-generation",
"zh",
"en",
"license:openrail",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-09-10T20:07:46Z |
---
license: openrail
pipeline_tag: text-generation
library_name: transformers
language:
- zh
- en
---
## Original model card
Buy me a coffee if you like this project ;)
<a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a>
#### Description
GGUF Format model files for [This project](https://huggingface.co/Undi95/MLewdBoros-L2-13B).
### GGUF Specs
GGUF is a format based on the existing GGJT, but makes a few changes to the format to make it more extensible and easier to use. The following features are desired:
Single-file deployment: they can be easily distributed and loaded, and do not require any external files for additional information.
Extensible: new features can be added to GGML-based executors/new information can be added to GGUF models without breaking compatibility with existing models.
mmap compatibility: models can be loaded using mmap for fast loading and saving.
Easy to use: models can be easily loaded and saved using a small amount of code, with no need for external libraries, regardless of the language used.
Full information: all information needed to load a model is contained in the model file, and no additional information needs to be provided by the user.
The key difference between GGJT and GGUF is the use of a key-value structure for the hyperparameters (now referred to as metadata), rather than a list of untyped values.
This allows for new metadata to be added without breaking compatibility with existing models, and to annotate the model with additional information that may be useful for
inference or for identifying the model.
### Perplexity params
Model Measure Q2_K Q3_K_S Q3_K_M Q3_K_L Q4_0 Q4_1 Q4_K_S Q4_K_M Q5_0 Q5_1 Q5_K_S Q5_K_M Q6_K Q8_0 F16
7B perplexity 6.7764 6.4571 6.1503 6.0869 6.1565 6.0912 6.0215 5.9601 5.9862 5.9481 5.9419 5.9208 5.9110 5.9070 5.9066
13B perplexity 5.8545 5.6033 5.4498 5.4063 5.3860 5.3608 5.3404 5.3002 5.2856 5.2706 5.2785 5.2638 5.2568 5.2548 5.2543
### inference
TODO
# Original model card
|
vladfatu/ppo-Huggy
|
vladfatu
| 2023-09-10T20:21:12Z | 3 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-09-10T20:21:08Z |
---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: vladfatu/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Varun29/my-ai-project
|
Varun29
| 2023-09-10T20:15:24Z | 5 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-09-10T20:10:37Z |
---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### My--ai-project- Dreambooth model trained by Varun29 following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: IIITB-109
Sample pictures of this concept:
.jpg)
|
Ori/lama-2-13b-peft-mh-retrieval-at-1-v2-seed-0
|
Ori
| 2023-09-10T20:10:58Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"region:us"
] | null | 2023-09-10T19:47:49Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
osieosie/llama-samsum-4bit-13b-bnb-seed65
|
osieosie
| 2023-09-10T20:04:53Z | 1 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-10T20:04:51Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.5.0.dev0
|
mindchain/llama2-ooiiioo
|
mindchain
| 2023-09-10T20:03:10Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-10T20:03:03Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.6.0.dev0
|
Ori/lama-2-13b-peft-mh-no-retrieval-v2-seed-0
|
Ori
| 2023-09-10T19:41:38Z | 1 | 0 |
peft
|
[
"peft",
"safetensors",
"region:us"
] | null | 2023-09-10T19:17:00Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
aaronamortegui/relismoilumi
|
aaronamortegui
| 2023-09-10T19:37:27Z | 87 | 1 |
diffusers
|
[
"diffusers",
"safetensors",
"stable-diffusion",
"text-to-image",
"arxiv:2112.10752",
"arxiv:2202.00512",
"arxiv:1910.09700",
"license:openrail++",
"region:us"
] |
text-to-image
| 2023-03-09T01:49:06Z |
---
license: openrail++
tags:
- stable-diffusion
- text-to-image
pinned: true
---
# Stable Diffusion v2-1 Model Card
This model card focuses on the model associated with the Stable Diffusion v2-1 model, codebase available [here](https://github.com/Stability-AI/stablediffusion).
This `stable-diffusion-2-1` model is fine-tuned from [stable-diffusion-2](https://huggingface.co/stabilityai/stable-diffusion-2) (`768-v-ema.ckpt`) with an additional 55k steps on the same dataset (with `punsafe=0.1`), and then fine-tuned for another 155k extra steps with `punsafe=0.98`.
- Use it with the [`stablediffusion`](https://github.com/Stability-AI/stablediffusion) repository: download the `v2-1_768-ema-pruned.ckpt` [here](https://huggingface.co/stabilityai/stable-diffusion-2-1/blob/main/v2-1_768-ema-pruned.ckpt).
- Use it with 🧨 [`diffusers`](#examples)
## Model Details
- **Developed by:** Robin Rombach, Patrick Esser
- **Model type:** Diffusion-based text-to-image generation model
- **Language(s):** English
- **License:** [CreativeML Open RAIL++-M License](https://huggingface.co/stabilityai/stable-diffusion-2/blob/main/LICENSE-MODEL)
- **Model Description:** This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses a fixed, pretrained text encoder ([OpenCLIP-ViT/H](https://github.com/mlfoundations/open_clip)).
- **Resources for more information:** [GitHub Repository](https://github.com/Stability-AI/).
- **Cite as:**
@InProceedings{Rombach_2022_CVPR,
author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn},
title = {High-Resolution Image Synthesis With Latent Diffusion Models},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {10684-10695}
}
## Examples
Using the [🤗's Diffusers library](https://github.com/huggingface/diffusers) to run Stable Diffusion 2 in a simple and efficient manner.
```bash
pip install diffusers transformers accelerate scipy safetensors
```
Running the pipeline (if you don't swap the scheduler it will run with the default DDIM, in this example we are swapping it to DPMSolverMultistepScheduler):
```python
from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler
model_id = "stabilityai/stable-diffusion-2-1"
# Use the DPMSolverMultistepScheduler (DPM-Solver++) scheduler here instead
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
pipe = pipe.to("cuda")
prompt = "a photo of an astronaut riding a horse on mars"
image = pipe(prompt).images[0]
image.save("astronaut_rides_horse.png")
```
**Notes**:
- Despite not being a dependency, we highly recommend you to install [xformers](https://github.com/facebookresearch/xformers) for memory efficient attention (better performance)
- If you have low GPU RAM available, make sure to add a `pipe.enable_attention_slicing()` after sending it to `cuda` for less VRAM usage (to the cost of speed)
# Uses
## Direct Use
The model is intended for research purposes only. Possible research areas and tasks include
- Safe deployment of models which have the potential to generate harmful content.
- Probing and understanding the limitations and biases of generative models.
- Generation of artworks and use in design and other artistic processes.
- Applications in educational or creative tools.
- Research on generative models.
Excluded uses are described below.
### Misuse, Malicious Use, and Out-of-Scope Use
_Note: This section is originally taken from the [DALLE-MINI model card](https://huggingface.co/dalle-mini/dalle-mini), was used for Stable Diffusion v1, but applies in the same way to Stable Diffusion v2_.
The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.
#### Out-of-Scope Use
The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.
#### Misuse and Malicious Use
Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to:
- Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc.
- Intentionally promoting or propagating discriminatory content or harmful stereotypes.
- Impersonating individuals without their consent.
- Sexual content without consent of the people who might see it.
- Mis- and disinformation
- Representations of egregious violence and gore
- Sharing of copyrighted or licensed material in violation of its terms of use.
- Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use.
## Limitations and Bias
### Limitations
- The model does not achieve perfect photorealism
- The model cannot render legible text
- The model does not perform well on more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere”
- Faces and people in general may not be generated properly.
- The model was trained mainly with English captions and will not work as well in other languages.
- The autoencoding part of the model is lossy
- The model was trained on a subset of the large-scale dataset
[LAION-5B](https://laion.ai/blog/laion-5b/), which contains adult, violent and sexual content. To partially mitigate this, we have filtered the dataset using LAION's NFSW detector (see Training section).
### Bias
While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases.
Stable Diffusion was primarily trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/),
which consists of images that are limited to English descriptions.
Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for.
This affects the overall output of the model, as white and western cultures are often set as the default. Further, the
ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts.
Stable Diffusion v2 mirrors and exacerbates biases to such a degree that viewer discretion must be advised irrespective of the input or its intent.
## Training
**Training Data**
The model developers used the following dataset for training the model:
- LAION-5B and subsets (details below). The training data is further filtered using LAION's NSFW detector, with a "p_unsafe" score of 0.1 (conservative). For more details, please refer to LAION-5B's [NeurIPS 2022](https://openreview.net/forum?id=M3Y74vmsMcY) paper and reviewer discussions on the topic.
**Training Procedure**
Stable Diffusion v2 is a latent diffusion model which combines an autoencoder with a diffusion model that is trained in the latent space of the autoencoder. During training,
- Images are encoded through an encoder, which turns images into latent representations. The autoencoder uses a relative downsampling factor of 8 and maps images of shape H x W x 3 to latents of shape H/f x W/f x 4
- Text prompts are encoded through the OpenCLIP-ViT/H text-encoder.
- The output of the text encoder is fed into the UNet backbone of the latent diffusion model via cross-attention.
- The loss is a reconstruction objective between the noise that was added to the latent and the prediction made by the UNet. We also use the so-called _v-objective_, see https://arxiv.org/abs/2202.00512.
We currently provide the following checkpoints:
- `512-base-ema.ckpt`: 550k steps at resolution `256x256` on a subset of [LAION-5B](https://laion.ai/blog/laion-5b/) filtered for explicit pornographic material, using the [LAION-NSFW classifier](https://github.com/LAION-AI/CLIP-based-NSFW-Detector) with `punsafe=0.1` and an [aesthetic score](https://github.com/christophschuhmann/improved-aesthetic-predictor) >= `4.5`.
850k steps at resolution `512x512` on the same dataset with resolution `>= 512x512`.
- `768-v-ema.ckpt`: Resumed from `512-base-ema.ckpt` and trained for 150k steps using a [v-objective](https://arxiv.org/abs/2202.00512) on the same dataset. Resumed for another 140k steps on a `768x768` subset of our dataset.
- `512-depth-ema.ckpt`: Resumed from `512-base-ema.ckpt` and finetuned for 200k steps. Added an extra input channel to process the (relative) depth prediction produced by [MiDaS](https://github.com/isl-org/MiDaS) (`dpt_hybrid`) which is used as an additional conditioning.
The additional input channels of the U-Net which process this extra information were zero-initialized.
- `512-inpainting-ema.ckpt`: Resumed from `512-base-ema.ckpt` and trained for another 200k steps. Follows the mask-generation strategy presented in [LAMA](https://github.com/saic-mdal/lama) which, in combination with the latent VAE representations of the masked image, are used as an additional conditioning.
The additional input channels of the U-Net which process this extra information were zero-initialized. The same strategy was used to train the [1.5-inpainting checkpoint](https://huggingface.co/runwayml/stable-diffusion-inpainting).
- `x4-upscaling-ema.ckpt`: Trained for 1.25M steps on a 10M subset of LAION containing images `>2048x2048`. The model was trained on crops of size `512x512` and is a text-guided [latent upscaling diffusion model](https://arxiv.org/abs/2112.10752).
In addition to the textual input, it receives a `noise_level` as an input parameter, which can be used to add noise to the low-resolution input according to a [predefined diffusion schedule](configs/stable-diffusion/x4-upscaling.yaml).
- **Hardware:** 32 x 8 x A100 GPUs
- **Optimizer:** AdamW
- **Gradient Accumulations**: 1
- **Batch:** 32 x 8 x 2 x 4 = 2048
- **Learning rate:** warmup to 0.0001 for 10,000 steps and then kept constant
## Evaluation Results
Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0,
5.0, 6.0, 7.0, 8.0) and 50 steps DDIM sampling steps show the relative improvements of the checkpoints:
Evaluated using 50 DDIM steps and 10000 random prompts from the COCO2017 validation set, evaluated at 512x512 resolution. Not optimized for FID scores.
## Environmental Impact
**Stable Diffusion v1** **Estimated Emissions**
Based on that information, we estimate the following CO2 emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact.
- **Hardware Type:** A100 PCIe 40GB
- **Hours used:** 200000
- **Cloud Provider:** AWS
- **Compute Region:** US-east
- **Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid):** 15000 kg CO2 eq.
## Citation
@InProceedings{Rombach_2022_CVPR,
author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn},
title = {High-Resolution Image Synthesis With Latent Diffusion Models},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {10684-10695}
}
*This model card was written by: Robin Rombach, Patrick Esser and David Ha and is based on the [Stable Diffusion v1](https://github.com/CompVis/stable-diffusion/blob/main/Stable_Diffusion_v1_Model_Card.md) and [DALL-E Mini model card](https://huggingface.co/dalle-mini/dalle-mini).*
|
omthkkr/speecht5_finetuned_voxpopuli_sl
|
omthkkr
| 2023-09-10T19:28:13Z | 82 | 0 |
transformers
|
[
"transformers",
"pytorch",
"speecht5",
"text-to-audio",
"generated_from_trainer",
"text-to-speech",
"dataset:facebook/voxpopuli",
"base_model:microsoft/speecht5_tts",
"base_model:finetune:microsoft/speecht5_tts",
"license:mit",
"endpoints_compatible",
"region:us"
] |
text-to-speech
| 2023-09-10T18:21:41Z |
---
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
- text-to-speech
datasets:
- facebook/voxpopuli
model-index:
- name: speecht5_finetuned_voxpopuli_sl
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# speecht5_finetuned_voxpopuli_sl
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the VoxPopuli dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4594
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.5181 | 8.46 | 500 | 0.4795 |
| 0.498 | 16.91 | 1000 | 0.4594 |
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
s3nh/Photolens-llama-2-13b-langchain-chat-GGUF
|
s3nh
| 2023-09-10T19:26:59Z | 57 | 2 |
transformers
|
[
"transformers",
"gguf",
"text-generation",
"en",
"license:openrail",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-09-01T11:23:57Z |
---
license: openrail
pipeline_tag: text-generation
library_name: transformers
language:
- en
---
## Original model card
Buy me a coffee if you like this project ;)
<a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a>
#### Description
GGUF Format model files for [This project](https://huggingface.co/Photolens/llama-2-13b-langchain-chat).
### GGUF Specs
GGUF is a format based on the existing GGJT, but makes a few changes to the format to make it more extensible and easier to use. The following features are desired:
Single-file deployment: they can be easily distributed and loaded, and do not require any external files for additional information.
Extensible: new features can be added to GGML-based executors/new information can be added to GGUF models without breaking compatibility with existing models.
mmap compatibility: models can be loaded using mmap for fast loading and saving.
Easy to use: models can be easily loaded and saved using a small amount of code, with no need for external libraries, regardless of the language used.
Full information: all information needed to load a model is contained in the model file, and no additional information needs to be provided by the user.
The key difference between GGJT and GGUF is the use of a key-value structure for the hyperparameters (now referred to as metadata), rather than a list of untyped values.
This allows for new metadata to be added without breaking compatibility with existing models, and to annotate the model with additional information that may be useful for
inference or for identifying the model.
### Perplexity params
Model Measure Q2_K Q3_K_S Q3_K_M Q3_K_L Q4_0 Q4_1 Q4_K_S Q4_K_M Q5_0 Q5_1 Q5_K_S Q5_K_M Q6_K Q8_0 F16
7B perplexity 6.7764 6.4571 6.1503 6.0869 6.1565 6.0912 6.0215 5.9601 5.9862 5.9481 5.9419 5.9208 5.9110 5.9070 5.9066
13B perplexity 5.8545 5.6033 5.4498 5.4063 5.3860 5.3608 5.3404 5.3002 5.2856 5.2706 5.2785 5.2638 5.2568 5.2548 5.2543
### inference
TODO
# Original model card
|
adyprat/a2c-PandaReachDense-v3
|
adyprat
| 2023-09-10T19:10:17Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-10T19:04:49Z |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v3
type: PandaReachDense-v3
metrics:
- type: mean_reward
value: -0.22 +/- 0.13
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
robertquest/controlnetv1.1
|
robertquest
| 2023-09-10T19:01:35Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-09-10T16:47:14Z |
---
license: creativeml-openrail-m
---
|
CyberHarem/oberon_fgo
|
CyberHarem
| 2023-09-10T18:48:41Z | 0 | 0 | null |
[
"art",
"text-to-image",
"dataset:CyberHarem/oberon_fgo",
"license:mit",
"region:us"
] |
text-to-image
| 2023-09-10T18:36:00Z |
---
license: mit
datasets:
- CyberHarem/oberon_fgo
pipeline_tag: text-to-image
tags:
- art
---
# Lora of oberon_fgo
This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11).
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 6600, you need to download `6600/oberon_fgo.pt` as the embedding and `6600/oberon_fgo.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The best step we recommend is 6600**, with the score of 0.905. The trigger words are:
1. `oberon_fgo`
2. `male_focus, bangs, blue_eyes, crown, medium_hair, cape, grey_hair, wings, smile, insect_wings, black_hair`
For the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
These are available steps:
| Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | pattern_6 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata |
|:---------|:----------|:------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------|
| **6600** | **0.905** | [**Download**](6600/oberon_fgo.zip) |  |  |  |  |  |  |  | [<NSFW, click to see>](6600/previews/bondage.png) |  |  |  | [<NSFW, click to see>](6600/previews/nude.png) | [<NSFW, click to see>](6600/previews/nude2.png) |  |  |
| 6160 | 0.853 | [Download](6160/oberon_fgo.zip) |  |  |  |  |  |  |  | [<NSFW, click to see>](6160/previews/bondage.png) |  |  |  | [<NSFW, click to see>](6160/previews/nude.png) | [<NSFW, click to see>](6160/previews/nude2.png) |  |  |
| 5720 | 0.883 | [Download](5720/oberon_fgo.zip) |  |  |  |  |  |  |  | [<NSFW, click to see>](5720/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5720/previews/nude.png) | [<NSFW, click to see>](5720/previews/nude2.png) |  |  |
| 5280 | 0.863 | [Download](5280/oberon_fgo.zip) |  |  |  |  |  |  |  | [<NSFW, click to see>](5280/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5280/previews/nude.png) | [<NSFW, click to see>](5280/previews/nude2.png) |  |  |
| 4840 | 0.799 | [Download](4840/oberon_fgo.zip) |  |  |  |  |  |  |  | [<NSFW, click to see>](4840/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4840/previews/nude.png) | [<NSFW, click to see>](4840/previews/nude2.png) |  |  |
| 4400 | 0.811 | [Download](4400/oberon_fgo.zip) |  |  |  |  |  |  |  | [<NSFW, click to see>](4400/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4400/previews/nude.png) | [<NSFW, click to see>](4400/previews/nude2.png) |  |  |
| 3960 | 0.854 | [Download](3960/oberon_fgo.zip) |  |  |  |  |  |  |  | [<NSFW, click to see>](3960/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3960/previews/nude.png) | [<NSFW, click to see>](3960/previews/nude2.png) |  |  |
| 3520 | 0.819 | [Download](3520/oberon_fgo.zip) |  |  |  |  |  |  |  | [<NSFW, click to see>](3520/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3520/previews/nude.png) | [<NSFW, click to see>](3520/previews/nude2.png) |  |  |
| 3080 | 0.783 | [Download](3080/oberon_fgo.zip) |  |  |  |  |  |  |  | [<NSFW, click to see>](3080/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3080/previews/nude.png) | [<NSFW, click to see>](3080/previews/nude2.png) |  |  |
| 2640 | 0.825 | [Download](2640/oberon_fgo.zip) |  |  |  |  |  |  |  | [<NSFW, click to see>](2640/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2640/previews/nude.png) | [<NSFW, click to see>](2640/previews/nude2.png) |  |  |
| 2200 | 0.760 | [Download](2200/oberon_fgo.zip) |  |  |  |  |  |  |  | [<NSFW, click to see>](2200/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2200/previews/nude.png) | [<NSFW, click to see>](2200/previews/nude2.png) |  |  |
| 1760 | 0.719 | [Download](1760/oberon_fgo.zip) |  |  |  |  |  |  |  | [<NSFW, click to see>](1760/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1760/previews/nude.png) | [<NSFW, click to see>](1760/previews/nude2.png) |  |  |
| 1320 | 0.772 | [Download](1320/oberon_fgo.zip) |  |  |  |  |  |  |  | [<NSFW, click to see>](1320/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1320/previews/nude.png) | [<NSFW, click to see>](1320/previews/nude2.png) |  |  |
| 880 | 0.686 | [Download](880/oberon_fgo.zip) |  |  |  |  |  |  |  | [<NSFW, click to see>](880/previews/bondage.png) |  |  |  | [<NSFW, click to see>](880/previews/nude.png) | [<NSFW, click to see>](880/previews/nude2.png) |  |  |
| 440 | 0.669 | [Download](440/oberon_fgo.zip) |  |  |  |  |  |  |  | [<NSFW, click to see>](440/previews/bondage.png) |  |  |  | [<NSFW, click to see>](440/previews/nude.png) | [<NSFW, click to see>](440/previews/nude2.png) |  |  |
|
Saugatkafley/Llama-7B-Chat-GPTQ-lora-nepali-sentence
|
Saugatkafley
| 2023-09-10T18:48:30Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-10T18:48:07Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: gptq
- bits: 4
- tokenizer: None
- dataset: None
- group_size: 128
- damp_percent: 0.01
- desc_act: False
- sym: True
- true_sequential: True
- use_cuda_fp16: False
- model_seqlen: None
- block_name_to_quantize: None
- module_name_preceding_first_block: None
- batch_size: 1
- pad_token_id: None
- disable_exllama: False
### Framework versions
- PEFT 0.5.0
|
HasanPA/distilbert-base-uncased-finetuned-cola
|
HasanPA
| 2023-09-10T18:41:05Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-24T07:17:34Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: cola
split: validation
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5263989868108533
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8446
- Matthews Correlation: 0.5264
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5218 | 1.0 | 535 | 0.4631 | 0.4977 |
| 0.347 | 2.0 | 1070 | 0.5200 | 0.5002 |
| 0.2277 | 3.0 | 1605 | 0.6188 | 0.5193 |
| 0.1783 | 4.0 | 2140 | 0.7643 | 0.5154 |
| 0.1303 | 5.0 | 2675 | 0.8446 | 0.5264 |
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
stefan-it/electra-base-gc4-64k-100000-cased-generator
|
stefan-it
| 2023-09-10T18:39:49Z | 127 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"safetensors",
"electra",
"fill-mask",
"de",
"dataset:german-nlp-group/german_common_crawl",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
language: de
license: mit
datasets:
- german-nlp-group/german_common_crawl
widget:
- text: "Heute ist ein [MASK] Tag"
---
# GC4LM: A Colossal (Biased) language model for German
This repository presents a colossal (and biased) language model for German trained on the recently released
["German colossal, clean Common Crawl corpus"](https://german-nlp-group.github.io/projects/gc4-corpus.html) (GC4),
with a total dataset size of ~844GB.
---
**Disclaimer**: the presented and trained language models in this repository are for **research only** purposes.
The GC4 corpus - that was used for training - contains crawled texts from the internet. Thus, the language models can
be considered as highly biased, resulting in a model that encodes stereotypical associations along gender, race,
ethnicity and disability status. Before using and working with the released checkpoints, it is highly recommended
to read:
[On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?](https://faculty.washington.edu/ebender/papers/Stochastic_Parrots.pdf)
from Emily M. Bender, Timnit Gebru, Angelina McMillan-Major and Shmargaret Shmitchell.
The aim of the released checkpoints is to boost research on large pre-trained language models for German, especially
for identifying biases and how to prevent them, as most research is currently done only for English.
---
Please use the new GitHub Discussions feature in order to discuss or present further research questions.
Feel free to use `#gc4lm` on Twitter 🐦.
|
actionpace/spicyboros-13b-2.2
|
actionpace
| 2023-09-10T18:37:39Z | 1 | 0 | null |
[
"gguf",
"en",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2023-09-10T18:32:52Z |
---
license: other
language:
- en
---
**Some of my own quants:**
* spicyboros-13b-2.2_Q5_1.gguf
**Source:** [jondurbin](https://huggingface.co/jondurbin)
**Source Model:** [spicyboros-13b-2.2](https://huggingface.co/jondurbin/spicyboros-13b-2.2)
**Source models for jondurbin/spicyboros-13b-2.2 (Finetune, jondurbin/airoboros-2.2)**
- [meta-llama/Llama-2-13b-hf](https://huggingface.co/meta-llama/Llama-2-13b-hf) ([Ref](https://huggingface.co/actionpace/Llama-2-13b-hf))
|
DriveMyScream/News_Similarity_Checking
|
DriveMyScream
| 2023-09-10T18:33:49Z | 0 | 0 |
keras
|
[
"keras",
"tf-keras",
"region:us"
] | null | 2023-09-10T18:31:20Z |
---
library_name: keras
---
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
| Hyperparameters | Value |
| :-- | :-- |
| name | Adam |
| weight_decay | None |
| clipnorm | None |
| global_clipnorm | None |
| clipvalue | None |
| use_ema | False |
| ema_momentum | 0.99 |
| ema_overwrite_frequency | None |
| jit_compile | True |
| is_legacy_optimizer | False |
| learning_rate | 0.0010000000474974513 |
| beta_1 | 0.9 |
| beta_2 | 0.999 |
| epsilon | 1e-07 |
| amsgrad | False |
| training_precision | float32 |
## Model Plot
<details>
<summary>View Model Plot</summary>
</details>
|
victornica/molgpt_selfies_100mzinc_384width
|
victornica
| 2023-09-10T18:22:28Z | 139 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:openai-community/gpt2",
"base_model:finetune:openai-community/gpt2",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-09-09T23:21:58Z |
---
license: mit
base_model: gpt2
tags:
- generated_from_trainer
model-index:
- name: molgpt_selfies_100mzinc_384width
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# molgpt_selfies_100mzinc_384width
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5005
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 192
- eval_batch_size: 192
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5000
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:------:|:---------------:|
| 0.578 | 0.1 | 50000 | 0.5535 |
| 0.5564 | 0.2 | 100000 | 0.5450 |
| 0.5487 | 0.3 | 150000 | 0.5389 |
| 0.5429 | 0.4 | 200000 | 0.5336 |
| 0.5369 | 0.5 | 250000 | 0.5275 |
| 0.5306 | 0.6 | 300000 | 0.5213 |
| 0.5236 | 0.7 | 350000 | 0.5146 |
| 0.5163 | 0.8 | 400000 | 0.5079 |
| 0.5095 | 0.9 | 450000 | 0.5022 |
| 0.5055 | 1.0 | 500000 | 0.5005 |
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1
- Datasets 2.14.5
- Tokenizers 0.13.3
|
osieosie/llama-samsum-4bit-7b-bnb-seed87
|
osieosie
| 2023-09-10T18:18:27Z | 2 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-10T14:00:11Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.5.0.dev0
|
Buseak/spellcorrector_1009_v4
|
Buseak
| 2023-09-10T18:17:13Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"canine",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-09-10T17:15:30Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: spellcorrector_1009_v4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# spellcorrector_1009_v4
This model is a fine-tuned version of [google/canine-s](https://huggingface.co/google/canine-s) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3307
- Precision: 0.9681
- Recall: 0.9681
- F1: 0.9681
- Accuracy: 0.9573
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2767 | 1.0 | 1951 | 0.2331 | 0.96 | 0.9562 | 0.9581 | 0.9383 |
| 0.2181 | 2.0 | 3902 | 0.2028 | 0.9524 | 0.9562 | 0.9543 | 0.9450 |
| 0.1776 | 3.0 | 5853 | 0.2019 | 0.96 | 0.9562 | 0.9581 | 0.9498 |
| 0.1519 | 4.0 | 7804 | 0.2038 | 0.9526 | 0.9602 | 0.9563 | 0.9498 |
| 0.1277 | 5.0 | 9755 | 0.2091 | 0.9567 | 0.9681 | 0.9624 | 0.9521 |
| 0.1133 | 6.0 | 11706 | 0.2187 | 0.9449 | 0.9562 | 0.9505 | 0.9540 |
| 0.1041 | 7.0 | 13657 | 0.2378 | 0.9762 | 0.9801 | 0.9781 | 0.9545 |
| 0.0906 | 8.0 | 15608 | 0.2371 | 0.9603 | 0.9641 | 0.9622 | 0.9558 |
| 0.0806 | 9.0 | 17559 | 0.2509 | 0.976 | 0.9721 | 0.9741 | 0.9532 |
| 0.0689 | 10.0 | 19510 | 0.2624 | 0.9681 | 0.9681 | 0.9681 | 0.9563 |
| 0.0623 | 11.0 | 21461 | 0.2623 | 0.976 | 0.9721 | 0.9741 | 0.9559 |
| 0.06 | 12.0 | 23412 | 0.2783 | 0.9643 | 0.9681 | 0.9662 | 0.9564 |
| 0.0537 | 13.0 | 25363 | 0.2938 | 0.976 | 0.9721 | 0.9741 | 0.9569 |
| 0.0507 | 14.0 | 27314 | 0.2976 | 0.9603 | 0.9641 | 0.9622 | 0.9565 |
| 0.0491 | 15.0 | 29265 | 0.3075 | 0.9681 | 0.9681 | 0.9681 | 0.9576 |
| 0.0426 | 16.0 | 31216 | 0.3182 | 0.9681 | 0.9681 | 0.9681 | 0.9571 |
| 0.0426 | 17.0 | 33167 | 0.3154 | 0.9681 | 0.9681 | 0.9681 | 0.9572 |
| 0.0387 | 18.0 | 35118 | 0.3266 | 0.9681 | 0.9681 | 0.9681 | 0.9573 |
| 0.0336 | 19.0 | 37069 | 0.3317 | 0.9681 | 0.9681 | 0.9681 | 0.9574 |
| 0.0341 | 20.0 | 39020 | 0.3307 | 0.9681 | 0.9681 | 0.9681 | 0.9573 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
cosmic-cactus/dqn-SpaceInvadersNoFrameskip-v4
|
cosmic-cactus
| 2023-09-10T18:11:02Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-10T18:10:30Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 391.50 +/- 122.11
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga cosmic-cactus -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga cosmic-cactus -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga cosmic-cactus
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
johaanm/test-planner-alpha-V8.0
|
johaanm
| 2023-09-10T18:10:48Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-10T18:10:44Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.4.0
- PEFT 0.4.0
|
darkandpure/LLama2-qlora-ecom
|
darkandpure
| 2023-09-10T18:08:25Z | 6 | 1 |
peft
|
[
"peft",
"pytorch",
"region:us"
] | null | 2023-08-19T20:26:08Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.6.0.dev0
|
acdg1214/rl_course_vizdoom_health_gathering_supreme
|
acdg1214
| 2023-09-10T18:05:24Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-10T17:41:36Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 8.01 +/- 2.66
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r acdg1214/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
mindchain/llama2-qlora-finetunined-french_aktuell
|
mindchain
| 2023-09-10T18:05:02Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-10T18:04:55Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.6.0.dev0
|
antonwonton/Llama-2-7b-chat-hf-int4-ft-0.75
|
antonwonton
| 2023-09-10T18:01:12Z | 0 | 0 | null |
[
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"base_model:finetune:meta-llama/Llama-2-7b-chat-hf",
"region:us"
] | null | 2023-09-10T18:01:00Z |
---
base_model: meta-llama/Llama-2-7b-chat-hf
tags:
- generated_from_trainer
model-index:
- name: Llama-2-7b-chat-hf-int4-ft-0.75
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Llama-2-7b-chat-hf-int4-ft-0.75
This model is a fine-tuned version of [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 6
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 12
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3
|
Specky/C-Jon
|
Specky
| 2023-09-10T17:56:10Z | 0 | 0 | null |
[
"license:cc-by-nc-nd-4.0",
"region:us"
] | null | 2023-09-09T19:10:32Z |
---
license: cc-by-nc-nd-4.0
---
|
samkitjain/my-pet-dog-xzp
|
samkitjain
| 2023-09-10T17:29:15Z | 0 | 0 | null |
[
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-09-10T17:26:38Z |
---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### My-Pet-dog-xzp Dreambooth model trained by samkitjain following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: IIITB-278
Sample pictures of this concept:
|
chmanoj/xls-r-1B-te
|
chmanoj
| 2023-09-10T17:17:07Z | 21 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"openslr_SLR66",
"generated_from_trainer",
"robust-speech-event",
"hf-asr-leaderboard",
"te",
"dataset:openslr",
"dataset:SLR66",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- te
license: apache-2.0
tags:
- automatic-speech-recognition
- openslr_SLR66
- generated_from_trainer
- robust-speech-event
- hf-asr-leaderboard
datasets:
- openslr
- SLR66
metrics:
- wer
model-index:
- name: xls-r-1B-te
results:
- task:
type: automatic-speech-recognition
name: Speech Recognition
dataset:
type: openslr
name: Open SLR
args: SLR66
metrics:
- type: wer
value: 20.624
name: Test WER
- type: cer
value: 3.979
name: Test CER
- type: wer
value: 26.14777618364419
name: Test WER (without LM)
- type: cer
value: 4.932543184970369
name: Test CER (without LM)
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the OPENSLR_SLR66 - NA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3119
- Wer: 0.2613
### Evaluation metrics
| Metric | Split | Decode with LM | Value |
|:------:|:------:|:--------------:|:---------:|
| WER | Train | No | 5.36 |
| CER | Train | No | 1.11 |
| WER | Test | No | 26.14 |
| CER | Test | No | 4.93 |
| WER | Train | Yes | 5.04 |
| CER | Train | Yes | 1.07 |
| WER | Test | Yes | 20.69 |
| CER | Test | Yes | 3.986 |
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 150.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:-----:|:---------------:|:------:|
| 2.9038 | 4.8 | 500 | 3.0125 | 1.0 |
| 1.3777 | 9.61 | 1000 | 0.8681 | 0.8753 |
| 1.1436 | 14.42 | 1500 | 0.6256 | 0.7961 |
| 1.0997 | 19.23 | 2000 | 0.5244 | 0.6875 |
| 1.0363 | 24.04 | 2500 | 0.4585 | 0.6276 |
| 0.7996 | 28.84 | 3000 | 0.4072 | 0.5295 |
| 0.825 | 33.65 | 3500 | 0.3590 | 0.5222 |
| 0.8018 | 38.46 | 4000 | 0.3678 | 0.4671 |
| 0.7545 | 43.27 | 4500 | 0.3474 | 0.3962 |
| 0.7375 | 48.08 | 5000 | 0.3224 | 0.3869 |
| 0.6198 | 52.88 | 5500 | 0.3233 | 0.3630 |
| 0.6608 | 57.69 | 6000 | 0.3029 | 0.3308 |
| 0.645 | 62.5 | 6500 | 0.3195 | 0.3722 |
| 0.5249 | 67.31 | 7000 | 0.3004 | 0.3202 |
| 0.4875 | 72.11 | 7500 | 0.2826 | 0.2992 |
| 0.5171 | 76.92 | 8000 | 0.2962 | 0.2976 |
| 0.4974 | 81.73 | 8500 | 0.2990 | 0.2933 |
| 0.4387 | 86.54 | 9000 | 0.2834 | 0.2755 |
| 0.4511 | 91.34 | 9500 | 0.2886 | 0.2787 |
| 0.4112 | 96.15 | 10000 | 0.3093 | 0.2976 |
| 0.4064 | 100.96 | 10500 | 0.3123 | 0.2863 |
| 0.4047 | 105.77 | 11000 | 0.2968 | 0.2719 |
| 0.3519 | 110.57 | 11500 | 0.3106 | 0.2832 |
| 0.3719 | 115.38 | 12000 | 0.3030 | 0.2737 |
| 0.3669 | 120.19 | 12500 | 0.2964 | 0.2714 |
| 0.3386 | 125.0 | 13000 | 0.3101 | 0.2714 |
| 0.3137 | 129.8 | 13500 | 0.3063 | 0.2710 |
| 0.3008 | 134.61 | 14000 | 0.3082 | 0.2617 |
| 0.301 | 139.42 | 14500 | 0.3121 | 0.2628 |
| 0.3291 | 144.23 | 15000 | 0.3105 | 0.2612 |
| 0.3133 | 149.04 | 15500 | 0.3114 | 0.2624 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
|
CyberHarem/mem_oshinoko
|
CyberHarem
| 2023-09-10T17:09:32Z | 0 | 0 | null |
[
"art",
"text-to-image",
"dataset:CyberHarem/mem_oshinoko",
"license:mit",
"region:us"
] |
text-to-image
| 2023-09-10T16:49:28Z |
---
license: mit
datasets:
- CyberHarem/mem_oshinoko
pipeline_tag: text-to-image
tags:
- art
---
# Lora of mem_oshinoko
This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11).
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 2940, you need to download `2940/mem_oshinoko.pt` as the embedding and `2940/mem_oshinoko.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The best step we recommend is 2940**, with the score of 0.978. The trigger words are:
1. `mem_oshinoko`
2. `bangs, short_hair, blonde_hair, blunt_bangs, horns, :3, smile, multicolored_hair, blue_eyes`
For the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
These are available steps:
| Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | pattern_6 | pattern_7 | pattern_8 | pattern_9 | pattern_10 | pattern_11 | pattern_12 | pattern_13 | pattern_14 | pattern_15 | pattern_16 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata |
|:---------|:----------|:--------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------|
| 6300 | 0.969 | [Download](6300/mem_oshinoko.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](6300/previews/bondage.png) |  |  |  | [<NSFW, click to see>](6300/previews/nude.png) | [<NSFW, click to see>](6300/previews/nude2.png) |  |  |
| 5880 | 0.974 | [Download](5880/mem_oshinoko.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](5880/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5880/previews/nude.png) | [<NSFW, click to see>](5880/previews/nude2.png) |  |  |
| 5460 | 0.974 | [Download](5460/mem_oshinoko.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](5460/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5460/previews/nude.png) | [<NSFW, click to see>](5460/previews/nude2.png) |  |  |
| 5040 | 0.970 | [Download](5040/mem_oshinoko.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](5040/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5040/previews/nude.png) | [<NSFW, click to see>](5040/previews/nude2.png) |  |  |
| 4620 | 0.965 | [Download](4620/mem_oshinoko.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](4620/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4620/previews/nude.png) | [<NSFW, click to see>](4620/previews/nude2.png) |  |  |
| 4200 | 0.975 | [Download](4200/mem_oshinoko.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](4200/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4200/previews/nude.png) | [<NSFW, click to see>](4200/previews/nude2.png) |  |  |
| 3780 | 0.934 | [Download](3780/mem_oshinoko.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](3780/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3780/previews/nude.png) | [<NSFW, click to see>](3780/previews/nude2.png) |  |  |
| 3360 | 0.969 | [Download](3360/mem_oshinoko.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](3360/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3360/previews/nude.png) | [<NSFW, click to see>](3360/previews/nude2.png) |  |  |
| **2940** | **0.978** | [**Download**](2940/mem_oshinoko.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](2940/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2940/previews/nude.png) | [<NSFW, click to see>](2940/previews/nude2.png) |  |  |
| 2520 | 0.969 | [Download](2520/mem_oshinoko.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](2520/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2520/previews/nude.png) | [<NSFW, click to see>](2520/previews/nude2.png) |  |  |
| 2100 | 0.980 | [Download](2100/mem_oshinoko.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](2100/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2100/previews/nude.png) | [<NSFW, click to see>](2100/previews/nude2.png) |  |  |
| 1680 | 0.965 | [Download](1680/mem_oshinoko.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](1680/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1680/previews/nude.png) | [<NSFW, click to see>](1680/previews/nude2.png) |  |  |
| 1260 | 0.958 | [Download](1260/mem_oshinoko.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](1260/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1260/previews/nude.png) | [<NSFW, click to see>](1260/previews/nude2.png) |  |  |
| 840 | 0.935 | [Download](840/mem_oshinoko.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](840/previews/bondage.png) |  |  |  | [<NSFW, click to see>](840/previews/nude.png) | [<NSFW, click to see>](840/previews/nude2.png) |  |  |
| 420 | 0.902 | [Download](420/mem_oshinoko.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](420/previews/bondage.png) |  |  |  | [<NSFW, click to see>](420/previews/nude.png) | [<NSFW, click to see>](420/previews/nude2.png) |  |  |
|
fedbor/13bllama2_lora16_modello
|
fedbor
| 2023-09-10T17:08:02Z | 3 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-10T17:08:00Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.0.dev0
|
StefanoCaloni/Pyramid
|
StefanoCaloni
| 2023-09-10T17:07:01Z | 5 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2023-09-10T17:05:21Z |
---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: StefanoCaloni/Pyramid
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
NewstaR/OpenStar-1b
|
NewstaR
| 2023-09-10T17:01:06Z | 131 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"en",
"dataset:NewstaR/AverageData",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-09-10T16:51:45Z |
---
license: apache-2.0
datasets:
- NewstaR/AverageData
language:
- en
metrics:
- accuracy
- bertscore
- character
---
|
alemoraesc/alemoraesc-sg161222-realistic-vision-v5-1-novae-autocrop-0001
|
alemoraesc
| 2023-09-10T16:51:17Z | 29 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"text-to-image",
"EMMA VAE",
"realistic vision",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-09-09T17:42:36Z |
---
license: creativeml-openrail-m
tags:
- text-to-image
- EMMA VAE
- realistic vision
---
### alemoraesc/sg161222-realistic-vision-v5-1-novae-autocrop-0001 on Stable Diffusion via Dreambooth
#### model by alemoraesc
This your the Stable Diffusion model fine-tuned the alemoraesc/sg161222-realistic-vision-v5-1-novae-autocrop-0001 concept taught to Stable Diffusion with Dreambooth.
It can be used by modifying the `instance_prompt`: **ukj alex**
You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb).
And you can run your new concept via `diffusers`: [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts)
Model used: stablediffusionapi/realistic-vision
VAE was embeded in the model
----
TRAIN
#@title Model training
!/usr/bin/python3 train_dreambooth.py --pretrained_model_name_or_path="/content/stable_diffusion_weights" --pretrained_vae_name_or_path="/content/stable_diffusion_weights/vae" --instance_data_dir="/content/data/ukj" --instance_prompt="photo of ukj alex person" --output_dir=$OUTPUT_DIR --revision="fp16" --seed=1337 --resolution=512 --train_batch_size=1 --train_text_encoder --mixed_precision="fp16" --use_8bit_adam --gradient_accumulation_steps=1 --learning_rate=1e-6 --lr_scheduler="constant" --lr_warmup_steps=120 --sample_batch_size=4 --max_train_steps=1200 --save_sample_negative_prompt="(deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime:1.4), text, close up, cropped, out of frame, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck" --save_interval=600 --save_sample_prompt="photo of ukj alex"
----
Prompt:
RAW photo, subject, 8k uhd, dslr, soft lighting, high quality, film grain, Fujifilm XT3
ᅠ
Negative Prompt:
(deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime), text, cropped, out of frame, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, UnrealisticDream
#used birme to cut the image
Here are the images used for training this concept:
|
Ridealist/xlm-roberta-base-finetuned-panx-all
|
Ridealist
| 2023-09-10T16:47:36Z | 112 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-09-10T16:38:34Z |
---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-all
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-all
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1745
- F1: 0.8577
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2915 | 1.0 | 835 | 0.1859 | 0.8171 |
| 0.1544 | 2.0 | 1670 | 0.1631 | 0.8509 |
| 0.1014 | 3.0 | 2505 | 0.1745 | 0.8577 |
### Framework versions
- Transformers 4.33.1
- Pytorch 1.12.1+cu116
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Q317/EmoraBert1
|
Q317
| 2023-09-10T16:44:46Z | 62 | 0 |
transformers
|
[
"transformers",
"tf",
"roberta",
"text-classification",
"generated_from_keras_callback",
"base_model:wonrax/phobert-base-vietnamese-sentiment",
"base_model:finetune:wonrax/phobert-base-vietnamese-sentiment",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-09-10T16:18:19Z |
---
license: mit
base_model: wonrax/phobert-base-vietnamese-sentiment
tags:
- generated_from_keras_callback
model-index:
- name: Q317/EmoraBert1
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Q317/EmoraBert1
This model is a fine-tuned version of [wonrax/phobert-base-vietnamese-sentiment](https://huggingface.co/wonrax/phobert-base-vietnamese-sentiment) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.3123
- Validation Loss: 0.8557
- Train Accuracy: 0.7158
- Epoch: 4
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 146205, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.7587 | 0.6693 | 0.7181 | 0 |
| 0.6184 | 0.6566 | 0.7267 | 1 |
| 0.5107 | 0.6663 | 0.7274 | 2 |
| 0.4007 | 0.7829 | 0.7262 | 3 |
| 0.3123 | 0.8557 | 0.7158 | 4 |
### Framework versions
- Transformers 4.33.1
- TensorFlow 2.13.0
- Datasets 2.14.5
- Tokenizers 0.13.3
|
nikolaalx/sample-model
|
nikolaalx
| 2023-09-10T16:44:43Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-10T16:42:49Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
|
Ridealist/xlm-roberta-base-finetuned-panx-en
|
Ridealist
| 2023-09-10T16:38:31Z | 124 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-09-10T16:37:28Z |
---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-en
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
config: PAN-X.en
split: validation
args: PAN-X.en
metrics:
- name: F1
type: f1
value: 0.6973094170403586
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-en
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3911
- F1: 0.6973
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.9995 | 1.0 | 50 | 0.5467 | 0.5618 |
| 0.4997 | 2.0 | 100 | 0.4371 | 0.6535 |
| 0.3801 | 3.0 | 150 | 0.3911 | 0.6973 |
### Framework versions
- Transformers 4.33.1
- Pytorch 1.12.1+cu116
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Ridealist/xlm-roberta-base-finetuned-panx-it
|
Ridealist
| 2023-09-10T16:37:25Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-09-10T16:35:23Z |
---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-it
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
config: PAN-X.it
split: validation
args: PAN-X.it
metrics:
- name: F1
type: f1
value: 0.8241577649958916
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-it
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2504
- F1: 0.8242
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.7373 | 1.0 | 70 | 0.2876 | 0.7604 |
| 0.2725 | 2.0 | 140 | 0.2869 | 0.8115 |
| 0.1718 | 3.0 | 210 | 0.2504 | 0.8242 |
### Framework versions
- Transformers 4.33.1
- Pytorch 1.12.1+cu116
- Datasets 2.4.0
- Tokenizers 0.12.1
|
baebee/OpenStar-1b
|
baebee
| 2023-09-10T16:35:42Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-10T16:35:40Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.6.0.dev0
|
DriveMyScream/Fake_News_Classification_model
|
DriveMyScream
| 2023-09-10T16:31:45Z | 0 | 0 |
keras
|
[
"keras",
"tf-keras",
"region:us"
] | null | 2023-09-10T16:30:23Z |
---
library_name: keras
---
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
| Hyperparameters | Value |
| :-- | :-- |
| name | Adam |
| weight_decay | None |
| clipnorm | None |
| global_clipnorm | None |
| clipvalue | None |
| use_ema | False |
| ema_momentum | 0.99 |
| ema_overwrite_frequency | None |
| jit_compile | False |
| is_legacy_optimizer | False |
| learning_rate | 0.0010000000474974513 |
| beta_1 | 0.9 |
| beta_2 | 0.999 |
| epsilon | 1e-07 |
| amsgrad | False |
| training_precision | float32 |
## Model Plot
<details>
<summary>View Model Plot</summary>
</details>
|
SaniyatMushrat/SusathoASR
|
SaniyatMushrat
| 2023-09-10T16:27:10Z | 3 | 0 |
transformers
|
[
"transformers",
"wav2vec2",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"openslr_SLR53",
"robust-speech-event",
"bn",
"dataset:openslr",
"dataset:SLR53",
"dataset:Harveenchadha/indic-text",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-09-10T16:25:59Z |
---
language:
- bn
license: apache-2.0
tags:
- automatic-speech-recognition
- hf-asr-leaderboard
- openslr_SLR53
- robust-speech-event
datasets:
- openslr
- SLR53
- Harveenchadha/indic-text
metrics:
- wer
- cer
model-index:
- name: Tahsin-Mayeesha/wav2vec2-bn-300m
results:
- task:
type: automatic-speech-recognition
name: Speech Recognition
dataset:
type: openslr
name: Open SLR
args: SLR66
metrics:
- type: wer
value: 0.31104373941386626
name: Test WER
- type: cer
value: 0.07263099973420006
name: Test CER
- type: wer
value: 0.17776164652632478
name: Test WER with lm
- type: cer
value: 0.04394092712884769
name: Test CER with lm
---
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the OPENSLR_SLR53 - bengali dataset.
It achieves the following results on the evaluation set.
Without language model :
- Wer: 0.3110
- Cer : 0.072
With 5 gram language model trained on [indic-text](https://huggingface.co/datasets/Harveenchadha/indic-text/tree/main) dataset :
- Wer: 0.17776
- Cer : 0.04394
Note : 10% of a total 218703 samples have been used for evaluation. Evaluation set has 21871 examples. Training was stopped after 30k steps. Output predictions are available under files section.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 16
- eval_batch_size: 16
- gradient_accumulation_steps: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
Note : Training and evaluation script modified from https://huggingface.co/chmanoj/xls-r-300m-te and https://github.com/huggingface/transformers/tree/master/examples/research_projects/robust-speech-event.
Bengali speech data was not available from common voice or librispeech multilingual datasets, so OpenSLR53 has been used.
Note 2 : Minimum audio duration of 0.1s has been used to filter the training data which excluded may be 10-20 samples.
# Citation
@misc {tahsin_mayeesha_2023,
author = { {Tahsin Mayeesha} },
title = { wav2vec2-bn-300m (Revision e10defc) },
year = 2023,
url = { https://huggingface.co/Tahsin-Mayeesha/wav2vec2-bn-300m },
doi = { 10.57967/hf/0939 },
publisher = { Hugging Face }
}
|
shengqin/bloomz-xss-sqli-3b
|
shengqin
| 2023-09-10T16:24:42Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-10T14:58:02Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0
|
waqasobeidy/sdxldemo0001
|
waqasobeidy
| 2023-09-10T16:22:11Z | 1 | 1 |
diffusers
|
[
"diffusers",
"text-to-image",
"autotrain",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0",
"region:us"
] |
text-to-image
| 2023-09-10T13:57:56Z |
---
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: photo of a sks imran
tags:
- text-to-image
- diffusers
- autotrain
inference: true
---
# DreamBooth trained by AutoTrain
Text encoder was not trained.
|
Ridealist/xlm-roberta-base-finetuned-panx-de
|
Ridealist
| 2023-09-10T16:12:10Z | 112 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-09-10T16:05:16Z |
---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
config: PAN-X.de
split: validation
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8643238940065961
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1363
- F1: 0.8643
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2536 | 1.0 | 525 | 0.1531 | 0.8247 |
| 0.1243 | 2.0 | 1050 | 0.1415 | 0.8546 |
| 0.08 | 3.0 | 1575 | 0.1363 | 0.8643 |
### Framework versions
- Transformers 4.33.1
- Pytorch 1.12.1+cu116
- Datasets 2.4.0
- Tokenizers 0.12.1
|
CyberHarem/arima_kana_oshinoko
|
CyberHarem
| 2023-09-10T16:09:58Z | 0 | 0 | null |
[
"art",
"text-to-image",
"dataset:CyberHarem/arima_kana_oshinoko",
"license:mit",
"region:us"
] |
text-to-image
| 2023-09-10T15:50:26Z |
---
license: mit
datasets:
- CyberHarem/arima_kana_oshinoko
pipeline_tag: text-to-image
tags:
- art
---
# Lora of arima_kana_oshinoko
This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11).
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 6000, you need to download `6000/arima_kana_oshinoko.pt` as the embedding and `6000/arima_kana_oshinoko.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The best step we recommend is 6000**, with the score of 0.952. The trigger words are:
1. `arima_kana_oshinoko`
2. `short_hair, bangs, red_hair, blunt_bangs, bob_cut, red_eyes, hat`
For the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
These are available steps:
| Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | pattern_6 | pattern_7 | pattern_8 | pattern_9 | pattern_10 | pattern_11 | pattern_12 | pattern_13 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata |
|:---------|:----------|:---------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------|
| **6000** | **0.952** | [**Download**](6000/arima_kana_oshinoko.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](6000/previews/bondage.png) |  |  |  | [<NSFW, click to see>](6000/previews/nude.png) | [<NSFW, click to see>](6000/previews/nude2.png) |  |  |
| 5600 | 0.937 | [Download](5600/arima_kana_oshinoko.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](5600/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5600/previews/nude.png) | [<NSFW, click to see>](5600/previews/nude2.png) |  |  |
| 5200 | 0.931 | [Download](5200/arima_kana_oshinoko.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](5200/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5200/previews/nude.png) | [<NSFW, click to see>](5200/previews/nude2.png) |  |  |
| 4800 | 0.941 | [Download](4800/arima_kana_oshinoko.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](4800/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4800/previews/nude.png) | [<NSFW, click to see>](4800/previews/nude2.png) |  |  |
| 4400 | 0.931 | [Download](4400/arima_kana_oshinoko.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](4400/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4400/previews/nude.png) | [<NSFW, click to see>](4400/previews/nude2.png) |  |  |
| 4000 | 0.904 | [Download](4000/arima_kana_oshinoko.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](4000/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4000/previews/nude.png) | [<NSFW, click to see>](4000/previews/nude2.png) |  |  |
| 3600 | 0.924 | [Download](3600/arima_kana_oshinoko.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](3600/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3600/previews/nude.png) | [<NSFW, click to see>](3600/previews/nude2.png) |  |  |
| 3200 | 0.922 | [Download](3200/arima_kana_oshinoko.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](3200/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3200/previews/nude.png) | [<NSFW, click to see>](3200/previews/nude2.png) |  |  |
| 2800 | 0.914 | [Download](2800/arima_kana_oshinoko.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](2800/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2800/previews/nude.png) | [<NSFW, click to see>](2800/previews/nude2.png) |  |  |
| 2400 | 0.894 | [Download](2400/arima_kana_oshinoko.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](2400/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2400/previews/nude.png) | [<NSFW, click to see>](2400/previews/nude2.png) |  |  |
| 2000 | 0.881 | [Download](2000/arima_kana_oshinoko.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](2000/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2000/previews/nude.png) | [<NSFW, click to see>](2000/previews/nude2.png) |  |  |
| 1600 | 0.876 | [Download](1600/arima_kana_oshinoko.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](1600/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1600/previews/nude.png) | [<NSFW, click to see>](1600/previews/nude2.png) |  |  |
| 1200 | 0.750 | [Download](1200/arima_kana_oshinoko.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](1200/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1200/previews/nude.png) | [<NSFW, click to see>](1200/previews/nude2.png) |  |  |
| 800 | 0.838 | [Download](800/arima_kana_oshinoko.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](800/previews/bondage.png) |  |  |  | [<NSFW, click to see>](800/previews/nude.png) | [<NSFW, click to see>](800/previews/nude2.png) |  |  |
| 400 | 0.726 | [Download](400/arima_kana_oshinoko.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](400/previews/bondage.png) |  |  |  | [<NSFW, click to see>](400/previews/nude.png) | [<NSFW, click to see>](400/previews/nude2.png) |  |  |
|
yacht/latte-mc-bert-base-chinese-ws
|
yacht
| 2023-09-10T16:09:39Z | 109 | 1 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"feature-extraction",
"word segmentation",
"token-classification",
"zh",
"dataset:ctb6",
"dataset:as",
"dataset:cityu",
"dataset:msra",
"dataset:pku",
"dataset:sxu",
"dataset:cnc",
"license:cc-by-sa-4.0",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-08-31T09:38:45Z |
---
language: zh
license: cc-by-sa-4.0
tags:
- word segmentation
datasets:
- ctb6
- as
- cityu
- msra
- pku
- sxu
- cnc
pipeline_tag: token-classification
---
# Multi-criteria BERT base Chinese with Lattice for Word Segmentation
This is a variant of the pre-trained model [BERT](https://github.com/google-research/bert) model.
The model was pre-trained on texts in the Chinese language and fine-tuned for word segmentation based on [bert-base-chinese](https://huggingface.co/bert-base-chinese).
This version of the model processes input texts with character-level with word-level incorporated with a lattice structure.
The scripts for the pre-training are available at [tchayintr/latte-ptm-ws](https://github.com/tchayintr/latte-ptm-ws).
The LATTE scripts are available at [tchayintr/latte-ws](https://github.com/tchayintr/latte-ws).
## Model architecture
The model architecture is described in this [paper](https://www.jstage.jst.go.jp/article/jnlp/30/2/30_456/_article/-char/ja).
## Training Data
The model is trained on multiple Chinese word segmented datasets, including ctb6, sighan2005 (as, cityu, msra, pku), sighan2008 (sxu), and cnc.
The datasets can be accessed from [here](https://github.com/hankcs/multi-criteria-cws/tree/master/data).
## Licenses
The pre-trained model is distributed under the terms of the [Creative Commons Attribution-ShareAlike 4.0](https://creativecommons.org/licenses/by-sa/4.0/).
## Acknowledgments
This model was trained with GPU servers provided by [Okumura-Funakoshi NLP Group](https://lr-www.pi.titech.ac.jp).
|
Ashutosh94/my-pet-character
|
Ashutosh94
| 2023-09-10T16:05:38Z | 4 | 1 |
diffusers
|
[
"diffusers",
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-09-10T16:01:50Z |
---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### my-pet-character Dreambooth model trained by Ashutosh94 following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: IIITB-430
Sample pictures of this concept:
|
deepcyber/Enhanced-CIFAR10-CNN
|
deepcyber
| 2023-09-10T15:45:13Z | 0 | 1 |
keras
|
[
"keras",
"image-classification",
"dataset:cifar10",
"license:mit",
"region:us"
] |
image-classification
| 2023-09-09T12:20:07Z |
---
license: mit
datasets:
- cifar10
library_name: keras
pipeline_tag: image-classification
---
### Model Name: `Enhanced-CIFAR10-CNN`
**Description:**
Introducing `Enhanced-CIFAR10-CNN`, a state-of-the-art Convolutional Neural Network (CNN) trained on the CIFAR dataset. Based on extensive research, with an impressive accuracy of 89%, this model sets a new benchmark in image classification tasks. What sets it apart?
- **High Performance**: Achieves an accuracy rate of 86%, surpassing standard benchmarks.
- **Fast Inference**: Optimized for speed, this model ensures quick predictions without compromising on accuracy.
- **Compact Size**: Its small footprint makes it ideal for edge deployments and integration into existing systems.
- **Transfer Learning Ready**: The model's architecture and pre-trained weights make it an excellent candidate for fine-tuning and further development in various applications.
**Usage Examples:**
```python
from keras.models import load_model
# Load the model
model = load_model('path/to/enhancedCIFAR-10-CNN.h5')
# Perform inference
result = model.predict(input_data)
```
**Dependencies:**
- Keras >= 2.4.0
- TensorFlow >= 2.5.0
**Citation:**
Ogundokun, Roseline Oluwaseun, et al. "Improved CNN based on batch normalization and adam optimizer." International Conference on Computational Science and Its Applications. Cham: Springer International Publishing, 2022.
If you find this model useful, please cite our work.
|
qnfino091/space-tour
|
qnfino091
| 2023-09-10T15:29:35Z | 14 | 2 |
diffusers
|
[
"diffusers",
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-09-10T14:14:58Z |
---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### space-tour- Dreambooth model trained by qnfino091 following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: IIITB-146
Sample pictures of this concept:
|
gyesibiney/Sentiment-review-analysis-roberta-3
|
gyesibiney
| 2023-09-10T15:13:06Z | 112 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/roberta-base",
"base_model:finetune:FacebookAI/roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-09-08T16:58:40Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: Roberta-capstone_2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Roberta-capstone_2
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3485
- Accuracy: 0.9400
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2568 | 1.0 | 623 | 0.1971 | 0.9265 |
| 0.1581 | 2.0 | 1246 | 0.2102 | 0.9339 |
| 0.109 | 3.0 | 1869 | 0.3126 | 0.9356 |
| 0.0687 | 4.0 | 2492 | 0.3040 | 0.9382 |
| 0.0199 | 5.0 | 3115 | 0.3485 | 0.9400 |
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
Dhruv21/my-white-horse-xzc
|
Dhruv21
| 2023-09-10T15:10:39Z | 1 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-09-10T15:06:42Z |
---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### My-white-horse-xzc Dreambooth model trained by Dhruv21 following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: IIITB-92
Sample pictures of this concept:
|
BanUrsus/tqc-PandaPickAndPlace-v3
|
BanUrsus
| 2023-09-10T15:09:11Z | 1 | 1 |
stable-baselines3
|
[
"stable-baselines3",
"PandaPickAndPlace-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-10T15:04:46Z |
---
library_name: stable-baselines3
tags:
- PandaPickAndPlace-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: TQC
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaPickAndPlace-v3
type: PandaPickAndPlace-v3
metrics:
- type: mean_reward
value: -6.90 +/- 1.58
name: mean_reward
verified: false
---
# **TQC** Agent playing **PandaPickAndPlace-v3**
This is a trained model of a **TQC** agent playing **PandaPickAndPlace-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Vadu/ppo-HuggyMyBeloved
|
Vadu
| 2023-09-10T14:51:50Z | 8 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-09-10T14:51:42Z |
---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: Vadu/ppo-HuggyMyBeloved
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
vasaicrow/bert-finetuned-ner
|
vasaicrow
| 2023-09-10T14:41:10Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-09-10T14:19:01Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: validation
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9375621066578337
- name: Recall
type: recall
value: 0.9527095254123191
- name: F1
type: f1
value: 0.9450751252086811
- name: Accuracy
type: accuracy
value: 0.9865632542532525
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0613
- Precision: 0.9376
- Recall: 0.9527
- F1: 0.9451
- Accuracy: 0.9866
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0886 | 1.0 | 1756 | 0.0764 | 0.9176 | 0.9315 | 0.9245 | 0.9801 |
| 0.0342 | 2.0 | 3512 | 0.0618 | 0.9292 | 0.9482 | 0.9386 | 0.9859 |
| 0.0168 | 3.0 | 5268 | 0.0613 | 0.9376 | 0.9527 | 0.9451 | 0.9866 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
|
SlytheeTove/BluePossum
|
SlytheeTove
| 2023-09-10T14:13:02Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-09-10T14:10:57Z |
---
license: apache-2.0
---
Initial training model based on the Llama2 7b foundation model.
|
codelion/whisper-age-estimator
|
codelion
| 2023-09-10T13:46:00Z | 93 | 3 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"generated_from_trainer",
"hi",
"base_model:openai/whisper-base",
"base_model:finetune:openai/whisper-base",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-07-17T08:30:57Z |
---
language:
- hi
license: apache-2.0
base_model: openai/whisper-base
tags:
- hf-asr-leaderboard
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: Whisper Base Hi - Age Estimation
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Base Hi - Age Estimation
This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0118
- Accuracy: 0.6259
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- training_steps: 300
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0 | 0.47 | 100 | 0.9908 | 0.6774 |
| 0.0 | 0.93 | 200 | 0.9996 | 0.6677 |
| 0.0 | 1.4 | 300 | 1.0118 | 0.6259 |
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.6.1
- Tokenizers 0.13.3
|
strumber/Llama2letsmod
|
strumber
| 2023-09-10T13:35:49Z | 0 | 0 |
peft
|
[
"peft",
"tensorboard",
"region:us"
] | null | 2023-09-10T13:34:51Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.5.0
|
CyberHarem/hoshino_ai_oshinoko
|
CyberHarem
| 2023-09-10T13:17:45Z | 0 | 1 | null |
[
"art",
"text-to-image",
"dataset:CyberHarem/hoshino_ai_oshinoko",
"license:mit",
"region:us"
] |
text-to-image
| 2023-09-10T12:59:09Z |
---
license: mit
datasets:
- CyberHarem/hoshino_ai_oshinoko
pipeline_tag: text-to-image
tags:
- art
---
# Lora of hoshino_ai_oshinoko
This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11).
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 3960, you need to download `3960/hoshino_ai_oshinoko.pt` as the embedding and `3960/hoshino_ai_oshinoko.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The best step we recommend is 3960**, with the score of 0.966. The trigger words are:
1. `hoshino_ai_oshinoko`
2. `long_hair, purple_eyes, purple_hair, bangs, smile, symbol-shaped_pupils, multicolored_hair, star-shaped_pupils`
For the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
These are available steps:
| Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | pattern_6 | pattern_7 | pattern_8 | pattern_9 | pattern_10 | pattern_11 | pattern_12 | pattern_13 | pattern_14 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata |
|:---------|:----------|:---------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------|
| 6600 | 0.966 | [Download](6600/hoshino_ai_oshinoko.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](6600/previews/bondage.png) |  |  |  | [<NSFW, click to see>](6600/previews/nude.png) | [<NSFW, click to see>](6600/previews/nude2.png) |  |  |
| 6160 | 0.956 | [Download](6160/hoshino_ai_oshinoko.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](6160/previews/bondage.png) |  |  |  | [<NSFW, click to see>](6160/previews/nude.png) | [<NSFW, click to see>](6160/previews/nude2.png) |  |  |
| 5720 | 0.952 | [Download](5720/hoshino_ai_oshinoko.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](5720/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5720/previews/nude.png) | [<NSFW, click to see>](5720/previews/nude2.png) |  |  |
| 5280 | 0.960 | [Download](5280/hoshino_ai_oshinoko.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](5280/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5280/previews/nude.png) | [<NSFW, click to see>](5280/previews/nude2.png) |  |  |
| 4840 | 0.955 | [Download](4840/hoshino_ai_oshinoko.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](4840/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4840/previews/nude.png) | [<NSFW, click to see>](4840/previews/nude2.png) |  |  |
| 4400 | 0.960 | [Download](4400/hoshino_ai_oshinoko.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](4400/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4400/previews/nude.png) | [<NSFW, click to see>](4400/previews/nude2.png) |  |  |
| **3960** | **0.966** | [**Download**](3960/hoshino_ai_oshinoko.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](3960/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3960/previews/nude.png) | [<NSFW, click to see>](3960/previews/nude2.png) |  |  |
| 3520 | 0.949 | [Download](3520/hoshino_ai_oshinoko.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](3520/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3520/previews/nude.png) | [<NSFW, click to see>](3520/previews/nude2.png) |  |  |
| 3080 | 0.956 | [Download](3080/hoshino_ai_oshinoko.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](3080/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3080/previews/nude.png) | [<NSFW, click to see>](3080/previews/nude2.png) |  |  |
| 2640 | 0.955 | [Download](2640/hoshino_ai_oshinoko.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](2640/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2640/previews/nude.png) | [<NSFW, click to see>](2640/previews/nude2.png) |  |  |
| 2200 | 0.939 | [Download](2200/hoshino_ai_oshinoko.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](2200/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2200/previews/nude.png) | [<NSFW, click to see>](2200/previews/nude2.png) |  |  |
| 1760 | 0.939 | [Download](1760/hoshino_ai_oshinoko.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](1760/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1760/previews/nude.png) | [<NSFW, click to see>](1760/previews/nude2.png) |  |  |
| 1320 | 0.931 | [Download](1320/hoshino_ai_oshinoko.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](1320/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1320/previews/nude.png) | [<NSFW, click to see>](1320/previews/nude2.png) |  |  |
| 880 | 0.933 | [Download](880/hoshino_ai_oshinoko.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](880/previews/bondage.png) |  |  |  | [<NSFW, click to see>](880/previews/nude.png) | [<NSFW, click to see>](880/previews/nude2.png) |  |  |
| 440 | 0.900 | [Download](440/hoshino_ai_oshinoko.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](440/previews/bondage.png) |  |  |  | [<NSFW, click to see>](440/previews/nude.png) | [<NSFW, click to see>](440/previews/nude2.png) |  |  |
|
aldigobbler/models
|
aldigobbler
| 2023-09-10T13:14:56Z | 0 | 0 |
fairseq
|
[
"fairseq",
"music",
"audio-to-audio",
"en",
"license:apache-2.0",
"region:us"
] |
audio-to-audio
| 2023-08-24T10:43:04Z |
---
license: apache-2.0
language:
- en
library_name: fairseq
pipeline_tag: audio-to-audio
tags:
- music
---
These are my models for many projects.
Most are RVCv2/v1
|
dsmsb/only_esg-class_bert_1009_v1
|
dsmsb
| 2023-09-10T13:08:42Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-multilingual-cased",
"base_model:finetune:google-bert/bert-base-multilingual-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-09-10T08:33:53Z |
---
license: apache-2.0
base_model: bert-base-multilingual-cased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: only_esg-class_bert_1009_v1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# only_esg-class_bert_1009_v1
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1359
- Accuracy: 0.9649
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 73 | 0.1758 | 0.9563 |
| No log | 2.0 | 146 | 0.1359 | 0.9649 |
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
joe-xhedi/rl_course_vizdoom_health_gathering_supreme
|
joe-xhedi
| 2023-09-10T13:06:37Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-10T12:20:50Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 8.45 +/- 3.21
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r joe-xhedi/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
zayuki/computer_generated_fake_review_detection
|
zayuki
| 2023-09-10T12:59:36Z | 245 | 1 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-09-06T04:13:13Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: zayuki/computer_generated_fake_review_detection
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# zayuki/computer_generated_fake_review_detection
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0116
- Validation Loss: 0.0731
- Train Accuracy: 0.9780
- Train F1: 0.9781
- Epoch: 2
## Model description
This model was empowered by fine-tuned version of Distilbert and trained on [Amazon Review Dataset](https://osf.io/tyue9/), comprising of computer-generated fake reviews and genuine Amazon reviews. The fake reviews were generated by GPT-2, an AI text algoritm.
This model was trained to detect computer-generated fake reviews which generated by AI text algorithm.
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 7580, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Train F1 | Epoch |
|:----------:|:---------------:|:--------------:|:--------:|:-----:|
| 0.1207 | 0.0677 | 0.9723 | 0.9726 | 0 |
| 0.0343 | 0.0736 | 0.9753 | 0.9756 | 1 |
| 0.0116 | 0.0731 | 0.9780 | 0.9781 | 2 |
### Framework versions
- Transformers 4.33.1
- TensorFlow 2.12.0
- Datasets 2.14.5
- Tokenizers 0.13.3
|
joe-xhedi/unit8-LunarLander-v2
|
joe-xhedi
| 2023-09-10T12:51:34Z | 0 | 0 | null |
[
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-10T12:51:29Z |
---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -188.88 +/- 122.54
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 50000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'joe-xhedi/unit8-LunarLander-v2'
'batch_size': 512
'minibatch_size': 128}
```
|
ps259/lora-1b1
|
ps259
| 2023-09-10T12:44:35Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-10T12:44:33Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
|
IsaacSarps/sentiment_analysis
|
IsaacSarps
| 2023-09-10T12:27:19Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/roberta-base",
"base_model:finetune:FacebookAI/roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-09-10T10:08:09Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: sentiment_analysis
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# sentiment_analysis
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8114
- F1 Score: 0.7322
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.7577 | 1.0 | 1000 | 0.7996 | 0.6603 |
| 0.7168 | 2.0 | 2000 | 0.7362 | 0.6627 |
| 0.7201 | 3.0 | 3000 | 0.7231 | 0.6675 |
| 0.6752 | 4.0 | 4000 | 0.7051 | 0.6970 |
| 0.6374 | 5.0 | 5000 | 0.7167 | 0.7007 |
| 0.6288 | 6.0 | 6000 | 0.7278 | 0.7193 |
| 0.5579 | 7.0 | 7000 | 0.8242 | 0.7190 |
| 0.5485 | 8.0 | 8000 | 0.7587 | 0.7291 |
| 0.5309 | 9.0 | 9000 | 0.7876 | 0.7269 |
| 0.4767 | 10.0 | 10000 | 0.8114 | 0.7322 |
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
goat923/my_awesome_qa_model
|
goat923
| 2023-09-10T12:22:50Z | 68 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"question-answering",
"generated_from_keras_callback",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-09-02T11:16:18Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: goat923/my_awesome_qa_model
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# goat923/my_awesome_qa_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.4850
- Validation Loss: 1.8321
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 500, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 3.3901 | 2.1715 | 0 |
| 1.7137 | 1.8321 | 1 |
| 1.4850 | 1.8321 | 2 |
### Framework versions
- Transformers 4.34.0.dev0
- TensorFlow 2.13.0
- Datasets 2.14.5
- Tokenizers 0.13.3
|
Ai-user1028/wildlife-tiger
|
Ai-user1028
| 2023-09-10T12:20:53Z | 0 | 0 | null |
[
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-09-10T12:19:42Z |
---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### Wildlife-Tiger Dreambooth model trained by Ai-user1028 following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: TIIPS-65
Sample pictures of this concept:
|
MekeyPan/mt5-small-finetuned-amazon-en-zh
|
MekeyPan
| 2023-09-10T11:58:35Z | 16 | 1 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"base_model:google/mt5-small",
"base_model:finetune:google/mt5-small",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
summarization
| 2023-09-10T10:56:40Z |
---
license: apache-2.0
base_model: google/mt5-small
tags:
- summarization
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-small-finetuned-amazon-en-zh
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-small-finetuned-amazon-en-zh
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1950
- Rouge1: 15.5597
- Rouge2: 6.7429
- Rougel: 15.1794
- Rougelsum: 15.063
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5.6e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|
| 8.0083 | 1.0 | 838 | 3.5147 | 13.2577 | 6.0411 | 12.9176 | 12.8293 |
| 4.0156 | 2.0 | 1676 | 3.3382 | 14.2493 | 6.3606 | 13.9407 | 13.7391 |
| 3.6492 | 3.0 | 2514 | 3.2576 | 15.915 | 7.4853 | 15.8512 | 15.72 |
| 3.473 | 4.0 | 3352 | 3.2266 | 16.3162 | 6.6844 | 15.9962 | 15.8693 |
| 3.3509 | 5.0 | 4190 | 3.2010 | 15.2992 | 6.2211 | 14.9191 | 14.8807 |
| 3.2828 | 6.0 | 5028 | 3.2008 | 15.379 | 6.38 | 15.1408 | 15.0073 |
| 3.2304 | 7.0 | 5866 | 3.2003 | 15.8089 | 6.7429 | 15.4859 | 15.3334 |
| 3.191 | 8.0 | 6704 | 3.1950 | 15.5597 | 6.7429 | 15.1794 | 15.063 |
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
AlexZhukov/llm-bloom-ti-lora
|
AlexZhukov
| 2023-09-10T11:28:35Z | 0 | 0 |
peft
|
[
"peft",
"endpoints_compatible",
"region:us"
] | null | 2023-09-10T07:57:04Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.5.0
|
matam/ppo-Huggy
|
matam
| 2023-09-10T11:13:25Z | 14 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-09-10T11:13:21Z |
---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: matam/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
makinaAI/makina_lora
|
makinaAI
| 2023-09-10T11:12:03Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-09-10T10:47:09Z |
---
license: creativeml-openrail-m
---
|
ru4rg/msr
|
ru4rg
| 2023-09-10T11:07:02Z | 190 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-09-10T11:06:56Z |
---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: msr
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.6696428656578064
---
# msr
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### cast iron bathtub
#### heating radiator
#### steel bathtub
#### steel cable
#### steel pipe
|
buddhilive/bert-base-zero
|
buddhilive
| 2023-09-10T10:49:10Z | 86 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"fill-mask",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-09-10T10:28:16Z |
---
tags:
- generated_from_keras_callback
model-index:
- name: bert-base-zero
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# bert-base-zero
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 1e-04, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.33.1
- TensorFlow 2.13.0
- Tokenizers 0.13.3
|
NewCosmos/distilhubert-finetuned-gtzan
|
NewCosmos
| 2023-09-10T10:32:09Z | 167 | 0 |
transformers
|
[
"transformers",
"pytorch",
"hubert",
"audio-classification",
"generated_from_trainer",
"dataset:marsyas/gtzan",
"base_model:ntu-spml/distilhubert",
"base_model:finetune:ntu-spml/distilhubert",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2023-08-17T07:05:02Z |
---
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-gtzan
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: GTZAN
type: marsyas/gtzan
config: all
split: train
args: all
metrics:
- name: Accuracy
type: accuracy
value: 0.1
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilhubert-finetuned-gtzan
This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Accuracy: 0.1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0 | 1.0 | 225 | nan | 0.1 |
### Framework versions
- Transformers 4.34.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Venkatesh4342/xlm-roberta-helpdesk-sentiment
|
Venkatesh4342
| 2023-09-10T10:04:31Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-09-10T07:40:46Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: xlm-roberta-helpdesk-sentiment
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-helpdesk-sentiment
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1923
- Accuracy: 0.9556
- F1: 0.9549
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 0.88 | 100 | 0.4935 | 0.7889 | 0.7840 |
| No log | 1.77 | 200 | 0.2955 | 0.8889 | 0.8867 |
| No log | 2.65 | 300 | 0.1830 | 0.9111 | 0.9093 |
| No log | 3.54 | 400 | 0.1461 | 0.9444 | 0.9431 |
| 0.5007 | 4.42 | 500 | 0.1554 | 0.9556 | 0.9549 |
| 0.5007 | 5.31 | 600 | 0.1923 | 0.9556 | 0.9549 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
robertquest/adetailer
|
robertquest
| 2023-09-10T10:04:15Z | 86 | 1 |
ultralytics
|
[
"ultralytics",
"pytorch",
"dataset:wider_face",
"dataset:skytnt/anime-segmentation",
"license:agpl-3.0",
"region:us"
] | null | 2023-09-10T10:01:31Z |
---
license: agpl-3.0
library_name: ultralytics
datasets:
- wider_face
- skytnt/anime-segmentation
tags:
- pytorch
---
# YOLOv8 Detection Model
## Datasets
### Face
- [Anime Face CreateML](https://universe.roboflow.com/my-workspace-mph8o/anime-face-createml)
- [xml2txt](https://universe.roboflow.com/0oooooo0/xml2txt-njqx1)
- [AN](https://universe.roboflow.com/sed-b8vkf/an-lfg5i)
- [wider face](http://shuoyang1213.me/WIDERFACE/index.html)
### Hand
- [AnHDet](https://universe.roboflow.com/1-yshhi/anhdet)
- [hand-detection-fuao9](https://universe.roboflow.com/catwithawand/hand-detection-fuao9)
### Person
- [coco2017](https://cocodataset.org/#home) (only person)
- [AniSeg](https://github.com/jerryli27/AniSeg)
- [skytnt/anime-segmentation](https://huggingface.co/datasets/skytnt/anime-segmentation)
### deepfashion2
- [deepfashion2](https://github.com/switchablenorms/DeepFashion2)
| id | label |
| --- | --------------------- |
| 0 | short_sleeved_shirt |
| 1 | long_sleeved_shirt |
| 2 | short_sleeved_outwear |
| 3 | long_sleeved_outwear |
| 4 | vest |
| 5 | sling |
| 6 | shorts |
| 7 | trousers |
| 8 | skirt |
| 9 | short_sleeved_dress |
| 10 | long_sleeved_dress |
| 11 | vest_dress |
| 12 | sling_dress |
## Info
| Model | Target | mAP 50 | mAP 50-95 |
| --------------------------- | --------------------- | ----------------------------- | ----------------------------- |
| face_yolov8n.pt | 2D / realistic face | 0.660 | 0.366 |
| face_yolov8n_v2.pt | 2D / realistic face | 0.669 | 0.372 |
| face_yolov8s.pt | 2D / realistic face | 0.713 | 0.404 |
| face_yolov8m.pt | 2D / realistic face | 0.737 | 0.424 |
| hand_yolov8n.pt | 2D / realistic hand | 0.767 | 0.505 |
| hand_yolov8s.pt | 2D / realistic hand | 0.794 | 0.527 |
| person_yolov8n-seg.pt | 2D / realistic person | 0.782 (bbox)<br/>0.761 (mask) | 0.555 (bbox)<br/>0.460 (mask) |
| person_yolov8s-seg.pt | 2D / realistic person | 0.824 (bbox)<br/>0.809 (mask) | 0.605 (bbox)<br/>0.508 (mask) |
| person_yolov8m-seg.pt | 2D / realistic person | 0.849 (bbox)<br/>0.831 (mask) | 0.636 (bbox)<br/>0.533 (mask) |
| deepfashion2_yolov8s-seg.pt | realistic clothes | 0.849 (bbox)<br/>0.840 (mask) | 0.763 (bbox)<br/>0.675 (mask) |
## Usage
```python
from huggingface_hub import hf_hub_download
from ultralytics import YOLO
path = hf_hub_download("Bingsu/adetailer", "face_yolov8n.pt")
model = YOLO(path)
```
```python
import cv2
from PIL import Image
img = "https://farm5.staticflickr.com/4139/4887614566_6b57ec4422_z.jpg"
output = model(img)
pred = output[0].plot()
pred = cv2.cvtColor(pred, cv2.COLOR_BGR2RGB)
pred = Image.fromarray(pred)
pred
```
|
SouthMemphis/t5-small_for_summarization
|
SouthMemphis
| 2023-09-10T09:50:23Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"t5",
"text2text-generation",
"generated_from_keras_callback",
"base_model:google-t5/t5-small",
"base_model:finetune:google-t5/t5-small",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-09-10T06:56:59Z |
---
license: apache-2.0
base_model: t5-small
tags:
- generated_from_keras_callback
model-index:
- name: SouthMemphis/t5-small_for_summarization
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# SouthMemphis/t5-small_for_summarization
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 3.0656
- Validation Loss: 2.6739
- Train Rouge1: 23.7763
- Train Rouge2: 5.3102
- Train Rougel: 18.5812
- Train Rougelsum: 18.5773
- Train Gen Len: 18.667
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Rouge1 | Train Rouge2 | Train Rougel | Train Rougelsum | Train Gen Len | Epoch |
|:----------:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-------------:|:-----:|
| 3.0656 | 2.6739 | 23.7763 | 5.3102 | 18.5812 | 18.5773 | 18.667 | 0 |
### Framework versions
- Transformers 4.33.1
- TensorFlow 2.15.0-dev20230905
- Datasets 2.14.4
- Tokenizers 0.13.3
|
swechatelangana/whisper-small-te-146h
|
swechatelangana
| 2023-09-10T09:40:54Z | 265 | 2 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"generated_from_trainer",
"te",
"dataset:INDIC_SUPERB_MUCS_OPENSLR",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-12-19T19:20:57Z |
---
language:
- te
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- INDIC_SUPERB_MUCS_OPENSLR
metrics:
- wer
model-index:
- name: Swecha Gonthuka - Limited Release
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Crowed sourced dataset
type: INDIC SUPERB, MUCS, OPENSLR
config: None
split: None
args: 'config: te, split: test'
metrics:
- name: Wer
type: wer
value: 28.59758159493464
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Swecha Gonthuka - Limited Release
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Crowed sourced dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0768
- Wer: 28.5976
# Collaborators
Trained by Naga Budigam
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 3000
- training_steps: 10000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|
| 0.0729 | 1.08 | 5000 | 0.0934 | 33.3306 |
| 0.0519 | 2.16 | 10000 | 0.0768 | 28.5976 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0
- Datasets 2.7.1
- Tokenizers 0.13.2
|
starfin/W.D.Gaster
|
starfin
| 2023-09-10T09:39:29Z | 0 | 0 | null |
[
"music",
"en",
"ja",
"license:openrail",
"region:us"
] | null | 2023-08-05T10:11:01Z |
---
license: openrail
language:
- en
- ja
tags:
- music
---
Rvc model of Gaster made from 12 minutes of dataset on 500 epochs. Two other models: 50 epoch version and one with other shorter dataset
|
rurulemon/lora-trained-xl-colab
|
rurulemon
| 2023-09-10T09:21:45Z | 2 | 1 |
diffusers
|
[
"diffusers",
"tensorboard",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] |
text-to-image
| 2023-09-09T14:07:47Z |
---
license: openrail++
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: a photo of celebrity
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - rurulemon/lora-trained-xl-colab
These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained on a photo of celebrity using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
|
osieosie/llama-samsum-4bit-13b-bnb-seed43
|
osieosie
| 2023-09-10T09:19:50Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-10T07:49:27Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.5.0.dev0
|
csukuangfj/sherpa-onnx-streaming-zipformer-en-20M-2023-02-17
|
csukuangfj
| 2023-09-10T09:07:28Z | 0 | 0 | null |
[
"onnx",
"license:apache-2.0",
"region:us"
] | null | 2023-09-10T08:47:59Z |
---
license: apache-2.0
---
# Introduction
This model is exported from https://huggingface.co/desh2608/icefall-asr-librispeech-pruned-transducer-stateless7-streaming-small.
Please see https://github.com/k2-fsa/icefall/pull/903.
It can be run with [sherpa-onnx](https://github.com/k2-fsa/sherpa-onnx).
|
andrew45/distilbert-base-uncased-finetuned-emotion
|
andrew45
| 2023-09-10T08:47:29Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-09-10T08:08:48Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.921
- name: F1
type: f1
value: 0.9212643452162217
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2328
- Accuracy: 0.921
- F1: 0.9213
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8876 | 1.0 | 250 | 0.3498 | 0.9015 | 0.9004 |
| 0.2726 | 2.0 | 500 | 0.2328 | 0.921 | 0.9213 |
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
ugshanyu/llama2-qlora-finetunined-french
|
ugshanyu
| 2023-09-10T08:46:39Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-10T08:46:34Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.6.0.dev0
|
Mira-LeafTown/GPT-2-Chinese-AnimeThesaurus
|
Mira-LeafTown
| 2023-09-10T08:23:06Z | 179 | 4 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"zh",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-09-01T16:52:45Z |
---
license: mit
language:
- zh
pipeline_tag: text-generation
widget:
- text: "[CLS]笨蛋"
---
# GPT-2-Chinese-AnimeThesaurus
GPT-2文爱模型
数据集来自https://github.com/Kyomotoi/AnimeThesaurus
训练用的项目https://github.com/yangjianxin1/GPT2-chitchat
|
Pablo94/roberta-base-bne-finetuned-detests
|
Pablo94
| 2023-09-10T08:11:40Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:BSC-LT/roberta-base-bne",
"base_model:finetune:BSC-LT/roberta-base-bne",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-05-12T14:18:07Z |
---
license: apache-2.0
base_model: BSC-TeMU/roberta-base-bne
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
model-index:
- name: roberta-base-bne-finetuned-detests
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-bne-finetuned-detests
This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1686
- Accuracy: 0.8494
- F1-score: 0.7869
- Precision: 0.7855
- Recall: 0.7883
- Auc: 0.7883
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1-score | Precision | Recall | Auc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------:|:------:|:------:|
| 0.0238 | 1.0 | 174 | 0.6262 | 0.8543 | 0.7656 | 0.8161 | 0.7382 | 0.7382 |
| 0.0269 | 2.0 | 348 | 1.1233 | 0.8298 | 0.6964 | 0.7997 | 0.6665 | 0.6665 |
| 0.0003 | 3.0 | 522 | 0.9814 | 0.8429 | 0.7600 | 0.7839 | 0.7435 | 0.7435 |
| 0.0001 | 4.0 | 696 | 1.1054 | 0.8445 | 0.7794 | 0.7787 | 0.7801 | 0.7801 |
| 0.0001 | 5.0 | 870 | 1.1088 | 0.8511 | 0.7948 | 0.7865 | 0.8046 | 0.8046 |
| 0.0001 | 6.0 | 1044 | 1.1265 | 0.8511 | 0.7908 | 0.7873 | 0.7945 | 0.7945 |
| 0.0001 | 7.0 | 1218 | 1.1441 | 0.8494 | 0.7879 | 0.7852 | 0.7909 | 0.7909 |
| 0.0 | 8.0 | 1392 | 1.1574 | 0.8494 | 0.7869 | 0.7855 | 0.7883 | 0.7883 |
| 0.0 | 9.0 | 1566 | 1.1657 | 0.8494 | 0.7869 | 0.7855 | 0.7883 | 0.7883 |
| 0.0 | 10.0 | 1740 | 1.1686 | 0.8494 | 0.7869 | 0.7855 | 0.7883 | 0.7883 |
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
Chang-Su/llama-2-13b-chat-ko
|
Chang-Su
| 2023-09-10T08:02:21Z | 64 | 5 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-08-09T14:42:53Z |
---
license: cc-by-nc-sa-4.0
---
|
Adbhut/whisper-small-dv
|
Adbhut
| 2023-09-10T07:38:50Z | 75 | 0 |
transformers
|
[
"transformers",
"pytorch",
"whisper",
"automatic-speech-recognition",
"dv",
"dataset:mozilla-foundation/common_voice_13_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-09-09T14:24:47Z |
---
license: apache-2.0
datasets:
- mozilla-foundation/common_voice_13_0
language:
- dv
metrics:
- wer
pipeline_tag: automatic-speech-recognition
---
This is whisper-small finetuned for 500 steps on Common Voice 13, lang=Divehi. Achieves normalized wer=12.66 on test set.
|
csukuangfj/sherpa-onnx-streaming-zipformer-zh-14M-2023-02-23
|
csukuangfj
| 2023-09-10T07:33:07Z | 0 | 1 | null |
[
"onnx",
"license:apache-2.0",
"region:us"
] | null | 2023-09-10T07:29:01Z |
---
license: apache-2.0
---
# Introduction
Models in this repo are converted from
https://huggingface.co/csukuangfj/sherpa-onnx-streaming-zipformer-zh-14M-2023-02-23
using [./export-onnx-zh-14M.sh](./export-onnx-zh-14M.sh).
|
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