|
--- |
|
tags: |
|
- sentence-transformers |
|
- sentence-similarity |
|
- feature-extraction |
|
- generated_from_trainer |
|
- dataset_size:69500 |
|
- loss:Infonce |
|
base_model: Snowflake/snowflake-arctic-embed-l-v2.0 |
|
widget: |
|
- source_sentence: What aspect of human relationship to nature is omitted from the |
|
text |
|
sentences: |
|
- 'There are a few good ones, though. Here are the best WWE apps and WWE games for |
|
Android! The first five are the best games... |
|
|
|
Go Android Apps (blog) |
|
|
|
The Best Themes for Android Free Download: Hi friend we are again back with our |
|
new top ten best free themes for android list. This article is especially dedicated |
|
for those persons who want to make their smartphone... |
|
|
|
Paragon Software has created an app for Android that allows your device to natively |
|
read partitions in file systems that Android normally can''t handle, such as Microsoft''s |
|
NTFS, allowing immediate and easy use of... While the Sentio Desktop app can be |
|
used on its own, it was primarily meant to complement Sentio''s Superbook, a crowdfunded |
|
laptop shell for Android smartphones and tablets that''s just entering production |
|
after... |
|
|
|
... phone then GBWhatsapp is the app for you. GBWhatsapp is basically similar |
|
to Whatsapp+ in terms of features. The newest available version right now is GBWhatsapp |
|
6.40 APK for Android devices.' |
|
- A true entertainer. date city state venue 11/23/2012 West Palm Beach FL Kravis |
|
Center 11/24/2012 Sarasota FL Van Wezel Performing Arts Hall 11/25/2012 Clearwater |
|
FL Capitol Theatre 11/29/2012 Durham NC Durham Performing Arts Center 12/1/2012 |
|
Atlantic City NJ Trump Taj Mahal 12/2/2012 Staten Island NY St. George Theatre |
|
12/4/2012 Bethlehem PA Musikfest Cafe 12/5/2012 Verona NY Turning Stone Casino |
|
12/6/2012 Stamford CT Palace Theatre Stamford 12/8/2012 Shippensburg PA Luhrs |
|
Center 12/9/2012 Boston MA Wilbur Theatre 12/11/2012 Greensburg PA The Palace |
|
Theatre 12/12/2012 Easton MD Avalon Theatre 12/15/2012 Saint Charles IL Arcada |
|
Theater 12/16/2012 Milwaukee WI Potawatomi Bingo Casino 12/18/2012 Beaver Creek |
|
CO Vilar Performing Arts Center 12/20/2012 Chandler AZ Ovations Live! |
|
- The reader will gain a better understanding of the direction nature and culture |
|
is heading today by learning how connections were made in the past. It omits that |
|
which Raymond Williams called "a working landscape" -- the most intimate human |
|
relationship to nature which is people who live and work on it. |
|
- source_sentence: Why is it recommended to contact a wedding agency or consultant |
|
before making a decision |
|
sentences: |
|
- Perhaps owing to this humiliation I resigned as Chief Winery Warlord, and took |
|
a position elsewhere. Following my resignation, we rebooked our date with axe |
|
throwing destiny, and converted the night from a team building exercise to a majestic |
|
send off in honour of my 10ish glorious years at Coffin Ridge. We arrived in our |
|
most impeccable vestments. |
|
- Therefore, those private companies increased their own rate of cash burn since |
|
the financial markets were willing to fund money-losing enterprises without hesitation. |
|
Out of the 100 largest North American-based technology companies, 16 have lost |
|
money over the past year. |
|
- Yet , it is best to contact a wedding agency or consultant before you make your |
|
concluding decision. This will make certain you are dealing with a respectable |
|
company. |
|
- source_sentence: What is the Electronic Music Education and Preservation Project |
|
(EMEAPP) and what are its functions |
|
sentences: |
|
- The Electronic Music Education and Preservation Project (EMEAPP) is the steward |
|
of a privately held world-class curated collection of rare vintage electronic |
|
instruments and stage-used gear. This includes effects units, amps, organs, synthesizers, |
|
electro-mechanical instruments, guitars, prototypes, vintage audio/video media |
|
and analog studio gear. In addition, EMEAPP itself is cultivating its own humble |
|
collection. It is our charge to cultivate and reap excellent knowledge from these |
|
unique resources and return it to our members and the world. We do this as a learning |
|
center, through research projects, creative endeavors, media programming and tours, |
|
enlightening many people along the way. There is so much to be harvested from |
|
history; EMEAPP has a key to the vault. EMEAPP is a private museum, a critical |
|
learning center and a multi-media production studio nicely packed into a brick-and-mortar |
|
facility outside of Philadelphia, Pennsylvania. EMEAPP is a 501(c)(3) non-profit |
|
organization. |
|
- You got a problem? Yo, she'll splode it. |
|
- I love sex; I think sex is completely absurdly demonized in our culture. But in |
|
the end, however much sex you want to have, with however many people in how many |
|
ways, to be loved and to love is what human beings really want. |
|
- source_sentence: What year did the Duchess die and where did it happen |
|
sentences: |
|
- 'League One |
|
|
|
|
|
League table |
|
|
|
|
|
Results summary |
|
|
|
|
|
Results by matchday |
|
|
|
|
|
Matches |
|
|
|
On 21 June 2018, the League One fixtures for the forthcoming season were announced. |
|
FA Cup |
|
|
|
|
|
The first round draw was made live on BBC by Dennis Wise and Dion Dublin on 22 |
|
October.' |
|
- "The Duchess was widowed in 2007 and died in London in 2011. Issue \n\nThe Duke\ |
|
\ and Duchess of Buccleuch and Queensberry had four children:\nRichard Scott,\ |
|
\ 10th Duke of Buccleuch (b. 1954), married Lady Elizabeth Kerr, daughter of the\ |
|
\ Marquess of Lothian, and has issue two sons and two daughters. Lord John (born\ |
|
\ 9 August 1957), married Berrin Torolsan, and lives in Istanbul, Turkey. Lady\ |
|
\ Charlotte-Anne (born 9 January 1966), married Count Bernard de Castellane in\ |
|
\ 1991, and has issue two sons and a daughter. Lord Damian (born 8 October 1969),\ |
|
\ married Elizabeth Powis, and has issue. External links\nJane in her wedding\ |
|
\ dress \nMovie clip of Jane's wedding\n\nReferences \n\n1929 births\n2011 deaths\n\ |
|
British duchesses by marriage\nJane\nScottish female models\nBritish cookbook\ |
|
\ writers\nWomen cookbook writers" |
|
- Is this common, do other people with epilepsy have dangerously low appetites? |
|
So we left there and stopped and got her a bite to eat. |
|
- source_sentence: Why is it important to keep moving over the summer |
|
sentences: |
|
- It's important to keep moving over the summer! |
|
- '2008. CHENG HF, LEE YM, Chu CH, Leung WK & Mok TMY. - Journal Editor (Hong Kong |
|
Medical Journal) 2008 |
|
|
|
- Editor-in-Chief (Hong Kong Dental Journal) 2007 |
|
|
|
- Editor-in-Chief (Hong Kong Dental Journal) 2006 |
|
|
|
- Deputy Editor (Hong Kong Dental Journal) 2004' |
|
- Both demand collective action and shared resources. While one is distinctly egalitarian |
|
and the other hierarchical in nature, both speak of sublimating private goals |
|
for the achievement of larger, shared ones. |
|
pipeline_tag: sentence-similarity |
|
library_name: sentence-transformers |
|
--- |
|
|
|
# SentenceTransformer based on Snowflake/snowflake-arctic-embed-l-v2.0 |
|
|
|
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Snowflake/snowflake-arctic-embed-l-v2.0](https://huggingface.co/Snowflake/snowflake-arctic-embed-l-v2.0). It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. |
|
|
|
## Model Details |
|
|
|
### Model Description |
|
- **Model Type:** Sentence Transformer |
|
- **Base model:** [Snowflake/snowflake-arctic-embed-l-v2.0](https://huggingface.co/Snowflake/snowflake-arctic-embed-l-v2.0) <!-- at revision 7f311bb640ad3babc0a4e3a8873240dcba44c9d2 --> |
|
- **Maximum Sequence Length:** 1024 tokens |
|
- **Output Dimensionality:** 1024 tokens |
|
- **Similarity Function:** Cosine Similarity |
|
<!-- - **Training Dataset:** Unknown --> |
|
<!-- - **Language:** Unknown --> |
|
<!-- - **License:** Unknown --> |
|
|
|
### Model Sources |
|
|
|
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
|
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
|
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
|
|
|
### Full Model Architecture |
|
|
|
``` |
|
SentenceTransformer( |
|
(0): Transformer({'max_seq_length': 1024, 'do_lower_case': False}) with Transformer model: XLMRobertaModel |
|
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) |
|
(2): Normalize() |
|
) |
|
``` |
|
|
|
## Usage |
|
|
|
### Direct Usage (Sentence Transformers) |
|
|
|
First install the Sentence Transformers library: |
|
|
|
```bash |
|
pip install -U sentence-transformers |
|
``` |
|
|
|
Then you can load this model and run inference. |
|
```python |
|
from sentence_transformers import SentenceTransformer |
|
|
|
# Download from the 🤗 Hub |
|
model = SentenceTransformer("Jrinky/snowflake") |
|
# Run inference |
|
sentences = [ |
|
'Why is it important to keep moving over the summer', |
|
"It's important to keep moving over the summer!", |
|
'2008. CHENG HF, LEE YM, Chu CH, Leung WK & Mok TMY. - Journal Editor (Hong Kong Medical Journal) 2008\n- Editor-in-Chief (Hong Kong Dental Journal) 2007\n- Editor-in-Chief (Hong Kong Dental Journal) 2006\n- Deputy Editor (Hong Kong Dental Journal) 2004', |
|
] |
|
embeddings = model.encode(sentences) |
|
print(embeddings.shape) |
|
# [3, 1024] |
|
|
|
# Get the similarity scores for the embeddings |
|
similarities = model.similarity(embeddings, embeddings) |
|
print(similarities.shape) |
|
# [3, 3] |
|
``` |
|
|
|
<!-- |
|
### Direct Usage (Transformers) |
|
|
|
<details><summary>Click to see the direct usage in Transformers</summary> |
|
|
|
</details> |
|
--> |
|
|
|
<!-- |
|
### Downstream Usage (Sentence Transformers) |
|
|
|
You can finetune this model on your own dataset. |
|
|
|
<details><summary>Click to expand</summary> |
|
|
|
</details> |
|
--> |
|
|
|
<!-- |
|
### Out-of-Scope Use |
|
|
|
*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
|
--> |
|
|
|
<!-- |
|
## Bias, Risks and Limitations |
|
|
|
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
|
--> |
|
|
|
<!-- |
|
### Recommendations |
|
|
|
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
|
--> |
|
|
|
## Training Details |
|
|
|
### Training Dataset |
|
|
|
#### Unnamed Dataset |
|
|
|
|
|
* Size: 69,500 training samples |
|
* Columns: <code>anchor</code> and <code>positive</code> |
|
* Approximate statistics based on the first 1000 samples: |
|
| | anchor | positive | |
|
|:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| |
|
| type | string | string | |
|
| details | <ul><li>min: 6 tokens</li><li>mean: 17.47 tokens</li><li>max: 44 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 113.33 tokens</li><li>max: 1024 tokens</li></ul> | |
|
* Samples: |
|
| anchor | positive | |
|
|:-----------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
|
| <code>What might have been unnecessary if better emergency plans had been implemented</code> | <code>If better emergency plans had been in place, maybe chemical dipersants wouldn't be needed. And on and on.</code> | |
|
| <code>What was the year of publication for the 3rd Edition of 'Regular Polytopes' by H.S.M. Coxeter</code> | <code>Coxeter, Regular Polytopes, 3rd Edition, Dover New York, 1973 <br> Kaleidoscopes: Selected Writings of H.S.M. Coxeter, edited by F. Arthur Sherk, Peter McMullen, Anthony C. Thompson, Asia Ivic Weiss, Wiley-Interscience Publication, 1995, <br> (Paper 22) H.S.M.</code> | |
|
| <code>Who is the author of the GURPS Shapeshifters supplement</code> | <code>GURPS Shapeshifters () is a supplement by Robert M. Schroeck for the GURPS role-playing game system, third edition.</code> | |
|
* Loss: <code>selfloss.Infonce</code> with these parameters: |
|
```json |
|
{ |
|
"scale": 20.0, |
|
"similarity_fct": "cos_sim" |
|
} |
|
``` |
|
|
|
### Evaluation Dataset |
|
|
|
#### Unnamed Dataset |
|
|
|
|
|
* Size: 17,376 evaluation samples |
|
* Columns: <code>anchor</code> and <code>positive</code> |
|
* Approximate statistics based on the first 1000 samples: |
|
| | anchor | positive | |
|
|:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| |
|
| type | string | string | |
|
| details | <ul><li>min: 6 tokens</li><li>mean: 16.87 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 115.36 tokens</li><li>max: 1024 tokens</li></ul> | |
|
* Samples: |
|
| anchor | positive | |
|
|:---------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
|
| <code>What impressive achievements did the Warriors accomplish during their last season in Division III</code> | <code>The Warriors were among the most lethal offensive teams in Division III this past year, posting a team batting average of .344 and averaging nearly seven runs per game, smacking 29 home runs, and collecting nearly 600 total bases. They shared the Little East Conference regular-season championship and later knocked off the top seed in the NCAA regional tournament (Montclair State) en route to their winningest season in 14 years.</code> | |
|
| <code>How many bars had nectar and capped honey on them</code> | <code>Eight of the bars had nectar and capped honey on them. There are eighteen bars with brood in some form on them and a mix of workers and drones.</code> | |
|
| <code>What idea is being requested regarding the 'triangle'</code> | <code>Next up...the "triangle". Please, seriously, if anyone could float me an idea, I would really appreciate it.</code> | |
|
* Loss: <code>selfloss.Infonce</code> with these parameters: |
|
```json |
|
{ |
|
"scale": 20.0, |
|
"similarity_fct": "cos_sim" |
|
} |
|
``` |
|
|
|
### Training Hyperparameters |
|
#### Non-Default Hyperparameters |
|
|
|
- `eval_strategy`: steps |
|
- `per_device_train_batch_size`: 3 |
|
- `per_device_eval_batch_size`: 3 |
|
- `learning_rate`: 5e-06 |
|
- `num_train_epochs`: 5 |
|
- `warmup_ratio`: 0.1 |
|
- `fp16`: True |
|
- `batch_sampler`: no_duplicates |
|
|
|
#### All Hyperparameters |
|
<details><summary>Click to expand</summary> |
|
|
|
- `overwrite_output_dir`: False |
|
- `do_predict`: False |
|
- `eval_strategy`: steps |
|
- `prediction_loss_only`: True |
|
- `per_device_train_batch_size`: 3 |
|
- `per_device_eval_batch_size`: 3 |
|
- `per_gpu_train_batch_size`: None |
|
- `per_gpu_eval_batch_size`: None |
|
- `gradient_accumulation_steps`: 1 |
|
- `eval_accumulation_steps`: None |
|
- `torch_empty_cache_steps`: None |
|
- `learning_rate`: 5e-06 |
|
- `weight_decay`: 0.0 |
|
- `adam_beta1`: 0.9 |
|
- `adam_beta2`: 0.999 |
|
- `adam_epsilon`: 1e-08 |
|
- `max_grad_norm`: 1.0 |
|
- `num_train_epochs`: 5 |
|
- `max_steps`: -1 |
|
- `lr_scheduler_type`: linear |
|
- `lr_scheduler_kwargs`: {} |
|
- `warmup_ratio`: 0.1 |
|
- `warmup_steps`: 0 |
|
- `log_level`: passive |
|
- `log_level_replica`: warning |
|
- `log_on_each_node`: True |
|
- `logging_nan_inf_filter`: True |
|
- `save_safetensors`: True |
|
- `save_on_each_node`: False |
|
- `save_only_model`: False |
|
- `restore_callback_states_from_checkpoint`: False |
|
- `no_cuda`: False |
|
- `use_cpu`: False |
|
- `use_mps_device`: False |
|
- `seed`: 42 |
|
- `data_seed`: None |
|
- `jit_mode_eval`: False |
|
- `use_ipex`: False |
|
- `bf16`: False |
|
- `fp16`: True |
|
- `fp16_opt_level`: O1 |
|
- `half_precision_backend`: auto |
|
- `bf16_full_eval`: False |
|
- `fp16_full_eval`: False |
|
- `tf32`: None |
|
- `local_rank`: 0 |
|
- `ddp_backend`: None |
|
- `tpu_num_cores`: None |
|
- `tpu_metrics_debug`: False |
|
- `debug`: [] |
|
- `dataloader_drop_last`: True |
|
- `dataloader_num_workers`: 0 |
|
- `dataloader_prefetch_factor`: None |
|
- `past_index`: -1 |
|
- `disable_tqdm`: False |
|
- `remove_unused_columns`: True |
|
- `label_names`: None |
|
- `load_best_model_at_end`: False |
|
- `ignore_data_skip`: False |
|
- `fsdp`: [] |
|
- `fsdp_min_num_params`: 0 |
|
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
|
- `fsdp_transformer_layer_cls_to_wrap`: None |
|
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
|
- `deepspeed`: None |
|
- `label_smoothing_factor`: 0.0 |
|
- `optim`: adamw_torch |
|
- `optim_args`: None |
|
- `adafactor`: False |
|
- `group_by_length`: False |
|
- `length_column_name`: length |
|
- `ddp_find_unused_parameters`: None |
|
- `ddp_bucket_cap_mb`: None |
|
- `ddp_broadcast_buffers`: False |
|
- `dataloader_pin_memory`: True |
|
- `dataloader_persistent_workers`: False |
|
- `skip_memory_metrics`: True |
|
- `use_legacy_prediction_loop`: False |
|
- `push_to_hub`: False |
|
- `resume_from_checkpoint`: None |
|
- `hub_model_id`: None |
|
- `hub_strategy`: every_save |
|
- `hub_private_repo`: False |
|
- `hub_always_push`: False |
|
- `gradient_checkpointing`: False |
|
- `gradient_checkpointing_kwargs`: None |
|
- `include_inputs_for_metrics`: False |
|
- `eval_do_concat_batches`: True |
|
- `fp16_backend`: auto |
|
- `push_to_hub_model_id`: None |
|
- `push_to_hub_organization`: None |
|
- `mp_parameters`: |
|
- `auto_find_batch_size`: False |
|
- `full_determinism`: False |
|
- `torchdynamo`: None |
|
- `ray_scope`: last |
|
- `ddp_timeout`: 1800 |
|
- `torch_compile`: False |
|
- `torch_compile_backend`: None |
|
- `torch_compile_mode`: None |
|
- `dispatch_batches`: None |
|
- `split_batches`: None |
|
- `include_tokens_per_second`: False |
|
- `include_num_input_tokens_seen`: False |
|
- `neftune_noise_alpha`: None |
|
- `optim_target_modules`: None |
|
- `batch_eval_metrics`: False |
|
- `eval_on_start`: False |
|
- `eval_use_gather_object`: False |
|
- `batch_sampler`: no_duplicates |
|
- `multi_dataset_batch_sampler`: proportional |
|
|
|
</details> |
|
|
|
### Training Logs |
|
| Epoch | Step | Training Loss | Validation Loss | |
|
|:------:|:----:|:-------------:|:---------------:| |
|
| 0.0777 | 150 | 0.0257 | 0.0134 | |
|
| 0.1554 | 300 | 0.0136 | 0.0082 | |
|
| 0.2332 | 450 | 0.0079 | 0.0062 | |
|
| 0.3109 | 600 | 0.0065 | 0.0051 | |
|
| 0.3886 | 750 | 0.0059 | 0.0045 | |
|
| 0.4663 | 900 | 0.0057 | 0.0040 | |
|
| 0.5440 | 1050 | 0.0064 | 0.0037 | |
|
| 0.6218 | 1200 | 0.005 | 0.0034 | |
|
| 0.6995 | 1350 | 0.0052 | 0.0034 | |
|
| 0.7772 | 1500 | 0.0041 | 0.0032 | |
|
|
|
|
|
### Framework Versions |
|
- Python: 3.12.3 |
|
- Sentence Transformers: 3.2.0 |
|
- Transformers: 4.44.2 |
|
- PyTorch: 2.6.0+cu124 |
|
- Accelerate: 1.3.0 |
|
- Datasets: 2.19.0 |
|
- Tokenizers: 0.19.1 |
|
|
|
## Citation |
|
|
|
### BibTeX |
|
|
|
#### Sentence Transformers |
|
```bibtex |
|
@inproceedings{reimers-2019-sentence-bert, |
|
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
|
author = "Reimers, Nils and Gurevych, Iryna", |
|
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
|
month = "11", |
|
year = "2019", |
|
publisher = "Association for Computational Linguistics", |
|
url = "https://arxiv.org/abs/1908.10084", |
|
} |
|
``` |
|
|
|
#### Infonce |
|
```bibtex |
|
@misc{henderson2017efficient, |
|
title={Efficient Natural Language Response Suggestion for Smart Reply}, |
|
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, |
|
year={2017}, |
|
eprint={1705.00652}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.CL} |
|
} |
|
``` |
|
|
|
<!-- |
|
## Glossary |
|
|
|
*Clearly define terms in order to be accessible across audiences.* |
|
--> |
|
|
|
<!-- |
|
## Model Card Authors |
|
|
|
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
|
--> |
|
|
|
<!-- |
|
## Model Card Contact |
|
|
|
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
|
--> |
