|
--- |
|
license: mit |
|
base_model: FacebookAI/xlm-roberta-base |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- accuracy |
|
model-index: |
|
- name: language_detector |
|
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. --> |
|
|
|
# language_detector |
|
|
|
This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the None dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.0157 |
|
- Accuracy: 0.9986 |
|
- F1 Macro: 0.9985 |
|
- Precision Macro: 0.9988 |
|
- Recall Macro: 0.9981 |
|
|
|
## 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: 5 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | Precision Macro | Recall Macro | |
|
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|:---------------:|:------------:| |
|
| 0.0207 | 1.0 | 3850 | 0.0094 | 0.9989 | 0.9987 | 0.9990 | 0.9985 | |
|
| 0.007 | 2.0 | 7700 | 0.0141 | 0.9984 | 0.9982 | 0.9983 | 0.9981 | |
|
| 0.0031 | 3.0 | 11550 | 0.0095 | 0.9993 | 0.9992 | 0.9995 | 0.999 | |
|
| 0.0083 | 4.0 | 15400 | 0.0061 | 0.9995 | 0.9995 | 0.9997 | 0.9993 | |
|
| 0.0013 | 5.0 | 19250 | 0.0157 | 0.9986 | 0.9985 | 0.9988 | 0.9981 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.42.3 |
|
- Pytorch 2.1.2 |
|
- Datasets 2.20.0 |
|
- Tokenizers 0.19.1 |
|
|
