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---
license: mit
base_model: Jean-Baptiste/roberta-large-ner-english
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: roberta
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
This model is a fine-tuned version of [Jean-Baptiste/roberta-large-ner-english](https://huggingface.co/Jean-Baptiste/roberta-large-ner-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3908
- Precision: 0.5990
- Recall: 0.5581
- F1: 0.5778
- Accuracy: 0.9470
## 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: 0.0001
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.99) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 151 | 0.2078 | 0.1899 | 0.2388 | 0.2115 | 0.9246 |
| No log | 2.0 | 302 | 0.1499 | 0.4322 | 0.5535 | 0.4854 | 0.9393 |
| No log | 3.0 | 453 | 0.1916 | 0.5204 | 0.4946 | 0.5072 | 0.9418 |
| 0.1542 | 4.0 | 604 | 0.1671 | 0.4615 | 0.5109 | 0.4849 | 0.9426 |
| 0.1542 | 5.0 | 755 | 0.1940 | 0.4841 | 0.4829 | 0.4835 | 0.9439 |
| 0.1542 | 6.0 | 906 | 0.2462 | 0.5066 | 0.5651 | 0.5343 | 0.9428 |
| 0.0616 | 7.0 | 1057 | 0.2106 | 0.5041 | 0.5271 | 0.5153 | 0.9437 |
| 0.0616 | 8.0 | 1208 | 0.2621 | 0.5620 | 0.5202 | 0.5403 | 0.9474 |
| 0.0616 | 9.0 | 1359 | 0.2903 | 0.5242 | 0.5550 | 0.5392 | 0.9440 |
| 0.0326 | 10.0 | 1510 | 0.3083 | 0.5883 | 0.5628 | 0.5753 | 0.9483 |
| 0.0326 | 11.0 | 1661 | 0.3125 | 0.5451 | 0.5853 | 0.5645 | 0.9444 |
| 0.0326 | 12.0 | 1812 | 0.3616 | 0.5503 | 0.5388 | 0.5445 | 0.9427 |
| 0.0326 | 13.0 | 1963 | 0.3398 | 0.5978 | 0.5023 | 0.5459 | 0.9447 |
| 0.0155 | 14.0 | 2114 | 0.2942 | 0.5701 | 0.5550 | 0.5625 | 0.9467 |
| 0.0155 | 15.0 | 2265 | 0.3723 | 0.5771 | 0.5597 | 0.5683 | 0.9462 |
| 0.0155 | 16.0 | 2416 | 0.3651 | 0.5751 | 0.5760 | 0.5755 | 0.9439 |
| 0.0062 | 17.0 | 2567 | 0.3674 | 0.5667 | 0.5891 | 0.5777 | 0.9455 |
| 0.0062 | 18.0 | 2718 | 0.3866 | 0.5897 | 0.5403 | 0.5639 | 0.9463 |
| 0.0062 | 19.0 | 2869 | 0.3908 | 0.5990 | 0.5581 | 0.5778 | 0.9470 |
| 0.0033 | 20.0 | 3020 | 0.4036 | 0.5914 | 0.5620 | 0.5763 | 0.9467 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.2
- Tokenizers 0.13.3
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