| | --- |
| | language: |
| | - en |
| | license: mit |
| | base_model: roberta-base |
| | tags: |
| | - pytorch |
| | - RobertaForTokenClassification |
| | - named-entity-recognition |
| | - roberta-base |
| | - generated_from_trainer |
| | metrics: |
| | - recall |
| | - precision |
| | - f1 |
| | - accuracy |
| | model-index: |
| | - name: roberta-base-ontonotes |
| | 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-ontonotes |
| |
|
| | This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the tner/ontonotes5 dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.0695 |
| | - Recall: 0.9227 |
| | - Precision: 0.9013 |
| | - F1: 0.9118 |
| | - Accuracy: 0.9820 |
| |
|
| | ## 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: 8e-05 |
| | - train_batch_size: 32 |
| | - eval_batch_size: 160 |
| | - seed: 75241309 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: cosine |
| | - training_steps: 6000 |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Recall | Precision | F1 | Accuracy | |
| | |:-------------:|:-----:|:----:|:---------------:|:------:|:---------:|:------:|:--------:| |
| | | 0.1305 | 0.31 | 600 | 0.1169 | 0.8550 | 0.8139 | 0.8340 | 0.9681 | |
| | | 0.118 | 0.63 | 1200 | 0.0925 | 0.8769 | 0.8592 | 0.8680 | 0.9750 | |
| | | 0.0937 | 0.94 | 1800 | 0.0874 | 0.8939 | 0.8609 | 0.8771 | 0.9764 | |
| | | 0.0698 | 1.25 | 2400 | 0.0821 | 0.9066 | 0.8775 | 0.8918 | 0.9784 | |
| | | 0.0663 | 1.56 | 3000 | 0.0827 | 0.9124 | 0.8764 | 0.8940 | 0.9789 | |
| | | 0.0624 | 1.88 | 3600 | 0.0732 | 0.9179 | 0.8868 | 0.9021 | 0.9804 | |
| | | 0.0364 | 2.19 | 4200 | 0.0750 | 0.9204 | 0.8968 | 0.9085 | 0.9816 | |
| | | 0.0429 | 2.5 | 4800 | 0.0699 | 0.9198 | 0.9031 | 0.9114 | 0.9818 | |
| | | 0.0323 | 2.82 | 5400 | 0.0697 | 0.9227 | 0.9008 | 0.9116 | 0.9819 | |
| | | 0.0334 | 3.13 | 6000 | 0.0695 | 0.9227 | 0.9013 | 0.9118 | 0.9820 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.36.2 |
| | - Pytorch 2.1.0+cu121 |
| | - Datasets 2.15.0 |
| | - Tokenizers 0.15.0 |
| |
|
