| --- |
| license: mit |
| base_model: roberta-base |
| tags: |
| - generated_from_trainer |
| metrics: |
| - precision |
| - recall |
| - f1 |
| - accuracy |
| model-index: |
| - name: roberta-ner |
| 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-ner |
|
|
| This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.1963 |
| - Precision: 0.3814 |
| - Recall: 0.4134 |
| - F1: 0.3968 |
| - Accuracy: 0.9525 |
|
|
| ## 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 | Precision | Recall | F1 | Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | No log | 1.0 | 60 | 0.2553 | 0.1878 | 0.1075 | 0.1368 | 0.9435 | |
| | No log | 2.0 | 120 | 0.2114 | 0.3456 | 0.2235 | 0.2714 | 0.9492 | |
| | No log | 3.0 | 180 | 0.2007 | 0.3372 | 0.3673 | 0.3516 | 0.9494 | |
| | No log | 4.0 | 240 | 0.1976 | 0.3618 | 0.3911 | 0.3758 | 0.9517 | |
| | No log | 5.0 | 300 | 0.1963 | 0.3814 | 0.4134 | 0.3968 | 0.9525 | |
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|
| ### Framework versions |
|
|
| - Transformers 4.34.1 |
| - Pytorch 2.1.0+cu118 |
| - Datasets 2.14.6 |
| - Tokenizers 0.14.1 |
|
|
