| --- |
| license: mit |
| base_model: surrey-nlp/roberta-base-finetuned-abbr |
| tags: |
| - generated_from_trainer |
| metrics: |
| - precision |
| - recall |
| - f1 |
| - accuracy |
| model-index: |
| - name: roberta-base-finetuned-abbr-finetuned-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-base-finetuned-abbr-finetuned-ner |
|
|
| This model is a fine-tuned version of [surrey-nlp/roberta-base-finetuned-abbr](https://huggingface.co/surrey-nlp/roberta-base-finetuned-abbr) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.1753 |
| - Precision: 0.9674 |
| - Recall: 0.9681 |
| - F1: 0.9678 |
| - Accuracy: 0.9618 |
|
|
| ## 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-06 |
| - train_batch_size: 16 |
| - eval_batch_size: 4 |
| - seed: 42 |
| - gradient_accumulation_steps: 4 |
| - total_train_batch_size: 64 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 10 |
| - mixed_precision_training: Native AMP |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | No log | 0.6 | 10 | 0.9865 | 0.7657 | 0.7982 | 0.7816 | 0.7573 | |
| | No log | 1.19 | 20 | 0.7172 | 0.8161 | 0.8566 | 0.8359 | 0.8204 | |
| | No log | 1.79 | 30 | 0.5382 | 0.8437 | 0.8759 | 0.8595 | 0.8478 | |
| | No log | 2.39 | 40 | 0.4196 | 0.8713 | 0.8938 | 0.8824 | 0.8733 | |
| | No log | 2.99 | 50 | 0.3485 | 0.8965 | 0.9112 | 0.9038 | 0.8979 | |
| | No log | 3.58 | 60 | 0.3031 | 0.9241 | 0.9325 | 0.9283 | 0.9218 | |
| | No log | 4.18 | 70 | 0.2688 | 0.9459 | 0.9496 | 0.9477 | 0.9411 | |
| | No log | 4.78 | 80 | 0.2434 | 0.9531 | 0.9559 | 0.9545 | 0.9481 | |
| | No log | 5.37 | 90 | 0.2235 | 0.9605 | 0.9623 | 0.9614 | 0.9555 | |
| | No log | 5.97 | 100 | 0.2078 | 0.9612 | 0.9623 | 0.9618 | 0.9559 | |
| | No log | 6.57 | 110 | 0.1966 | 0.9637 | 0.9647 | 0.9642 | 0.9580 | |
| | No log | 7.16 | 120 | 0.1879 | 0.9646 | 0.9655 | 0.9651 | 0.9591 | |
| | No log | 7.76 | 130 | 0.1821 | 0.9664 | 0.9671 | 0.9667 | 0.9608 | |
| | No log | 8.36 | 140 | 0.1782 | 0.9669 | 0.9676 | 0.9673 | 0.9613 | |
| | No log | 8.96 | 150 | 0.1760 | 0.9674 | 0.9683 | 0.9679 | 0.9618 | |
| | No log | 9.55 | 160 | 0.1753 | 0.9674 | 0.9681 | 0.9678 | 0.9618 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.39.3 |
| - Pytorch 2.2.2+cu121 |
| - Datasets 2.19.0 |
| - Tokenizers 0.15.2 |
| |
