| --- |
| license: mit |
| tags: |
| - generated_from_trainer |
| datasets: |
| - conllpp |
| metrics: |
| - precision |
| - recall |
| - f1 |
| - accuracy |
| model-index: |
| - name: roberta-large-md-conllpp |
| results: |
| - task: |
| name: Token Classification |
| type: token-classification |
| dataset: |
| name: conllpp |
| type: conllpp |
| config: conllpp |
| split: train |
| args: conllpp |
| metrics: |
| - name: Precision |
| type: precision |
| value: 0.9971177780689113 |
| - name: Recall |
| type: recall |
| value: 0.9968043586452576 |
| - name: F1 |
| type: f1 |
| value: 0.9969610437242934 |
| - name: Accuracy |
| type: accuracy |
| value: 0.995003768708948 |
| --- |
| |
| <!-- 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-large-md-conllpp |
|
|
| This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the conllpp dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.0457 |
| - Precision: 0.9971 |
| - Recall: 0.9968 |
| - F1: 0.9970 |
| - Accuracy: 0.9950 |
|
|
| ## 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: 1e-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 | |
| |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | 0.0748 | 1.0 | 878 | 0.0309 | 0.9959 | 0.9962 | 0.9961 | 0.9935 | |
| | 0.0111 | 2.0 | 1756 | 0.0346 | 0.9974 | 0.9967 | 0.9970 | 0.9951 | |
| | 0.0057 | 3.0 | 2634 | 0.0348 | 0.9974 | 0.9960 | 0.9967 | 0.9946 | |
| | 0.0031 | 4.0 | 3512 | 0.0434 | 0.9976 | 0.9964 | 0.9970 | 0.9951 | |
| | 0.0017 | 5.0 | 4390 | 0.0457 | 0.9971 | 0.9968 | 0.9970 | 0.9950 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.21.2 |
| - Pytorch 1.12.1 |
| - Datasets 2.4.0 |
| - Tokenizers 0.12.1 |
|
|
