| | --- |
| | license: mit |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - precision |
| | - recall |
| | - f1 |
| | - accuracy |
| | base_model: roberta-large |
| | model-index: |
| | - name: roberta-large-aces |
| | 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-large-aces |
| |
|
| | This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an unknown dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.5257 |
| | - Precision: 0.8561 |
| | - Recall: 0.8594 |
| | - F1: 0.8553 |
| | - Accuracy: 0.8594 |
| | - F1 Who: 0.8494 |
| | - F1 What: 0.8391 |
| | - F1 Where: 0.7558 |
| | - F1 How: 0.9208 |
| |
|
| | ## 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: 8 |
| | - eval_batch_size: 8 |
| | - 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 | F1 Who | F1 What | F1 Where | F1 How | |
| | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|:------:|:-------:|:--------:|:------:| |
| | | 0.4619 | 1.0 | 87 | 0.5447 | 0.8247 | 0.8416 | 0.8308 | 0.8416 | 0.8309 | 0.8188 | 0.6973 | 0.9244 | |
| | | 0.4358 | 2.0 | 174 | 0.4662 | 0.8522 | 0.8571 | 0.8517 | 0.8571 | 0.8314 | 0.8446 | 0.7613 | 0.9238 | |
| | | 0.3793 | 3.0 | 261 | 0.4892 | 0.8507 | 0.8622 | 0.8556 | 0.8622 | 0.8321 | 0.8418 | 0.7725 | 0.9280 | |
| | | 0.2875 | 4.0 | 348 | 0.5034 | 0.8702 | 0.8641 | 0.8593 | 0.8641 | 0.8471 | 0.8441 | 0.7715 | 0.9225 | |
| | | 0.1847 | 5.0 | 435 | 0.5257 | 0.8561 | 0.8594 | 0.8553 | 0.8594 | 0.8494 | 0.8391 | 0.7558 | 0.9208 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.25.1 |
| | - Pytorch 1.13.1+cu117 |
| | - Datasets 2.8.0 |
| | - Tokenizers 0.13.2 |
| |
|
