| license: mit | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - accuracy | |
| - f1 | |
| - precision | |
| - recall | |
| model-index: | |
| - name: sentence-compression | |
| 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. --> | |
| # sentence-compression | |
| This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.3054 | |
| - Accuracy: 0.8802 | |
| - F1: 0.7439 | |
| - Precision: 0.7661 | |
| - Recall: 0.7229 | |
| ## 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: 5e-05 | |
| - train_batch_size: 16 | |
| - eval_batch_size: 64 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - lr_scheduler_warmup_steps: 500 | |
| - num_epochs: 3 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | |
| | 0.317 | 1.0 | 200 | 0.3566 | 0.8513 | 0.6227 | 0.7998 | 0.5099 | | |
| | 0.346 | 2.0 | 400 | 0.5853 | 0.7919 | 0.6724 | 0.5413 | 0.8875 | | |
| | 0.2365 | 3.0 | 600 | 0.3054 | 0.8802 | 0.7439 | 0.7661 | 0.7229 | | |
| ### Framework versions | |
| - Transformers 4.12.5 | |
| - Pytorch 1.10.0+cu111 | |
| - Datasets 1.16.1 | |
| - Tokenizers 0.10.3 | |