| | --- |
| | license: mit |
| | tags: |
| | - generated_from_trainer |
| | datasets: |
| | - esnli |
| | metrics: |
| | - f1 |
| | - accuracy |
| | model-index: |
| | - name: roberta-large-e-snli-classification-nli-base |
| | results: |
| | - task: |
| | name: Text Classification |
| | type: text-classification |
| | dataset: |
| | name: esnli |
| | type: esnli |
| | config: plain_text |
| | split: validation |
| | args: plain_text |
| | metrics: |
| | - name: F1 |
| | type: f1 |
| | value: 0.9258678577111056 |
| | - name: Accuracy |
| | type: accuracy |
| | value: 0.9260312944523471 |
| | --- |
| | |
| | <!-- 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-e-snli-classification-nli-base |
| |
|
| | This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the esnli dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.2221 |
| | - F1: 0.9259 |
| | - Accuracy: 0.9260 |
| |
|
| | ## 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: 64 |
| | - 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_ratio: 0.05 |
| | - num_epochs: 3 |
| | - mixed_precision_training: Native AMP |
| | |
| | ### Training results |
| | |
| | | Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy | |
| | |:-------------:|:-----:|:----:|:---------------:|:------:|:--------:| |
| | | 0.9995 | 0.05 | 400 | 0.4236 | 0.8437 | 0.8465 | |
| | | 0.4089 | 0.09 | 800 | 0.2961 | 0.8926 | 0.8933 | |
| | | 0.3681 | 0.14 | 1200 | 0.2980 | 0.8914 | 0.8924 | |
| | | 0.3467 | 0.19 | 1600 | 0.2872 | 0.8977 | 0.8990 | |
| | | 0.324 | 0.23 | 2000 | 0.2506 | 0.9106 | 0.9110 | |
| | | 0.3222 | 0.28 | 2400 | 0.2552 | 0.9132 | 0.9128 | |
| | | 0.3138 | 0.33 | 2800 | 0.2379 | 0.9183 | 0.9183 | |
| | | 0.3107 | 0.37 | 3200 | 0.2396 | 0.9152 | 0.9156 | |
| | | 0.304 | 0.42 | 3600 | 0.2354 | 0.9174 | 0.9177 | |
| | | 0.3027 | 0.47 | 4000 | 0.2360 | 0.9191 | 0.9191 | |
| | | 0.2968 | 0.51 | 4400 | 0.2329 | 0.9182 | 0.9187 | |
| | | 0.2888 | 0.56 | 4800 | 0.2462 | 0.9189 | 0.9196 | |
| | | 0.2898 | 0.61 | 5200 | 0.2335 | 0.9206 | 0.9212 | |
| | | 0.288 | 0.65 | 5600 | 0.2350 | 0.9220 | 0.9223 | |
| | | 0.2746 | 0.7 | 6000 | 0.2208 | 0.9275 | 0.9278 | |
| | | 0.2756 | 0.75 | 6400 | 0.2304 | 0.9209 | 0.9216 | |
| | | 0.272 | 0.79 | 6800 | 0.2243 | 0.9237 | 0.9238 | |
| | | 0.2809 | 0.84 | 7200 | 0.2176 | 0.9259 | 0.9261 | |
| | | 0.2733 | 0.89 | 7600 | 0.2194 | 0.9271 | 0.9273 | |
| | | 0.2723 | 0.93 | 8000 | 0.2221 | 0.9259 | 0.9260 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.27.1 |
| | - Pytorch 1.12.1+cu113 |
| | - Datasets 2.10.1 |
| | - Tokenizers 0.13.2 |
| | |
