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roberta-large-e-snli-classification-nli-base

Text Classification
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PyTorch
roberta
generated_from_trainer
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+ ---
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+ license: mit
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - esnli
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+ metrics:
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+ - f1
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+ - accuracy
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+ model-index:
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+ - name: roberta-large-e-snli-classification-nli-base
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+ results:
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+ - task:
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+ name: Text Classification
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+ type: text-classification
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+ dataset:
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+ name: esnli
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+ type: esnli
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+ config: plain_text
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+ split: validation
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+ args: plain_text
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+ metrics:
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+ - name: F1
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+ type: f1
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+ value: 0.9258678577111056
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.9260312944523471
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # roberta-large-e-snli-classification-nli-base
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+
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+ This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the esnli dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.2221
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+ - F1: 0.9259
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+ - Accuracy: 0.9260
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 1e-05
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+ - train_batch_size: 64
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+ - eval_batch_size: 64
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+ - seed: 42
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_ratio: 0.05
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+ - num_epochs: 3
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+ - mixed_precision_training: Native AMP
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|
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+ | 0.9995 | 0.05 | 400 | 0.4236 | 0.8437 | 0.8465 |
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+ | 0.4089 | 0.09 | 800 | 0.2961 | 0.8926 | 0.8933 |
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+ | 0.3681 | 0.14 | 1200 | 0.2980 | 0.8914 | 0.8924 |
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+ | 0.3467 | 0.19 | 1600 | 0.2872 | 0.8977 | 0.8990 |
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+ | 0.324 | 0.23 | 2000 | 0.2506 | 0.9106 | 0.9110 |
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+ | 0.3222 | 0.28 | 2400 | 0.2552 | 0.9132 | 0.9128 |
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+ | 0.3138 | 0.33 | 2800 | 0.2379 | 0.9183 | 0.9183 |
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+ | 0.3107 | 0.37 | 3200 | 0.2396 | 0.9152 | 0.9156 |
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+ | 0.304 | 0.42 | 3600 | 0.2354 | 0.9174 | 0.9177 |
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+ | 0.3027 | 0.47 | 4000 | 0.2360 | 0.9191 | 0.9191 |
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+ | 0.2968 | 0.51 | 4400 | 0.2329 | 0.9182 | 0.9187 |
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+ | 0.2888 | 0.56 | 4800 | 0.2462 | 0.9189 | 0.9196 |
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+ | 0.2898 | 0.61 | 5200 | 0.2335 | 0.9206 | 0.9212 |
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+ | 0.288 | 0.65 | 5600 | 0.2350 | 0.9220 | 0.9223 |
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+ | 0.2746 | 0.7 | 6000 | 0.2208 | 0.9275 | 0.9278 |
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+ | 0.2756 | 0.75 | 6400 | 0.2304 | 0.9209 | 0.9216 |
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+ | 0.272 | 0.79 | 6800 | 0.2243 | 0.9237 | 0.9238 |
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+ | 0.2809 | 0.84 | 7200 | 0.2176 | 0.9259 | 0.9261 |
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+ | 0.2733 | 0.89 | 7600 | 0.2194 | 0.9271 | 0.9273 |
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+ | 0.2723 | 0.93 | 8000 | 0.2221 | 0.9259 | 0.9260 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.27.1
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+ - Pytorch 1.12.1+cu113
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+ - Datasets 2.10.1
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+ - Tokenizers 0.13.2