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--- |
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license: apache-2.0 |
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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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- accuracy |
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- f1 |
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- rouge |
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- bleu |
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model-index: |
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- name: t5-small-e-snli-generation-label_and_explanation-selected-b64 |
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results: |
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- task: |
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name: Sequence-to-sequence Language Modeling |
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type: text2text-generation |
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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: Accuracy |
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type: accuracy |
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value: 0.8732981101402154 |
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- name: F1 |
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type: f1 |
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value: 0.8729633394714756 |
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- name: Rouge1 |
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type: rouge |
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value: 0.6144211309547953 |
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- name: Bleu |
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type: bleu |
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value: 0.4223746159966924 |
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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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# t5-small-e-snli-generation-label_and_explanation-selected-b64 |
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This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the esnli dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.9257 |
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- Accuracy: 0.8733 |
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- F1: 0.8730 |
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- Bertscore F1: 0.9356 |
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- Rouge1: 0.6144 |
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- Rouge2: 0.4096 |
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- Rougel: 0.5592 |
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- Rougelsum: 0.5611 |
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- Bleu: 0.4224 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.001 |
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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: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Bertscore F1 | Rouge1 | Rouge2 | Rougel | Rougelsum | Bleu | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:------------:|:------:|:------:|:------:|:---------:|:------:| |
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| 1.6638 | 0.23 | 2000 | 2.0039 | 0.7883 | 0.7869 | 0.9274 | 0.5705 | 0.3601 | 0.5175 | 0.5192 | 0.3730 | |
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| 1.2998 | 0.47 | 4000 | 1.9378 | 0.8283 | 0.8293 | 0.9303 | 0.5861 | 0.3748 | 0.5310 | 0.5329 | 0.3854 | |
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| 1.2351 | 0.7 | 6000 | 1.8752 | 0.8431 | 0.8437 | 0.9321 | 0.5951 | 0.3880 | 0.5411 | 0.5430 | 0.3954 | |
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| 1.1948 | 0.93 | 8000 | 1.9346 | 0.8536 | 0.8529 | 0.9333 | 0.6018 | 0.3931 | 0.5451 | 0.5472 | 0.4006 | |
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| 1.1537 | 1.16 | 10000 | 1.8881 | 0.8654 | 0.8647 | 0.9332 | 0.6070 | 0.4023 | 0.5483 | 0.5506 | 0.4096 | |
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| 1.1298 | 1.4 | 12000 | 1.9265 | 0.8690 | 0.8685 | 0.9337 | 0.6053 | 0.3988 | 0.5507 | 0.5526 | 0.4093 | |
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| 1.1219 | 1.63 | 14000 | 1.9017 | 0.8713 | 0.8714 | 0.9332 | 0.6029 | 0.3941 | 0.5470 | 0.5489 | 0.4042 | |
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| 1.1088 | 1.86 | 16000 | 1.9257 | 0.8733 | 0.8730 | 0.9356 | 0.6144 | 0.4096 | 0.5592 | 0.5611 | 0.4224 | |
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### Framework versions |
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- Transformers 4.27.4 |
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- Pytorch 2.0.0+cu117 |
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- Datasets 2.11.0 |
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- Tokenizers 0.13.2 |
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