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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: google-flan-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: 0.8691322901849218 |
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- name: F1 |
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type: f1 |
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value: 0.8686267742768865 |
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- name: Rouge1 |
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type: rouge |
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value: 0.6062872493545299 |
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- name: Bleu |
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type: bleu |
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value: 0.4012059786299585 |
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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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# google-flan-t5-small-e-snli-generation-label_and_explanation-selected-b64 |
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This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co./google/flan-t5-small) on the esnli dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.8703 |
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- Accuracy: 0.8691 |
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- F1: 0.8686 |
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- Bertscore F1: 0.9338 |
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- Rouge1: 0.6063 |
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- Rouge2: 0.3995 |
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- Rougel: 0.5500 |
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- Rougelsum: 0.5521 |
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- Bleu: 0.4012 |
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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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| 1.4692 | 0.23 | 2000 | 1.7872 | 0.8212 | 0.8203 | 0.9287 | 0.5787 | 0.3685 | 0.5239 | 0.5257 | 0.3856 | |
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| 1.2505 | 0.47 | 4000 | 1.8808 | 0.8263 | 0.8264 | 0.9308 | 0.5870 | 0.3749 | 0.5321 | 0.5337 | 0.3904 | |
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| 1.2003 | 0.7 | 6000 | 1.8477 | 0.8475 | 0.8481 | 0.9325 | 0.5984 | 0.3913 | 0.5452 | 0.5469 | 0.4004 | |
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| 1.1624 | 0.93 | 8000 | 1.8244 | 0.8599 | 0.8587 | 0.9335 | 0.6029 | 0.3928 | 0.5441 | 0.5457 | 0.4024 | |
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| 1.1155 | 1.16 | 10000 | 1.8499 | 0.8695 | 0.8688 | 0.9331 | 0.6083 | 0.4019 | 0.5519 | 0.5540 | 0.4022 | |
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| 1.0913 | 1.4 | 12000 | 1.8703 | 0.8691 | 0.8686 | 0.9338 | 0.6063 | 0.3995 | 0.5500 | 0.5521 | 0.4012 | |
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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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