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--- |
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license: mit |
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base_model: roberta-base |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: Models-RoBERTa-1704501009.345538 |
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results: [] |
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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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# Models-RoBERTa-1704501009.345538 |
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This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an MNLI dataset. |
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It achieves the following results on the evaluation set (1000 instances of MNLI validation matched): |
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- eval_loss: 0.2357 |
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- eval_accuracy: 0.92 |
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- eval_runtime: 7.4465 |
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- eval_samples_per_second: 134.292 |
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- eval_steps_per_second: 4.297 |
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- epoch: 1.03 |
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- step: 12597 |
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## Model description |
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The baseline NLI model is a fine-tuned version of *roberta-base* for Text Classifacation |
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on the MNLI dataset , with entailments as label 0 and all others (neutral or contradiction) |
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as label 1. |
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Two classes: |
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* entailment: 0 |
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* non-entailment: 1 |
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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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Model's performance on the validation sets: |
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``` |
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MNLI: 92.07% |
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MNLI-mm: 92.09% |
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``` |
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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: 2e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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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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- num_epochs: 2 |
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### Framework versions |
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- Transformers 4.36.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.0 |
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