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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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metrics: |
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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: sentence_eval1 |
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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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# sentence_eval1 |
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This model is a fine-tuned version of [roberta-large-mnli](https://huggingface.co./roberta-large-mnli) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4766 |
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- Precision: {'precision': 0.863681451041519} |
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- Recall: {'recall': 0.8702170188463735} |
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- F1: {'f1': 0.8669369177156675} |
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- Acc: {'accuracy': 0.8073120494335736} |
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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: 5e-05 |
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- train_batch_size: 48 |
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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: 3.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Acc | |
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|:-------------:|:-----:|:----:|:---------------:|:---------------------------------:|:------------------------------:|:--------------------------:|:--------------------------------:| |
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| 0.437 | 1.0 | 1771 | 0.4753 | {'precision': 0.9119431443924424} | {'recall': 0.7511422044545973} | {'f1': 0.8237688874970641} | {'accuracy': 0.7681771369721936} | |
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| 0.367 | 2.0 | 3542 | 0.4342 | {'precision': 0.8658256880733946} | {'recall': 0.8623643632210166} | {'f1': 0.8640915593705294} | {'accuracy': 0.8043254376930999} | |
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| 0.2915 | 3.0 | 5313 | 0.4766 | {'precision': 0.863681451041519} | {'recall': 0.8702170188463735} | {'f1': 0.8669369177156675} | {'accuracy': 0.8073120494335736} | |
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### Framework versions |
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- Transformers 4.25.1 |
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- Pytorch 1.13.0+cu116 |
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- Datasets 2.7.1 |
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- Tokenizers 0.13.2 |
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