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--- |
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library_name: transformers |
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license: mit |
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base_model: facebook/bart-large-mnli |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: bart-large-mnli_nli |
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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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# bart-large-mnli_nli |
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This model is a fine-tuned version of [facebook/bart-large-mnli](https://huggingface.co/facebook/bart-large-mnli) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 3.1748 |
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- Accuracy: 0.6146 |
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- Precision Macro: 0.6164 |
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- Recall Macro: 0.6143 |
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- F1 Macro: 0.6144 |
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- F1 Weighted: 0.6145 |
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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: 64 |
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- eval_batch_size: 64 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 128 |
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 20 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision Macro | Recall Macro | F1 Macro | F1 Weighted | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------------:|:------------:|:--------:|:-----------:| |
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| 0.9341 | 1.0 | 143 | 0.9033 | 0.5916 | 0.6003 | 0.5914 | 0.5927 | 0.5926 | |
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| 0.858 | 2.0 | 286 | 0.9499 | 0.5477 | 0.6120 | 0.5459 | 0.5366 | 0.5368 | |
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| 0.8298 | 3.0 | 429 | 0.9050 | 0.5849 | 0.5995 | 0.5868 | 0.5782 | 0.5775 | |
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| 0.7423 | 4.0 | 572 | 0.9457 | 0.6027 | 0.6128 | 0.6037 | 0.5964 | 0.5959 | |
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| 0.6525 | 5.0 | 715 | 0.9301 | 0.6009 | 0.6127 | 0.6002 | 0.6008 | 0.6008 | |
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| 0.5548 | 6.0 | 858 | 1.0490 | 0.6200 | 0.6258 | 0.6209 | 0.6164 | 0.6159 | |
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| 0.4609 | 7.0 | 1001 | 1.0379 | 0.6137 | 0.6159 | 0.6144 | 0.6131 | 0.6129 | |
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| 0.3427 | 8.0 | 1144 | 1.1565 | 0.6018 | 0.6050 | 0.6013 | 0.6017 | 0.6017 | |
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| 0.2528 | 9.0 | 1287 | 1.3466 | 0.5809 | 0.5906 | 0.5801 | 0.5788 | 0.5790 | |
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| 0.1635 | 10.0 | 1430 | 1.5895 | 0.6111 | 0.6163 | 0.6121 | 0.6090 | 0.6087 | |
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| 0.1308 | 11.0 | 1573 | 1.7222 | 0.6106 | 0.6118 | 0.6111 | 0.6104 | 0.6103 | |
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| 0.0883 | 12.0 | 1716 | 2.1606 | 0.6027 | 0.6052 | 0.6022 | 0.6019 | 0.6020 | |
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| 0.057 | 13.0 | 1859 | 2.2558 | 0.6093 | 0.6101 | 0.6093 | 0.6088 | 0.6087 | |
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| 0.0431 | 14.0 | 2002 | 2.4892 | 0.6169 | 0.6173 | 0.6168 | 0.6168 | 0.6168 | |
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| 0.0246 | 15.0 | 2145 | 2.8257 | 0.6155 | 0.6178 | 0.6153 | 0.6147 | 0.6147 | |
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| 0.0144 | 16.0 | 2288 | 2.8977 | 0.6169 | 0.6172 | 0.6168 | 0.6168 | 0.6168 | |
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| 0.0116 | 17.0 | 2431 | 3.0826 | 0.6173 | 0.6202 | 0.6170 | 0.6175 | 0.6175 | |
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| 0.0071 | 18.0 | 2574 | 3.1267 | 0.6164 | 0.6186 | 0.6160 | 0.6163 | 0.6163 | |
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| 0.0019 | 19.0 | 2717 | 3.1718 | 0.6142 | 0.6161 | 0.6138 | 0.6139 | 0.6140 | |
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| 0.0033 | 20.0 | 2860 | 3.1748 | 0.6146 | 0.6164 | 0.6143 | 0.6144 | 0.6145 | |
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### Framework versions |
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- Transformers 4.55.0 |
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- Pytorch 2.7.0+cu126 |
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- Datasets 4.0.0 |
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- Tokenizers 0.21.4 |
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