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--- |
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base_model: allenai/scibert_scivocab_uncased |
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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: scibert_prefix_cont_ll_SEP |
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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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<!-- extra_gated_prompt: "By requesting access to this model, you agree to properly reference it in your works." |
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extra_gated_fields: |
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Company or Institution: text |
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Country: country |
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I want to use this dataset for: |
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type: select |
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options: |
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- Research |
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- Education |
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- label: Other |
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value: other --> |
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# scibert_prefix_cont_ll_SEP |
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This model is a fine-tuned version of [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0769 |
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- F1 Weighted: 0.9112 |
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- F1 Samples: 0.9155 |
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- F1 Macro: 0.8184 |
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- F1 Micro: 0.9121 |
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- Accuracy: 0.8863 |
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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: 1e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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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: 15 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | F1 Weighted | F1 Samples | F1 Macro | F1 Micro | Accuracy | |
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|:-------------:|:------:|:----:|:---------------:|:-----------:|:----------:|:--------:|:--------:|:--------:| |
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| 0.2213 | 0.3381 | 500 | 0.1392 | 0.8151 | 0.8223 | 0.6081 | 0.8355 | 0.8018 | |
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| 0.1377 | 0.6761 | 1000 | 0.1129 | 0.8523 | 0.8584 | 0.6889 | 0.8645 | 0.8342 | |
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| 0.1214 | 1.0142 | 1500 | 0.1103 | 0.8504 | 0.8552 | 0.6955 | 0.8613 | 0.8302 | |
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| 0.0921 | 1.3523 | 2000 | 0.0961 | 0.8656 | 0.8655 | 0.7111 | 0.8740 | 0.8390 | |
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| 0.0863 | 1.6903 | 2500 | 0.0900 | 0.8789 | 0.8810 | 0.7281 | 0.8847 | 0.8545 | |
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| 0.0825 | 2.0284 | 3000 | 0.0959 | 0.8764 | 0.8844 | 0.7323 | 0.8826 | 0.8532 | |
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| 0.0567 | 2.3665 | 3500 | 0.0856 | 0.8879 | 0.8951 | 0.7454 | 0.8922 | 0.8633 | |
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| 0.061 | 2.7045 | 4000 | 0.0952 | 0.8802 | 0.8827 | 0.7397 | 0.8856 | 0.8586 | |
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| 0.0532 | 3.0426 | 4500 | 0.0839 | 0.8979 | 0.9058 | 0.7639 | 0.9031 | 0.8775 | |
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| 0.0361 | 3.3807 | 5000 | 0.0831 | 0.9007 | 0.9113 | 0.7791 | 0.9045 | 0.8769 | |
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| 0.0369 | 3.7187 | 5500 | 0.0833 | 0.9018 | 0.9094 | 0.7880 | 0.9031 | 0.8775 | |
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| 0.0392 | 4.0568 | 6000 | 0.0826 | 0.9062 | 0.9108 | 0.8180 | 0.9081 | 0.8823 | |
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| 0.027 | 4.3949 | 6500 | 0.0769 | 0.9112 | 0.9155 | 0.8184 | 0.9121 | 0.8863 | |
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| 0.0251 | 4.7329 | 7000 | 0.0868 | 0.8996 | 0.9061 | 0.7693 | 0.9018 | 0.8714 | |
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| 0.0255 | 5.0710 | 7500 | 0.0867 | 0.9083 | 0.9147 | 0.8048 | 0.9115 | 0.8870 | |
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| 0.0212 | 5.4091 | 8000 | 0.0834 | 0.9100 | 0.9161 | 0.8209 | 0.9116 | 0.8850 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.3.1+cu121 |
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- Datasets 2.19.2 |
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- Tokenizers 0.19.1 |
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## Citation |
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If using this model in your work, please cite: |
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``` |
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@misc {scibert_claim-classification, |
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author = { Bleuze, Clémentine }, |
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title = { Fine-tuned SciBERT model for claim classification }, |
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year = 2024, |
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url = { https://huggingface.co/ClementineBleuze/scibert_claim-classification }, |
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doi = { 10.57967/hf/4797 }, |
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publisher = { Hugging Face } |
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} |
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``` |
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