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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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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: SciBERT_100K_steps |
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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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# SciBERT_100K_steps |
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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: 0.0144 |
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- Accuracy: 0.9947 |
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- Precision: 0.7850 |
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- Recall: 0.6355 |
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- F1: 0.7024 |
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- Hamming: 0.0053 |
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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: 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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- lr_scheduler_warmup_ratio: 0.1 |
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- training_steps: 100000 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Hamming | |
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|:-------------:|:-----:|:------:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:| |
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| 0.1681 | 0.08 | 5000 | 0.0487 | 0.9902 | 0.0 | 0.0 | 0.0 | 0.0098 | |
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| 0.032 | 0.16 | 10000 | 0.0223 | 0.9930 | 0.8068 | 0.3728 | 0.5100 | 0.0070 | |
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| 0.0201 | 0.24 | 15000 | 0.0186 | 0.9937 | 0.7815 | 0.4970 | 0.6076 | 0.0063 | |
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| 0.018 | 0.32 | 20000 | 0.0172 | 0.9941 | 0.7763 | 0.5550 | 0.6472 | 0.0059 | |
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| 0.017 | 0.4 | 25000 | 0.0166 | 0.9942 | 0.7864 | 0.5624 | 0.6558 | 0.0058 | |
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| 0.0166 | 0.47 | 30000 | 0.0163 | 0.9943 | 0.7707 | 0.5880 | 0.6671 | 0.0057 | |
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| 0.0163 | 0.55 | 35000 | 0.0160 | 0.9943 | 0.7802 | 0.5809 | 0.6659 | 0.0057 | |
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| 0.0159 | 0.63 | 40000 | 0.0158 | 0.9944 | 0.7719 | 0.6012 | 0.6759 | 0.0056 | |
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| 0.0157 | 0.71 | 45000 | 0.0155 | 0.9945 | 0.7750 | 0.6104 | 0.6829 | 0.0055 | |
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| 0.0154 | 0.79 | 50000 | 0.0153 | 0.9945 | 0.7734 | 0.6202 | 0.6884 | 0.0055 | |
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| 0.0153 | 0.87 | 55000 | 0.0151 | 0.9945 | 0.7823 | 0.6072 | 0.6837 | 0.0055 | |
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| 0.0152 | 0.95 | 60000 | 0.0151 | 0.9945 | 0.7813 | 0.6124 | 0.6866 | 0.0055 | |
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| 0.0148 | 1.03 | 65000 | 0.0149 | 0.9946 | 0.7843 | 0.6208 | 0.6930 | 0.0054 | |
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| 0.0143 | 1.11 | 70000 | 0.0148 | 0.9946 | 0.7802 | 0.6231 | 0.6929 | 0.0054 | |
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| 0.0142 | 1.19 | 75000 | 0.0148 | 0.9946 | 0.7714 | 0.6377 | 0.6982 | 0.0054 | |
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| 0.0141 | 1.27 | 80000 | 0.0146 | 0.9947 | 0.7837 | 0.6281 | 0.6973 | 0.0053 | |
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| 0.0141 | 1.34 | 85000 | 0.0146 | 0.9947 | 0.7836 | 0.6374 | 0.7030 | 0.0053 | |
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| 0.014 | 1.42 | 90000 | 0.0145 | 0.9947 | 0.7859 | 0.6326 | 0.7010 | 0.0053 | |
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| 0.0139 | 1.5 | 95000 | 0.0145 | 0.9947 | 0.7875 | 0.6317 | 0.7010 | 0.0053 | |
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| 0.0139 | 1.58 | 100000 | 0.0144 | 0.9947 | 0.7850 | 0.6355 | 0.7024 | 0.0053 | |
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### Framework versions |
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- Transformers 4.35.0.dev0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.7.1 |
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- Tokenizers 0.14.1 |
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