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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_TwoWayLoss_25K_bs64_P10_N5 |
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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_TwoWayLoss_25K_bs64_P10_N5 |
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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: 15.1250 |
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- Accuracy: 0.7066 |
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- Precision: 0.0321 |
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- Recall: 0.9982 |
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- F1: 0.0622 |
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- Hamming: 0.2934 |
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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: 64 |
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- eval_batch_size: 64 |
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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: 25000 |
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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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| 28.5732 | 0.16 | 5000 | 26.4288 | 0.6945 | 0.0307 | 0.9910 | 0.0595 | 0.3055 | |
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| 19.8755 | 0.32 | 10000 | 18.9620 | 0.7010 | 0.0315 | 0.9959 | 0.0610 | 0.2990 | |
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| 17.1294 | 0.47 | 15000 | 16.5587 | 0.7021 | 0.0316 | 0.9970 | 0.0613 | 0.2979 | |
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| 15.8209 | 0.63 | 20000 | 15.4919 | 0.7053 | 0.0320 | 0.9982 | 0.0620 | 0.2947 | |
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| 15.4304 | 0.79 | 25000 | 15.1250 | 0.7066 | 0.0321 | 0.9982 | 0.0622 | 0.2934 | |
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### Framework versions |
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- Transformers 4.33.3 |
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- Pytorch 2.2.0.dev20231002 |
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- Datasets 2.7.1 |
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- Tokenizers 0.13.3 |
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