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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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model-index: |
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- name: CRAFT_SciBERT_NER |
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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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# CRAFT_SciBERT_NER |
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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: 0.1143 |
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- Seqeval classification report: precision recall f1-score support |
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CHEBI 0.74 0.70 0.72 457 |
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CL 0.82 0.75 0.78 1099 |
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GGP 0.92 0.93 0.93 2232 |
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GO 0.78 0.84 0.81 2508 |
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SO 0.83 0.81 0.82 1365 |
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Taxon 0.99 0.99 0.99 87655 |
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micro avg 0.98 0.98 0.98 95316 |
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macro avg 0.85 0.84 0.84 95316 |
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weighted avg 0.98 0.98 0.98 95316 |
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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: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 32 |
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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 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Seqeval classification report | |
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|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:| |
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| No log | 1.0 | 347 | 0.1140 | precision recall f1-score support |
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CHEBI 0.66 0.69 0.67 457 |
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CL 0.83 0.69 0.75 1099 |
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GGP 0.89 0.93 0.91 2232 |
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GO 0.76 0.85 0.80 2508 |
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SO 0.79 0.73 0.76 1365 |
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Taxon 0.99 0.99 0.99 87655 |
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micro avg 0.97 0.97 0.97 95316 |
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macro avg 0.82 0.81 0.81 95316 |
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weighted avg 0.97 0.97 0.97 95316 |
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| 0.1263 | 2.0 | 695 | 0.1126 | precision recall f1-score support |
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CHEBI 0.73 0.69 0.71 457 |
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CL 0.85 0.72 0.78 1099 |
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GGP 0.91 0.93 0.92 2232 |
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GO 0.74 0.87 0.80 2508 |
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SO 0.82 0.80 0.81 1365 |
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Taxon 0.99 0.99 0.99 87655 |
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micro avg 0.97 0.97 0.97 95316 |
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macro avg 0.84 0.83 0.83 95316 |
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weighted avg 0.97 0.97 0.97 95316 |
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| 0.0326 | 3.0 | 1041 | 0.1143 | precision recall f1-score support |
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CHEBI 0.74 0.70 0.72 457 |
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CL 0.82 0.75 0.78 1099 |
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GGP 0.92 0.93 0.93 2232 |
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GO 0.78 0.84 0.81 2508 |
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SO 0.83 0.81 0.82 1365 |
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Taxon 0.99 0.99 0.99 87655 |
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micro avg 0.98 0.98 0.98 95316 |
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macro avg 0.85 0.84 0.84 95316 |
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weighted avg 0.98 0.98 0.98 95316 |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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