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--- |
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license: mit |
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base_model: microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: CRAFT_PubMedBERT_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_PubMedBERT_NER |
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This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1043 |
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- Seqeval classification report: precision recall f1-score support |
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CHEBI 0.71 0.73 0.72 616 |
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CL 0.85 0.89 0.87 1740 |
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GGP 0.84 0.76 0.80 611 |
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GO 0.89 0.90 0.90 3810 |
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SO 0.81 0.83 0.82 8854 |
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Taxon 0.58 0.60 0.59 284 |
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micro avg 0.82 0.84 0.83 15915 |
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macro avg 0.78 0.79 0.78 15915 |
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weighted avg 0.82 0.84 0.83 15915 |
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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 | 0.1260 | precision recall f1-score support |
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CHEBI 0.66 0.61 0.63 616 |
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CL 0.81 0.86 0.83 1740 |
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GGP 0.74 0.54 0.63 611 |
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GO 0.86 0.89 0.87 3810 |
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SO 0.73 0.78 0.76 8854 |
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Taxon 0.47 0.57 0.52 284 |
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micro avg 0.76 0.80 0.78 15915 |
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macro avg 0.71 0.71 0.71 15915 |
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weighted avg 0.76 0.80 0.78 15915 |
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| 0.182 | 2.0 | 695 | 0.1089 | precision recall f1-score support |
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CHEBI 0.69 0.74 0.71 616 |
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CL 0.84 0.88 0.86 1740 |
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GGP 0.83 0.74 0.78 611 |
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GO 0.88 0.90 0.89 3810 |
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SO 0.79 0.82 0.81 8854 |
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Taxon 0.57 0.60 0.58 284 |
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micro avg 0.81 0.84 0.82 15915 |
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macro avg 0.77 0.78 0.77 15915 |
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weighted avg 0.81 0.84 0.82 15915 |
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| 0.0443 | 3.0 | 1041 | 0.1043 | precision recall f1-score support |
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CHEBI 0.71 0.73 0.72 616 |
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CL 0.85 0.89 0.87 1740 |
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GGP 0.84 0.76 0.80 611 |
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GO 0.89 0.90 0.90 3810 |
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SO 0.81 0.83 0.82 8854 |
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Taxon 0.58 0.60 0.59 284 |
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micro avg 0.82 0.84 0.83 15915 |
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macro avg 0.78 0.79 0.78 15915 |
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weighted avg 0.82 0.84 0.83 15915 |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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