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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: JNLPBA_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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# JNLPBA_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: 0.1450 |
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- Seqeval classification report: precision recall f1-score support |
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DNA 0.75 0.83 0.79 955 |
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RNA 0.80 0.83 0.82 1144 |
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cell_line 0.76 0.79 0.78 5330 |
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cell_type 0.86 0.91 0.88 2518 |
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protein 0.87 0.85 0.86 926 |
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micro avg 0.80 0.83 0.81 10873 |
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macro avg 0.81 0.84 0.82 10873 |
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weighted avg 0.80 0.83 0.81 10873 |
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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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| 0.2726 | 1.0 | 582 | 0.1526 | precision recall f1-score support |
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DNA 0.73 0.82 0.77 955 |
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RNA 0.79 0.82 0.81 1144 |
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cell_line 0.75 0.78 0.76 5330 |
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cell_type 0.86 0.86 0.86 2518 |
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protein 0.86 0.84 0.85 926 |
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micro avg 0.79 0.81 0.80 10873 |
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macro avg 0.80 0.82 0.81 10873 |
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weighted avg 0.79 0.81 0.80 10873 |
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| 0.145 | 2.0 | 1164 | 0.1473 | precision recall f1-score support |
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DNA 0.73 0.82 0.77 955 |
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RNA 0.85 0.78 0.81 1144 |
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cell_line 0.77 0.78 0.78 5330 |
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cell_type 0.85 0.92 0.88 2518 |
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protein 0.88 0.83 0.85 926 |
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micro avg 0.80 0.82 0.81 10873 |
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macro avg 0.81 0.83 0.82 10873 |
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weighted avg 0.80 0.82 0.81 10873 |
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| 0.1276 | 3.0 | 1746 | 0.1450 | precision recall f1-score support |
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DNA 0.75 0.83 0.79 955 |
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RNA 0.80 0.83 0.82 1144 |
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cell_line 0.76 0.79 0.78 5330 |
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cell_type 0.86 0.91 0.88 2518 |
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protein 0.87 0.85 0.86 926 |
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micro avg 0.80 0.83 0.81 10873 |
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macro avg 0.81 0.84 0.82 10873 |
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weighted avg 0.80 0.83 0.81 10873 |
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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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