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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: BioLinkBERT-base-finetuned-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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# BioLinkBERT-base-finetuned-ner |
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This model is a fine-tuned version of [michiyasunaga/BioLinkBERT-base](https://huggingface.co/michiyasunaga/BioLinkBERT-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1226 |
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- Precision: 0.8760 |
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- Recall: 0.9185 |
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- F1: 0.8968 |
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- Accuracy: 0.9647 |
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## Model description |
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This model is designed to perform NER function for specific text using BioLink BERT |
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## Intended uses & limitations |
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The goal was to have a drug tag printed immediately for a particular sentence, but it has the disadvantage of being marked as LABEL |
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LABEL0 : irrelevant text |
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LABEL1,2 : Drug |
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LABEL3,4 : condition |
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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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Reference Code: SciBERT Fine-Tuning on Drug/ADE Corpus (https://github.com/jsylee/personal-projects/blob/master/Hugging%20Face%20ADR%20Fine-Tuning/SciBERT%20ADR%20Fine-Tuning.ipynb) |
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## How to use |
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from transformers import AutoTokenizer, AutoModelForTokenClassification |
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tokenizer = AutoTokenizer.from_pretrained("HMHMlee/BioLinkBERT-base-finetuned-ner") |
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model = AutoModelForTokenClassification.from_pretrained("HMHMlee/BioLinkBERT-base-finetuned-ner") |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-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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- 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: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| 0.1099 | 1.0 | 201 | 0.1489 | 0.8415 | 0.9032 | 0.8713 | 0.9566 | |
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| 0.1716 | 2.0 | 402 | 0.1318 | 0.8456 | 0.9135 | 0.8782 | 0.9597 | |
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| 0.1068 | 3.0 | 603 | 0.1197 | 0.8682 | 0.9110 | 0.8891 | 0.9641 | |
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| 0.0161 | 4.0 | 804 | 0.1219 | 0.8694 | 0.9157 | 0.8919 | 0.9639 | |
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| 0.1499 | 5.0 | 1005 | 0.1226 | 0.8760 | 0.9185 | 0.8968 | 0.9647 | |
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### Framework versions |
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- Transformers 4.20.1 |
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- Pytorch 1.12.0+cu113 |
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- Datasets 2.4.0 |
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- Tokenizers 0.12.1 |
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