| | --- |
| | license: apache-2.0 |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - precision |
| | - recall |
| | - f1 |
| | - accuracy |
| | model-index: |
| | - name: BioLinkBERT-base-finetuned-ner |
| | results: [] |
| | --- |
| | |
| | <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| | should probably proofread and complete it, then remove this comment. --> |
| |
|
| | # BioLinkBERT-base-finetuned-ner |
| |
|
| | This model is a fine-tuned version of [michiyasunaga/BioLinkBERT-base](https://huggingface.co/michiyasunaga/BioLinkBERT-base) on an unknown dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.1226 |
| | - Precision: 0.8760 |
| | - Recall: 0.9185 |
| | - F1: 0.8968 |
| | - Accuracy: 0.9647 |
| |
|
| | ## Model description |
| |
|
| | This model is designed to perform NER function for specific text using BioLink BERT |
| |
|
| | ## Intended uses & limitations |
| |
|
| | The goal was to have a drug tag printed immediately for a particular sentence, but it has the disadvantage of being marked as LABEL |
| |
|
| | LABEL0 : irrelevant text |
| | LABEL1,2 : Drug |
| | LABEL3,4 : condition |
| |
|
| | ## Training and evaluation data |
| |
|
| | More information needed |
| |
|
| | ## Training procedure |
| |
|
| | 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) |
| |
|
| | ## How to use |
| |
|
| | from transformers import AutoTokenizer, AutoModelForTokenClassification |
| |
|
| | tokenizer = AutoTokenizer.from_pretrained("HMHMlee/BioLinkBERT-base-finetuned-ner") |
| | |
| | model = AutoModelForTokenClassification.from_pretrained("HMHMlee/BioLinkBERT-base-finetuned-ner") |
| |
|
| | ### Training hyperparameters |
| |
|
| | The following hyperparameters were used during training: |
| | - learning_rate: 1e-05 |
| | - train_batch_size: 16 |
| | - eval_batch_size: 16 |
| | - seed: 42 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: linear |
| | - num_epochs: 5 |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
| | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | | 0.1099 | 1.0 | 201 | 0.1489 | 0.8415 | 0.9032 | 0.8713 | 0.9566 | |
| | | 0.1716 | 2.0 | 402 | 0.1318 | 0.8456 | 0.9135 | 0.8782 | 0.9597 | |
| | | 0.1068 | 3.0 | 603 | 0.1197 | 0.8682 | 0.9110 | 0.8891 | 0.9641 | |
| | | 0.0161 | 4.0 | 804 | 0.1219 | 0.8694 | 0.9157 | 0.8919 | 0.9639 | |
| | | 0.1499 | 5.0 | 1005 | 0.1226 | 0.8760 | 0.9185 | 0.8968 | 0.9647 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.20.1 |
| | - Pytorch 1.12.0+cu113 |
| | - Datasets 2.4.0 |
| | - Tokenizers 0.12.1 |
| |
|
