readme: ---
  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
  