transformers tensorboard safetensors bert token-classification generated_from_trainer base_model:bionlp/bluebert_pubmed_mimic_uncased_L-12_H-768_A-12 base_model:finetune:bionlp/bluebert_pubmed_mimic_uncased_L-12_H-768_A-12 license:cc0-1.0 autotrain_compatible endpoints_compatible region:us
---
base_model: bionlp/bluebert_pubmed_mimic_uncased_L-12_H-768_A-12
license: cc0-1.0
tags:
- generated_from_trainer
model-index:
- name: BC5CDR_BlueBERT_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. -->

# BC5CDR_BlueBERT_NER

This model is a fine-tuned version of [bionlp/bluebert_pubmed_mimic_uncased_L-12_H-768_A-12](https://huggingface.co/bionlp/bluebert_pubmed_mimic_uncased_L-12_H-768_A-12) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0944
- Seqeval classification report:               precision    recall  f1-score   support

    Chemical       0.84      0.89      0.87      7079
     Disease       0.82      0.85      0.83      4968

   micro avg       0.83      0.87      0.85     12047
   macro avg       0.83      0.87      0.85     12047
weighted avg       0.83      0.87      0.85     12047


## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Seqeval classification report                                                                                                                                                                                                                                                                                                          |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| No log        | 1.0   | 143  | 0.1111          |               precision    recall  f1-score   support

    Chemical       0.82      0.86      0.84      7079
     Disease       0.76      0.83      0.80      4968

   micro avg       0.79      0.85      0.82     12047
   macro avg       0.79      0.85      0.82     12047
weighted avg       0.79      0.85      0.82     12047
 |
| No log        | 2.0   | 286  | 0.0987          |               precision    recall  f1-score   support

    Chemical       0.83      0.89      0.86      7079
     Disease       0.78      0.86      0.82      4968

   micro avg       0.81      0.88      0.84     12047
   macro avg       0.80      0.87      0.84     12047
weighted avg       0.81      0.88      0.84     12047
 |
| No log        | 3.0   | 429  | 0.0944          |               precision    recall  f1-score   support

    Chemical       0.84      0.89      0.87      7079
     Disease       0.82      0.85      0.83      4968

   micro avg       0.83      0.87      0.85     12047
   macro avg       0.83      0.87      0.85     12047
weighted avg       0.83      0.87      0.85     12047
 |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
