transformers pytorch tensorboard bert token-classification generated_from_trainer NER en dataset:blurb license:apache-2.0 autotrain_compatible endpoints_compatible region:us
---
datasets:
- blurb
language:
- en
license: apache-2.0
metrics:
- seqeval
pipeline_tag: token-classification
tags:
- generated_from_trainer
- NER
model-index:
- name: bert-base-cased-finetuned-ner-BC2GM-IOB
  results: []
---

# bert-base-cased-finetuned-ner-BC2GM-IOB

This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased).
It achieves the following results on the evaluation set:
- Loss: 0.0813
- Gene
  - Precision: 0.752111423914654
  - Recall: 0.8025296442687747
  - F1: 0.7765029830197338
  - Number: 6325
- Overall
  - Precision: 0.7521
  - Recall: 0.8025
  - F1: 0.7765
  - Accuracy: 0.9736

## Model description

For more information on how it was created, check out the following link: https://github.com/DunnBC22/NLP_Projects/blob/main/Token%20Classification/Monolingual/EMBO-BLURB/NER%20Project%20Using%20EMBO-BLURB%20Dataset.ipynb

## Intended uses & limitations

This model is intended to demonstrate my ability to solve a complex problem using technology.

## Training and evaluation data

Dataset Source: https://huggingface.co/datasets/EMBO/BLURB

## 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
- 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 | Gene Precision | Gene Recall | Gene F1 | Gene Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:---------:|:---------:|:---------:|:-----------------:|:--------------:|:------:|:------:|
| 0.0882        | 1.0   | 786  | 0.0771          | 0.7383 | 0.7538 | 0.7460 | 6325 | 0.7383 | 0.7538 | 0.7460 | 0.9697 |
| 0.0547        | 2.0   | 1572 | 0.0823          | 0.7617 | 0.7758 | 0.7687 | 6325 | 0.7617 | 0.7758 | 0.7687 | 0.9732 |
| 0.0356        | 3.0   | 2358 | 0.0813          | 0.7521 | 0.8025 | 0.7765 | 6325 | 0.7521 | 0.8025 | 0.7765 | 0.9736 |

*All values in the above chart are rounded to the nearest ten-thousandth.

### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0
- Datasets 2.11.0
- Tokenizers 0.13.3