pytorch tensorboard generated_from_trainer token-classification en dataset:EMBO/BLURB license:apache-2.0 region:us
---
datasets:
- EMBO/BLURB
language:
- en
license: apache-2.0
metrics:
- precision
- recall
- f1
- accuracy
pipeline_tag: token-classification
tags:
- generated_from_trainer
model-index:
- name: bert-large-cased-lora-finetuned-ner-EMBO-SourceData
  results: []
---

# bert-large-cased-lora-finetuned-ner-EMBO-SourceData

This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased).

It achieves the following results on the evaluation set:
- Loss: 0.1282
- Precision: 0.7999
- Recall: 0.8278
- F1: 0.8136
- Accuracy: 0.9584

## 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-SourceData%20with%20LoRA/NER%20Project%20Using%20EMBO-SourceData%20with%20LoRA.ipynb](https://github.com/DunnBC22/NLP_Projects/blob/main/Token%20Classification/Monolingual/EMBO-SourceData%20with%20LoRA/NER%20Project%20Using%20EMBO-SourceData%20with%20LoRA.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](https://huggingface.co/datasets/EMBO/BLURB)

**Token Distribution**
![Token Distribution](https://raw.githubusercontent.com/DunnBC22/NLP_Projects/main/Token%20Classification/Monolingual/EMBO-SourceData%20with%20LoRA/Images/Class%20Distribution.png)

**Token Distribution After Removing 'O' Tokens**
![Token Distribution After Removing 'O' Tokens](https://raw.githubusercontent.com/DunnBC22/NLP_Projects/main/Token%20Classification/Monolingual/EMBO-SourceData%20with%20LoRA/Images/Class%20Distribution%20After%20Removing%20Other%20Token.png)

**Histogram of Tokenized Input Lengths**

![](https://raw.githubusercontent.com/DunnBC22/NLP_Projects/main/Token%20Classification/Monolingual/EMBO-SourceData%20with%20LoRA/Images/Histogram%20of%20Encoded%20Token%20Input%20Lengths.png)

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.001
- 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 | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1552        | 1.0   | 3454  | 0.1499          | 0.7569    | 0.7968 | 0.7763 | 0.9516   |
| 0.1179        | 2.0   | 6908  | 0.1328          | 0.7910    | 0.8120 | 0.8013 | 0.9564   |
| 0.0998        | 3.0   | 10362 | 0.1282          | 0.7999    | 0.8278 | 0.8136 | 0.9584   |


### Framework versions

- Transformers 4.26.1
- Pytorch 2.0.1
- Datasets 2.13.1
- Tokenizers 0.13.3