metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- EMBO/BLURB
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-large-cased-lora-finetuned-ner-EMBO-SourceData
results: []
language:
- en
pipeline_tag: token-classification
bert-large-cased-lora-finetuned-ner-EMBO-SourceData
This model is a fine-tuned version of 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
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
Token Distribution
Token Distribution After Removing 'O' Tokens
Histogram of Tokenized Input Lengths
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