metadata
library_name: transformers
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
datasets:
- ncbi_disease
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: ncbi_disease
type: ncbi_disease
config: ncbi_disease
split: validation
args: ncbi_disease
metrics:
- name: Precision
type: precision
value: 0.7791907514450868
- name: Recall
type: recall
value: 0.8564167725540025
- name: F1
type: f1
value: 0.8159806295399515
- name: Accuracy
type: accuracy
value: 0.9823540395867049
bert-finetuned-ner
This model is a fine-tuned version of bert-base-cased on the ncbi_disease dataset. It achieves the following results on the evaluation set:
- Loss: 0.0713
- Precision: 0.7792
- Recall: 0.8564
- F1: 0.8160
- Accuracy: 0.9824
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: 8
- eval_batch_size: 8
- 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 |
|---|---|---|---|---|---|---|---|
| 0.1175 | 1.0 | 680 | 0.0609 | 0.7252 | 0.8183 | 0.7690 | 0.9815 |
| 0.0437 | 2.0 | 1360 | 0.0603 | 0.7489 | 0.8450 | 0.7940 | 0.9821 |
| 0.016 | 3.0 | 2040 | 0.0713 | 0.7792 | 0.8564 | 0.8160 | 0.9824 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1