metadata
library_name: transformers
license: mit
base_model: emilyalsentzer/Bio_ClinicalBERT
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: 0.7952941176470588
- name: Recall
type: recall
value: 0.8589580686149937
- name: F1
type: f1
value: 0.8259010384850336
- name: Accuracy
type: accuracy
value: 0.9841210883090352
bert-finetuned-ner
This model is a fine-tuned version of emilyalsentzer/Bio_ClinicalBERT on the ncbi_disease dataset. It achieves the following results on the evaluation set:
- Loss: 0.0623
- Precision: 0.7953
- Recall: 0.8590
- F1: 0.8259
- Accuracy: 0.9841
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 |
|---|---|---|---|---|---|---|---|
| 0.1204 | 1.0 | 680 | 0.0536 | 0.7417 | 0.8247 | 0.7810 | 0.9824 |
| 0.0386 | 2.0 | 1360 | 0.0542 | 0.7808 | 0.8463 | 0.8122 | 0.9831 |
| 0.0144 | 3.0 | 2040 | 0.0623 | 0.7953 | 0.8590 | 0.8259 | 0.9841 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1