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---
library_name: transformers
license: mit
base_model: microsoft/biogpt
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: 0.08217270194986072
- name: Recall
type: recall
value: 0.07496823379923762
- name: F1
type: f1
value: 0.07840531561461794
- name: Accuracy
type: accuracy
value: 0.9369870473375083
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [microsoft/biogpt](https://huggingface.co/microsoft/biogpt) on the ncbi_disease dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2151
- Precision: 0.0822
- Recall: 0.0750
- F1: 0.0784
- Accuracy: 0.9370
## 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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3388 | 1.0 | 679 | 0.2280 | 0.0292 | 0.0254 | 0.0272 | 0.9312 |
| 0.2425 | 2.0 | 1358 | 0.2161 | 0.0612 | 0.0572 | 0.0591 | 0.9345 |
| 0.1811 | 3.0 | 2037 | 0.2151 | 0.0822 | 0.0750 | 0.0784 | 0.9370 |
### Framework versions
- Transformers 4.51.2
- Pytorch 2.1.0+cu121
- Datasets 3.5.0
- Tokenizers 0.21.1
|