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---
library_name: transformers
license: apache-2.0
base_model: google-bert/bert-base-uncased
tags:
- generated_from_trainer
datasets:
- biobert_json
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-uncased-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: biobert_json
type: biobert_json
config: Biobert_json
split: validation
args: Biobert_json
metrics:
- name: Precision
type: precision
value: 0.9437070282658518
- name: Recall
type: recall
value: 0.9691575953711876
- name: F1
type: f1
value: 0.9562630025642267
- name: Accuracy
type: accuracy
value: 0.977555086732302
---
<!-- 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-base-uncased-finetuned-ner
This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on the biobert_json dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1107
- Precision: 0.9437
- Recall: 0.9692
- F1: 0.9563
- Accuracy: 0.9776
## 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: 16
- eval_batch_size: 16
- 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.4381 | 1.0 | 612 | 0.1172 | 0.9235 | 0.9536 | 0.9383 | 0.9689 |
| 0.1389 | 2.0 | 1224 | 0.1117 | 0.9247 | 0.9731 | 0.9483 | 0.9717 |
| 0.0935 | 3.0 | 1836 | 0.0962 | 0.9433 | 0.9662 | 0.9546 | 0.9769 |
| 0.0758 | 4.0 | 2448 | 0.0926 | 0.9408 | 0.9736 | 0.9569 | 0.9771 |
| 0.0536 | 5.0 | 3060 | 0.0958 | 0.9404 | 0.9722 | 0.9561 | 0.9769 |
| 0.0476 | 6.0 | 3672 | 0.1029 | 0.9418 | 0.9681 | 0.9548 | 0.9761 |
| 0.0395 | 7.0 | 4284 | 0.1023 | 0.9425 | 0.9720 | 0.9570 | 0.9769 |
| 0.0375 | 8.0 | 4896 | 0.1091 | 0.9426 | 0.9695 | 0.9559 | 0.9771 |
| 0.0299 | 9.0 | 5508 | 0.1080 | 0.9451 | 0.9693 | 0.9570 | 0.9778 |
| 0.0266 | 10.0 | 6120 | 0.1107 | 0.9437 | 0.9692 | 0.9563 | 0.9776 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
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