---
license: apache-2.0
base_model: google-bert/bert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: BERT_BIOMAT_NER__ST_1000_DA
  results: []
---

<!-- 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_BIOMAT_NER__ST_1000_DA

This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4791
- Precision: 0.4732
- Recall: 0.6716
- F1: 0.5552
- Accuracy: 0.9364

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 397  | 0.2764          | 0.4422    | 0.5997 | 0.5090 | 0.9323   |
| 0.2077        | 2.0   | 794  | 0.3185          | 0.4682    | 0.6485 | 0.5438 | 0.9356   |
| 0.0466        | 3.0   | 1191 | 0.3461          | 0.4699    | 0.6592 | 0.5487 | 0.9362   |
| 0.0179        | 4.0   | 1588 | 0.3994          | 0.4567    | 0.6595 | 0.5397 | 0.9342   |
| 0.0179        | 5.0   | 1985 | 0.4091          | 0.4735    | 0.6733 | 0.5560 | 0.9369   |
| 0.0088        | 6.0   | 2382 | 0.4392          | 0.4701    | 0.6630 | 0.5501 | 0.9366   |
| 0.0048        | 7.0   | 2779 | 0.4594          | 0.4654    | 0.6644 | 0.5473 | 0.9356   |
| 0.0032        | 8.0   | 3176 | 0.4684          | 0.4740    | 0.6775 | 0.5578 | 0.9369   |
| 0.0024        | 9.0   | 3573 | 0.4763          | 0.4703    | 0.6623 | 0.5500 | 0.9359   |
| 0.0024        | 10.0  | 3970 | 0.4791          | 0.4732    | 0.6716 | 0.5552 | 0.9364   |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1