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---
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_NER1800
  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_NER1800

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: 0.1528
- Precision: 0.8240
- Recall: 0.8614
- F1: 0.8422
- Accuracy: 0.9741

## 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.257         | 1.0   | 869  | 0.0952          | 0.7898    | 0.8157 | 0.8025 | 0.9694   |
| 0.0707        | 2.0   | 1738 | 0.1023          | 0.8197    | 0.8494 | 0.8343 | 0.9729   |
| 0.0412        | 3.0   | 2607 | 0.1078          | 0.8234    | 0.8569 | 0.8398 | 0.9739   |
| 0.0263        | 4.0   | 3476 | 0.1201          | 0.8178    | 0.8675 | 0.8419 | 0.9732   |
| 0.0143        | 5.0   | 4345 | 0.1208          | 0.8317    | 0.8572 | 0.8443 | 0.9748   |
| 0.0094        | 6.0   | 5214 | 0.1353          | 0.8212    | 0.8566 | 0.8385 | 0.9736   |
| 0.0059        | 7.0   | 6083 | 0.1476          | 0.8128    | 0.8644 | 0.8378 | 0.9732   |
| 0.0047        | 8.0   | 6952 | 0.1474          | 0.8208    | 0.8630 | 0.8414 | 0.9741   |
| 0.0032        | 9.0   | 7821 | 0.1572          | 0.8129    | 0.8550 | 0.8334 | 0.9728   |
| 0.0024        | 10.0  | 8690 | 0.1528          | 0.8240    | 0.8614 | 0.8422 | 0.9741   |


### Framework versions

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