---
library_name: transformers
license: apache-2.0
base_model: albert-base-v2
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: albert-finetuned-ner-gbgb
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: conll2003
      type: conll2003
      config: conll2003
      split: validation
      args: conll2003
    metrics:
    - name: Precision
      type: precision
      value: 0.5032151387102701
    - name: Recall
      type: recall
      value: 0.46095590710198586
    - name: F1
      type: f1
      value: 0.4811594202898551
    - name: Accuracy
      type: accuracy
      value: 0.8898127980220168
---

<!-- 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. -->

# albert-finetuned-ner-gbgb

This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3371
- Precision: 0.5032
- Recall: 0.4610
- F1: 0.4812
- Accuracy: 0.8898

## 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 OptimizerNames.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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.5379        | 1.0   | 1756 | 0.4843          | 0.4079    | 0.2740 | 0.3278 | 0.8502   |
| 0.3491        | 2.0   | 3512 | 0.3726          | 0.4903    | 0.3837 | 0.4305 | 0.8778   |
| 0.26          | 3.0   | 5268 | 0.3371          | 0.5032    | 0.4610 | 0.4812 | 0.8898   |


### Framework versions

- Transformers 4.46.1
- Pytorch 2.5.1+cpu
- Datasets 3.1.0
- Tokenizers 0.20.2