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---
library_name: transformers
base_model: huawei-noah/TinyBERT_General_4L_312D
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: TinyBERT-finetuned-NER
  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: 0.8465303458777463
    - name: Recall
      type: recall
      value: 0.870679046873252
    - name: F1
      type: f1
      value: 0.8584348977003253
    - name: Accuracy
      type: accuracy
      value: 0.9670516466233497
---

<!-- 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. -->

# TinyBERT-finetuned-NER

This model is a fine-tuned version of [huawei-noah/TinyBERT_General_4L_312D](https://huggingface.co/huawei-noah/TinyBERT_General_4L_312D) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1232
- Precision: 0.8465
- Recall: 0.8707
- F1: 0.8584
- Accuracy: 0.9671

## 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.5173        | 1.0   | 878  | 0.2116          | 0.7429    | 0.7756 | 0.7589 | 0.9493   |
| 0.196         | 2.0   | 1756 | 0.1528          | 0.8262    | 0.8383 | 0.8323 | 0.9620   |
| 0.1444        | 3.0   | 2634 | 0.1355          | 0.8447    | 0.8606 | 0.8526 | 0.9652   |
| 0.116         | 4.0   | 3512 | 0.1255          | 0.8452    | 0.8660 | 0.8555 | 0.9663   |
| 0.1116        | 5.0   | 4390 | 0.1232          | 0.8465    | 0.8707 | 0.8584 | 0.9671   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.19.1