---
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: 0.8117213736323259
    - name: Recall
      type: recall
      value: 0.8382369392549502
    - name: F1
      type: f1
      value: 0.8247660979636764
    - name: Accuracy
      type: accuracy
      value: 0.9613166632246175
---

<!-- 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: 0.1547
- Precision: 0.8117
- Recall: 0.8382
- F1: 0.8248
- Accuracy: 0.9613

## 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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.5069        | 1.0   | 878  | 0.2184          | 0.7396    | 0.7742 | 0.7565 | 0.9481   |
| 0.2068        | 2.0   | 1756 | 0.1667          | 0.8115    | 0.8201 | 0.8158 | 0.9593   |
| 0.166         | 3.0   | 2634 | 0.1547          | 0.8117    | 0.8382 | 0.8248 | 0.9613   |


### Framework versions

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