---
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-tiny-finetuned-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: conll2003
      type: conll2003
      args: conll2003
    metrics:
    - name: Precision
      type: precision
      value: 0.5147295742232451
    - name: Recall
      type: recall
      value: 0.5003915426781519
    - name: F1
      type: f1
      value: 0.5074593000170173
    - name: Accuracy
      type: accuracy
      value: 0.8967226396810015
---

<!-- 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-tiny-finetuned-ner

This model is a fine-tuned version of [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4053
- Precision: 0.5147
- Recall: 0.5004
- F1: 0.5075
- Accuracy: 0.8967

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.9306        | 1.0   | 878  | 0.5040          | 0.4321    | 0.4099 | 0.4207 | 0.8762   |
| 0.4777        | 2.0   | 1756 | 0.4240          | 0.4978    | 0.4851 | 0.4913 | 0.8926   |
| 0.4306        | 3.0   | 2634 | 0.4053          | 0.5147    | 0.5004 | 0.5075 | 0.8967   |


### Framework versions

- Transformers 4.10.0
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3