---
license: apache-2.0
base_model: dslim/distilbert-NER
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-NER-conll2003
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: conll2003
      type: conll2003
      config: conll2003
      split: test
      args: conll2003
    metrics:
    - name: Precision
      type: precision
      value: 0.8735552872175263
    - name: Recall
      type: recall
      value: 0.896600566572238
    - name: F1
      type: f1
      value: 0.8849279161205766
    - name: Accuracy
      type: accuracy
      value: 0.9755572305373102
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# distilbert-NER-conll2003

This model is a fine-tuned version of [dslim/distilbert-NER](https://huggingface.co/dslim/distilbert-NER) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1932
- Precision: 0.8736
- Recall: 0.8966
- F1: 0.8849
- Accuracy: 0.9756

## 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: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0567        | 1.0   | 3922 | 0.1809          | 0.8698    | 0.8904 | 0.8800 | 0.9750   |
| 0.0245        | 2.0   | 7844 | 0.1932          | 0.8736    | 0.8966 | 0.8849 | 0.9756   |


### Framework versions

- Transformers 4.33.2
- Pytorch 2.2.2
- Datasets 2.20.0
- Tokenizers 0.13.3