---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-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: 0.9139204232337705
    - name: Recall
      type: recall
      value: 0.9276205392102025
    - name: F1
      type: f1
      value: 0.9207195203197868
    - name: Accuracy
      type: accuracy
      value: 0.9817306623032075
---

<!-- 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. -->

# distilbert-base-uncased-finetuned-ner

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0662
- Precision: 0.9139
- Recall: 0.9276
- F1: 0.9207
- Accuracy: 0.9817

## 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: 48
- eval_batch_size: 48
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 293  | 0.0861          | 0.8786    | 0.9014 | 0.8899 | 0.9765   |
| 0.1963        | 2.0   | 586  | 0.0682          | 0.9031    | 0.9218 | 0.9124 | 0.9805   |
| 0.1963        | 3.0   | 879  | 0.0662          | 0.9139    | 0.9276 | 0.9207 | 0.9817   |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.12.0
- Datasets 2.10.1
- Tokenizers 0.11.0