---
license: apache-2.0
base_model: distilbert-base-uncased
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: 0.8794544654641443
    - name: Recall
      type: recall
      value: 0.8945072155722117
    - name: F1
      type: f1
      value: 0.8869169763185625
    - name: Accuracy
      type: accuracy
      value: 0.9731996759178356
---

<!-- 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: 0.1902
- Precision: 0.8795
- Recall: 0.8945
- F1: 0.8869
- Accuracy: 0.9732

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 439  | 0.1942          | 0.8818    | 0.8931 | 0.8874 | 0.9732   |
| 0.0009        | 2.0   | 878  | 0.1902          | 0.8817    | 0.8933 | 0.8875 | 0.9729   |
| 0.001         | 3.0   | 1317 | 0.1894          | 0.8794    | 0.8952 | 0.8872 | 0.9733   |
| 0.0009        | 4.0   | 1756 | 0.1902          | 0.8795    | 0.8945 | 0.8869 | 0.9732   |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2