---
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: 0.9089708310401761
    - name: Recall
      type: recall
      value: 0.9238169817652981
    - name: F1
      type: f1
      value: 0.9163337771859743
    - name: Accuracy
      type: accuracy
      value: 0.980618615660794
---

<!-- 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: 0.0690
- Precision: 0.9090
- Recall: 0.9238
- F1: 0.9163
- Accuracy: 0.9806

## 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: 64
- eval_batch_size: 64
- 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   | 220  | 0.0951          | 0.8731    | 0.8890 | 0.8810 | 0.9740   |
| No log        | 2.0   | 440  | 0.0718          | 0.9029    | 0.9169 | 0.9099 | 0.9796   |
| 0.1848        | 3.0   | 660  | 0.0690          | 0.9090    | 0.9238 | 0.9163 | 0.9806   |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.1.2+cu118
- Datasets 2.20.0
- Tokenizers 0.15.1