---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-cased-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: 0.9267369114257491
    - name: Recall
      type: recall
      value: 0.9473241332884551
    - name: F1
      type: f1
      value: 0.9369174434087884
    - name: Accuracy
      type: accuracy
      value: 0.9852239948195679
---

<!-- 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-cased-ner

This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1060
- Precision: 0.9267
- Recall: 0.9473
- F1: 0.9369
- Accuracy: 0.9852

## 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: 8
- eval_batch_size: 8
- seed: 2147483647
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1012        | 1.0   | 1756  | 0.0895          | 0.8924    | 0.9194 | 0.9057 | 0.9767   |
| 0.0491        | 2.0   | 3512  | 0.0818          | 0.9070    | 0.9260 | 0.9164 | 0.9801   |
| 0.0334        | 3.0   | 5268  | 0.0818          | 0.9170    | 0.9315 | 0.9242 | 0.9821   |
| 0.0235        | 4.0   | 7024  | 0.0893          | 0.9074    | 0.9364 | 0.9216 | 0.9815   |
| 0.0167        | 5.0   | 8780  | 0.0879          | 0.9106    | 0.9414 | 0.9258 | 0.9828   |
| 0.0071        | 6.0   | 10536 | 0.0955          | 0.9172    | 0.9435 | 0.9301 | 0.9836   |
| 0.0039        | 7.0   | 12292 | 0.1016          | 0.9209    | 0.9423 | 0.9315 | 0.9835   |
| 0.0021        | 8.0   | 14048 | 0.1043          | 0.9294    | 0.9463 | 0.9378 | 0.9847   |
| 0.0014        | 9.0   | 15804 | 0.1064          | 0.9271    | 0.9475 | 0.9372 | 0.9853   |
| 0.0005        | 10.0  | 17560 | 0.1060          | 0.9267    | 0.9473 | 0.9369 | 0.9852   |


### Framework versions

- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3