---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- conll2002
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: conll2002
      type: conll2002
      config: es
      split: validation
      args: es
    metrics:
    - name: Precision
      type: precision
      value: 0.6381977967570244
    - name: Recall
      type: recall
      value: 0.621055167429535
    - name: F1
      type: f1
      value: 0.6295097979366338
    - name: Accuracy
      type: accuracy
      value: 0.9309591653454259
---

<!-- 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 conll2002 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2431
- Precision: 0.6382
- Recall: 0.6211
- F1: 0.6295
- Accuracy: 0.9310

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3539        | 1.0   | 521  | 0.2735          | 0.5837    | 0.5829 | 0.5833 | 0.9218   |
| 0.207         | 2.0   | 1042 | 0.2431          | 0.6382    | 0.6211 | 0.6295 | 0.9310   |


### Framework versions

- Transformers 4.45.1
- Pytorch 2.4.0+cpu
- Datasets 3.0.1
- Tokenizers 0.20.0