---
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: 0.7348668280871671
    - name: Recall
      type: recall
      value: 0.7311491206938088
    - name: F1
      type: f1
      value: 0.733003260475788
    - name: Accuracy
      type: accuracy
      value: 0.94996285742796
---

<!-- 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: 0.2347
- Precision: 0.7349
- Recall: 0.7311
- F1: 0.7330
- Accuracy: 0.9500

## 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3477        | 1.0   | 521  | 0.2581          | 0.6392    | 0.5888 | 0.6130 | 0.9270   |
| 0.1883        | 2.0   | 1042 | 0.2224          | 0.6617    | 0.6644 | 0.6631 | 0.9370   |
| 0.1339        | 3.0   | 1563 | 0.2079          | 0.7044    | 0.7021 | 0.7033 | 0.9431   |
| 0.1039        | 4.0   | 2084 | 0.2040          | 0.7017    | 0.7221 | 0.7118 | 0.9446   |
| 0.0835        | 5.0   | 2605 | 0.2126          | 0.7306    | 0.7166 | 0.7235 | 0.9486   |
| 0.0647        | 6.0   | 3126 | 0.2221          | 0.7220    | 0.7198 | 0.7209 | 0.9478   |
| 0.0536        | 7.0   | 3647 | 0.2258          | 0.7198    | 0.7244 | 0.7221 | 0.9480   |
| 0.0443        | 8.0   | 4168 | 0.2319          | 0.7047    | 0.7334 | 0.7188 | 0.9469   |
| 0.0375        | 9.0   | 4689 | 0.2350          | 0.7182    | 0.7315 | 0.7248 | 0.9482   |
| 0.0349        | 10.0  | 5210 | 0.2347          | 0.7349    | 0.7311 | 0.7330 | 0.9500   |


### Framework versions

- Transformers 4.43.3
- Pytorch 2.4.0
- Datasets 2.20.0
- Tokenizers 0.19.1