---
library_name: transformers
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: base-NER
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: conll2003
      type: conll2003
      config: conll2003
      split: test
      args: conll2003
    metrics:
    - name: Precision
      type: precision
      value: 0.8845085098992705
    - name: Recall
      type: recall
      value: 0.9017351274787535
    - name: F1
      type: f1
      value: 0.8930387515342801
    - name: Accuracy
      type: accuracy
      value: 0.9782491655001615
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# base-NER

This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1129
- Precision: 0.8845
- Recall: 0.9017
- F1: 0.8930
- Accuracy: 0.9782

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0595        | 1.0   | 878  | 0.1046          | 0.8676    | 0.8909 | 0.8791 | 0.9762   |
| 0.0319        | 2.0   | 1756 | 0.1129          | 0.8845    | 0.9017 | 0.8930 | 0.9782   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1