---
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: ner_model_2
  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: 0.8894709271870089
    - name: Recall
      type: recall
      value: 0.9019121813031161
    - name: F1
      type: f1
      value: 0.8956483516483517
    - name: Accuracy
      type: accuracy
      value: 0.9791105846882739
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# ner_model_2

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: 0.1156
- Precision: 0.8895
- Recall: 0.9019
- F1: 0.8956
- Accuracy: 0.9791

## 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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.207         | 1.0   | 878  | 0.1029          | 0.8715    | 0.8862 | 0.8788 | 0.9756   |
| 0.0398        | 2.0   | 1756 | 0.1129          | 0.8753    | 0.9019 | 0.8884 | 0.9777   |
| 0.0223        | 3.0   | 2634 | 0.1156          | 0.8895    | 0.9019 | 0.8956 | 0.9791   |


### Framework versions

- Transformers 4.46.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3