---
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: lora-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: 0.8535113174695299
    - name: Recall
      type: recall
      value: 0.8726560645620698
    - name: F1
      type: f1
      value: 0.8629775247931459
    - name: Accuracy
      type: accuracy
      value: 0.9730680240629338
---

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

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0920
- Precision: 0.8535
- Recall: 0.8727
- F1: 0.8630
- Accuracy: 0.9731

## 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: 0.001
- train_batch_size: 64
- eval_batch_size: 64
- 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 220  | 0.1085          | 0.8026    | 0.8314 | 0.8167 | 0.9675   |
| No log        | 2.0   | 440  | 0.0804          | 0.8693    | 0.8818 | 0.8755 | 0.9759   |
| 0.2014        | 3.0   | 660  | 0.0720          | 0.8764    | 0.8970 | 0.8866 | 0.9783   |
| 0.2014        | 4.0   | 880  | 0.0688          | 0.8773    | 0.9056 | 0.8912 | 0.9792   |
| 0.0882        | 5.0   | 1100 | 0.0674          | 0.8823    | 0.9067 | 0.8943 | 0.9796   |


### Framework versions

- Transformers 4.46.3
- Pytorch 2.4.0
- Datasets 3.1.0
- Tokenizers 0.20.3