---
library_name: transformers
license: apache-2.0
base_model: bert-large-cased
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-large-200-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: 0.7622442653440794
    - name: Recall
      type: recall
      value: 0.8276674520363514
    - name: F1
      type: f1
      value: 0.7936098111989672
    - name: Accuracy
      type: accuracy
      value: 0.968322884622873
---

<!-- 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. -->

# bert-large-200-ner

This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1527
- Precision: 0.7622
- Recall: 0.8277
- F1: 0.7936
- Accuracy: 0.9683

## 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 100  | 0.3317          | 0.4836    | 0.5545 | 0.5167 | 0.9237   |
| No log        | 2.0   | 200  | 0.1819          | 0.7122    | 0.7763 | 0.7429 | 0.9607   |
| No log        | 3.0   | 300  | 0.1527          | 0.7622    | 0.8277 | 0.7936 | 0.9683   |


### Framework versions

- Transformers 4.51.2
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1