---
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-20-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: 0.26426174496644295
    - name: Recall
      type: recall
      value: 0.05301245371928644
    - name: F1
      type: f1
      value: 0.08830950378469302
    - name: Accuracy
      type: accuracy
      value: 0.8491102371402983
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# bert-large-20-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: 0.5525
- Precision: 0.2643
- Recall: 0.0530
- F1: 0.0883
- Accuracy: 0.8491

## 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   | 25   | 0.7702          | 0.2857    | 0.0003 | 0.0007 | 0.8326   |
| No log        | 2.0   | 50   | 0.6046          | 0.1537    | 0.0106 | 0.0198 | 0.8373   |
| No log        | 3.0   | 75   | 0.5525          | 0.2643    | 0.0530 | 0.0883 | 0.8491   |


### Framework versions

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