---
license: apache-2.0
tags:
- generated_from_trainer
base_model: bert-base-uncased
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: NER_Pittsburgh_TAA
  results:
  - task:
      type: token-classification
      name: Token Classification
    dataset:
      name: conll2003
      type: conll2003
      config: conll2003
      split: validation
      args: conll2003
    metrics:
    - type: precision
      value: 0.9343718926085516
      name: Precision
    - type: recall
      value: 0.9460789797516501
      name: Recall
    - type: f1
      value: 0.940188993885492
      name: F1
    - type: accuracy
      value: 0.9858293484995313
      name: Accuracy
---

<!-- 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. -->

# NER_Pittsburgh_TAA

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: 0.0599
- Precision: 0.9344
- Recall: 0.9461
- F1: 0.9402
- Accuracy: 0.9858

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 439  | 0.0604          | 0.9175    | 0.9290 | 0.9232 | 0.9829   |
| 0.0953        | 2.0   | 878  | 0.0545          | 0.9312    | 0.9412 | 0.9361 | 0.9850   |
| 0.0409        | 3.0   | 1317 | 0.0571          | 0.9357    | 0.9412 | 0.9384 | 0.9855   |
| 0.0234        | 4.0   | 1756 | 0.0593          | 0.9343    | 0.9482 | 0.9412 | 0.9858   |
| 0.0159        | 5.0   | 2195 | 0.0599          | 0.9344    | 0.9461 | 0.9402 | 0.9858   |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1