---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-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: 0.9341149273447821
    - name: Recall
      type: recall
      value: 0.9520363513968361
    - name: F1
      type: f1
      value: 0.9429904984164028
    - name: Accuracy
      type: accuracy
      value: 0.9866515570730559
---

<!-- 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-finetuned-ner

This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0741
- Precision: 0.9341
- Recall: 0.9520
- F1: 0.9430
- Accuracy: 0.9867

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0775        | 1.0   | 1756 | 0.0694          | 0.8912    | 0.9273 | 0.9089 | 0.9817   |
| 0.0377        | 2.0   | 3512 | 0.0707          | 0.9245    | 0.9445 | 0.9344 | 0.9850   |
| 0.0243        | 3.0   | 5268 | 0.0671          | 0.9281    | 0.9465 | 0.9372 | 0.9855   |
| 0.0145        | 4.0   | 7024 | 0.0734          | 0.9353    | 0.9507 | 0.9429 | 0.9859   |
| 0.006         | 5.0   | 8780 | 0.0741          | 0.9341    | 0.9520 | 0.9430 | 0.9867   |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1

### How to use and it's democase

from transformers import pipeline

model_checkpoint = "amannagrawall002/bert-finetuned-ner"
token_classifier = pipeline(
    "token-classification", model=model_checkpoint, aggregation_strategy="simple"
)

print(token_classifier("My name is Sylvain and I work at Hugging Face in Brooklyn."))
# [{'entity_group': 'PER', 'score': 0.9997023, 'word': 'Sylvain', 'start': 11, 'end': 18}, {'entity_group': 'ORG', 'score': 0.995275, 'word': 'Hugging Face', 'start': 33, 'end': 45}, {'entity_group': 'LOC', 'score': 0.9987465, 'word': 'Brooklyn', 'start': 49, 'end': 57}]