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finetuned-ner-conll

Token Classification
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finetuned-ner-conll
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---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: finetuned-ner-conll
  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: 0.9285243741765481
    - name: Recall
      type: recall
      value: 0.9488387748232918
    - name: F1
      type: f1
      value: 0.9385716663892125
    - name: Accuracy
      type: accuracy
      value: 0.9862247601106728
pipeline_tag: token-classification

widget:
- text: "Saketh Lives in India"
  example_title: "Classification"
- text: "Apollo hospitals is in India"
  example_title: "Classification"
- text: "Saketh works for Apollo"
  example_title: "Classification"
---

<!-- 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. -->

# finetuned-ner-conll

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: nan
- Precision: 0.9285
- Recall: 0.9488
- F1: 0.9386
- Accuracy: 0.9862

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.218         | 1.0   | 878  | nan             | 0.9080    | 0.9367 | 0.9221 | 0.9827   |
| 0.0449        | 2.0   | 1756 | nan             | 0.9277    | 0.9485 | 0.9380 | 0.9857   |
| 0.0232        | 3.0   | 2634 | nan             | 0.9285    | 0.9488 | 0.9386 | 0.9862   |


### Framework versions

- Transformers 4.37.0
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.1