---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: named-entity-recognition-distilbert-B
  results: []
---

<!-- 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. -->

# named-entity-recognition-distilbert-B

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the Multinerd dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0350
- Precision: 0.9377
- Recall: 0.9442
- F1: 0.9409
- Accuracy: 0.9918

## 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: 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0164        | 1.0   | 8205  | 0.0256          | 0.9347    | 0.9385 | 0.9366 | 0.9913   |
| 0.0082        | 2.0   | 16410 | 0.0288          | 0.9381    | 0.9413 | 0.9397 | 0.9917   |
| 0.0044        | 3.0   | 24615 | 0.0350          | 0.9377    | 0.9442 | 0.9409 | 0.9918   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0

### Citation 
### Bibtex
```
@software{Ali_Raza,
    author = {Raza, Ali},
    license = { BSD-2-Clause license},
    title = {{Named Entity Recognition using Multinerd}},
    url = {https://github.com/raza4729/NER}
}
```