---
license: apache-2.0
base_model: bert-base-multilingual-cased
tags:
- generated_from_trainer
datasets:
- ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: my_awesome_wnut_model
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: ner
      type: ner
      config: indian_names
      split: train
      args: indian_names
    metrics:
    - name: Precision
      type: precision
      value: 0.9950245302230862
    - name: Recall
      type: recall
      value: 0.9949324324324325
    - name: F1
      type: f1
      value: 0.9949784791965567
    - name: Accuracy
      type: accuracy
      value: 0.9976269686240996
---

<!-- 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. -->

# my_awesome_wnut_model

This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0019
- Precision: 0.9950
- Recall: 0.9949
- F1: 0.9950
- Accuracy: 0.9976

## 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: 5e-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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0679        | 1.0   | 688  | 0.0131          | 0.9998    | 0.9826 | 0.9911 | 0.9920   |
| 0.0331        | 2.0   | 1376 | 0.0085          | 0.9998    | 0.9826 | 0.9911 | 0.9921   |
| 0.0248        | 3.0   | 2064 | 0.0057          | 0.9999    | 0.9826 | 0.9912 | 0.9921   |
| 0.0155        | 4.0   | 2752 | 0.0026          | 0.9948    | 0.9936 | 0.9942 | 0.9969   |
| 0.0103        | 5.0   | 3440 | 0.0019          | 0.9950    | 0.9949 | 0.9950 | 0.9976   |


### Framework versions

- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3