---
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: 0.9998842940781709
    - name: Recall
      type: recall
      value: 0.9998380192062941
    - name: F1
      type: f1
      value: 0.9998611561068173
    - name: Accuracy
      type: accuracy
      value: 0.999938944347773
---

<!-- 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: 0.0001
- Precision: 0.9999
- Recall: 0.9998
- F1: 0.9999
- Accuracy: 0.9999

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0342        | 1.0   | 688  | 0.0063          | 0.9950    | 0.9917 | 0.9934 | 0.9956   |
| 0.0117        | 2.0   | 1376 | 0.0015          | 0.9979    | 0.9974 | 0.9977 | 0.9988   |
| 0.0049        | 3.0   | 2064 | 0.0006          | 0.9991    | 0.9994 | 0.9992 | 0.9995   |
| 0.0017        | 4.0   | 2752 | 0.0001          | 0.9997    | 0.9997 | 0.9997 | 0.9999   |
| 0.001         | 5.0   | 3440 | 0.0001          | 0.9999    | 0.9998 | 0.9999 | 0.9999   |


### Framework versions

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