---
license: apache-2.0
base_model: distilbert-base-uncased
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: 0.9269461077844311
    - name: Recall
      type: recall
      value: 0.9381818181818182
    - name: F1
      type: f1
      value: 0.9325301204819277
    - name: Accuracy
      type: accuracy
      value: 0.9986404599129894
---

<!-- 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 [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0067
- Precision: 0.9269
- Recall: 0.9382
- F1: 0.9325
- Accuracy: 0.9986

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 63   | 0.0500          | 0.8048    | 0.4097 | 0.5430 | 0.9883   |
| No log        | 2.0   | 126  | 0.0305          | 0.8104    | 0.7564 | 0.7824 | 0.9936   |
| No log        | 3.0   | 189  | 0.0136          | 0.8643    | 0.8412 | 0.8526 | 0.9965   |
| No log        | 4.0   | 252  | 0.0089          | 0.8571    | 0.9164 | 0.8858 | 0.9976   |
| No log        | 5.0   | 315  | 0.0067          | 0.9269    | 0.9382 | 0.9325 | 0.9986   |


### Framework versions

- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3