---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- few_nerd
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Cybonto-distilbert-base-uncased-finetuned-ner-v0.1
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: few_nerd
      type: few_nerd
      args: supervised
    metrics:
    - name: Precision
      type: precision
      value: 0.7377633209417596
    - name: Recall
      type: recall
      value: 0.7817648386368765
    - name: F1
      type: f1
      value: 0.7591269959856158
    - name: Accuracy
      type: accuracy
      value: 0.9383331648547562
---

<!-- 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. -->

# Cybonto-distilbert-base-uncased-finetuned-ner-v0.1

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the few_nerd dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1930
- Precision: 0.7378
- Recall: 0.7818
- F1: 0.7591
- Accuracy: 0.9383

## 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: 36
- eval_batch_size: 36
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2001        | 1.0   | 3661  | 0.1954          | 0.7244    | 0.7750 | 0.7488 | 0.9360   |
| 0.1717        | 2.0   | 7322  | 0.1898          | 0.7392    | 0.7767 | 0.7575 | 0.9384   |
| 0.1485        | 3.0   | 10983 | 0.1930          | 0.7378    | 0.7818 | 0.7591 | 0.9383   |


### Framework versions

- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6