---
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-FewNerd
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: few_nerd
      type: few_nerd
      args: supervised
    metrics:
    - name: Precision
      type: precision
      value: 0.7422259388187705
    - name: Recall
      type: recall
      value: 0.7830368683449253
    - name: F1
      type: f1
      value: 0.7620854216169805
    - name: Accuracy
      type: accuracy
      value: 0.9386106950200795
---

<!-- 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-FewNerd

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: 0.2091
- Precision: 0.7422
- Recall: 0.7830
- F1: 0.7621
- Accuracy: 0.9386

## 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: 32
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1964        | 1.0   | 4118  | 0.1946          | 0.7302    | 0.7761 | 0.7525 | 0.9366   |
| 0.1685        | 2.0   | 8236  | 0.1907          | 0.7414    | 0.7776 | 0.7591 | 0.9384   |
| 0.145         | 3.0   | 12354 | 0.1967          | 0.7454    | 0.7816 | 0.7631 | 0.9388   |
| 0.1263        | 4.0   | 16472 | 0.2021          | 0.7402    | 0.7845 | 0.7617 | 0.9384   |
| 0.1114        | 5.0   | 20590 | 0.2091          | 0.7422    | 0.7830 | 0.7621 | 0.9386   |


### Framework versions

- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.1.0
- Tokenizers 0.12.1