---
pipeline_tag: token-classification
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- few_nerd
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: few_nerd
      type: few_nerd
      args: supervised
    metrics:
    - name: Precision
      type: precision
      value: 0.6424480067658478
    - name: Recall
      type: recall
      value: 0.6854236732015421
    - name: F1
      type: f1
      value: 0.6632404008334158
    - name: Accuracy
      type: accuracy
      value: 0.9075199647113962
---

<!-- 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. -->

# distilbert-base-uncased-finetuned-ner

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: 0.3136
- Precision: 0.6424
- Recall: 0.6854
- F1: 0.6632
- Accuracy: 0.9075

## 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: 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: 3

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.328         | 1.0   | 8236  | 0.3197          | 0.6274    | 0.6720 | 0.6489 | 0.9041   |
| 0.2776        | 2.0   | 16472 | 0.3111          | 0.6433    | 0.6759 | 0.6592 | 0.9069   |
| 0.241         | 3.0   | 24708 | 0.3136          | 0.6424    | 0.6854 | 0.6632 | 0.9075   |


### Framework versions

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