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distilbert-base-uncased-finetuned-ner

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distilbert-base-uncased-finetuned-ner
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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wnut_17
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: wnut_17
      type: wnut_17
      args: wnut_17
    metrics:
    - name: Precision
      type: precision
      value: 0.5899772209567198
    - name: Recall
      type: recall
      value: 0.4117647058823529
    - name: F1
      type: f1
      value: 0.4850187265917604
    - name: Accuracy
      type: accuracy
      value: 0.9304392705585502
---

<!-- 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 wnut_17 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3202
- Precision: 0.5900
- Recall: 0.4118
- F1: 0.4850
- Accuracy: 0.9304

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 213  | 0.3469          | 0.5480    | 0.2814 | 0.3718 | 0.9193   |
| No log        | 2.0   | 426  | 0.3135          | 0.5909    | 0.3903 | 0.4701 | 0.9281   |
| 0.1903        | 3.0   | 639  | 0.3202          | 0.5900    | 0.4118 | 0.4850 | 0.9304   |


### Framework versions

- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1