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entity-extraction

Token Classification
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entity-extraction
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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: entity-extraction
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: conll2003
      type: conll2003
      args: conll2003
    metrics:
    - name: Precision
      type: precision
      value: 0.8862817854414493
    - name: Recall
      type: recall
      value: 0.9084908826490659
    - name: F1
      type: f1
      value: 0.8972489227709645
    - name: Accuracy
      type: accuracy
      value: 0.9774889986814304
  - task:
      type: token-classification
      name: entity_extraction
    dataset:
      type: conll2003
      name: conll2003
      config: conll2003
      split: test
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9703231821006837
      verified: true
    - name: Precision
      type: precision
      value: 0.9758137392136365
      verified: true
    - name: Recall
      type: recall
      value: 0.9764192759122017
      verified: true
    - name: F1 Score
      type: f1
      value: 0.9761164136513085
      verified: true
  - metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9703231821006837
      verified: true
    - name: Precision
      type: precision
      value: 0.9758137392136365
      verified: true
    - name: Recall
      type: recall
      value: 0.9764192759122017
      verified: true
    - name: F1
      type: f1
      value: 0.9761164136513085
      verified: true
    task:
      type: token-classification
      name: Token Classification
    dataset:
      name: conll2003
      type: conll2003
      config: conll2003
      split: test
---

<!-- 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. -->

# entity-extraction

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0808
- Precision: 0.8863
- Recall: 0.9085
- F1: 0.8972
- Accuracy: 0.9775

## 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: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2552        | 1.0   | 878  | 0.0808          | 0.8863    | 0.9085 | 0.8972 | 0.9775   |


### Framework versions

- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1