---
license: apache-2.0
base_model: distilbert-base-cased
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilBERT-infoExtract
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: conll2003
      type: conll2003
      config: conll2003
      split: validation
      args: conll2003
    metrics:
    - name: Precision
      type: precision
      value: 0.9133716160787531
    - name: Recall
      type: recall
      value: 0.9368899360484685
    - name: F1
      type: f1
      value: 0.9249813076347928
    - name: Accuracy
      type: accuracy
      value: 0.9832077471007241
language:
- en
---

<!-- 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-infoExtract

This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0718
- Precision: 0.9134
- Recall: 0.9369
- F1: 0.9250
- Accuracy: 0.9832

## Model description

The model can identify human name, organization and location so far (no time recognition). It was trained for 5 minutes with T4 GPU on Colab.

## 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: 8
- eval_batch_size: 8
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0954        | 1.0   | 1756 | 0.0846          | 0.8880    | 0.9194 | 0.9034 | 0.9769   |
| 0.0498        | 2.0   | 3512 | 0.0699          | 0.9057    | 0.9310 | 0.9182 | 0.9815   |
| 0.031         | 3.0   | 5268 | 0.0718          | 0.9134    | 0.9369 | 0.9250 | 0.9832   |


### Framework versions

- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1