---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- dstefa/New_York_Times_Topics
metrics:
- accuracy
model-index:
- name: DistilBERT base classify news topics - Devinit
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: New York Times Topics
      type: dstefa/New_York_Times_Topics
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.913482481060606
widget:
  - text: "Insurers: Costs Would Skyrocket Under House Health Bill."
---


# DistilBERT base classify news topics - Devinit

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the New York Times Topics dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2871
- Accuracy: 0.9135

## 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: 1e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.386         | 1.0   | 1340  | 0.3275          | 0.8921   |
| 0.2833        | 2.0   | 2680  | 0.2840          | 0.9033   |
| 0.2411        | 3.0   | 4020  | 0.2694          | 0.9102   |
| 0.2069        | 4.0   | 5360  | 0.2665          | 0.9114   |
| 0.1796        | 5.0   | 6700  | 0.2657          | 0.9128   |
| 0.1636        | 6.0   | 8040  | 0.2674          | 0.9142   |
| 0.144         | 7.0   | 9380  | 0.2761          | 0.9129   |
| 0.1277        | 8.0   | 10720 | 0.2820          | 0.9125   |
| 0.1201        | 9.0   | 12060 | 0.2853          | 0.9136   |
| 0.1104        | 10.0  | 13400 | 0.2871          | 0.9135   |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0