---
license: afl-3.0
datasets:
- ag_news
language:
- en
metrics:
- accuracy
library_name: transformers
pipeline_tag: text-classification
---

## Model description
This model is a fine-tuned version of the [bert-base-uncased](https://huggingface.co/transformers/model_doc/bert.html) model to classify news articles into one of four
categories: World(label 0), Sports(label 1), Business(label 2), Sci/Tech(label 3).

## How to use

You can use the model with the following code.

```python
from transformers import BertForSequenceClassification, BertTokenizer, TextClassificationPipeline
model_path = "JiaqiLee/bert-agnews"
tokenizer = BertTokenizer.from_pretrained(model_path)
model = BertForSequenceClassification.from_pretrained(model_path, num_labels=4)
pipeline = TextClassificationPipeline(model=model, tokenizer=tokenizer)
print(pipeline("Google scores first-day bump of 18 (USATODAY.com): USATODAY.com - Even a big first-day jump in shares of Google (GOOG) couldn't quiet debate over whether the Internet search engine's contentious auction was a hit or a flop."))
```

## Training data
The training data comes from HuggingFace [AGNews dataset](https://huggingface.co/datasets/ag_news). We use 90% of the `train.csv` data to train the model and the remaining 10% for evaluation.

## Evaluation results

The model achieves 0.9447 classification accuracy in AGNews test dataset.