---
license: mit
tags:
- setfit
- endpoints-template
- text-classification
inference: false
---

# SetFit AG News 

This is a [SetFit](https://github.com/huggingface/setfit/tree/main) classifier fine-tuned on the [AG News](https://huggingface.co/datasets/ag_news) dataset. 
The model was created following the [Outperform OpenAI GPT-3 with SetFit for text-classifiation](https://www.philschmid.de/getting-started-setfit) blog post of [Philipp Schmid](https://www.linkedin.com/in/philipp-schmid-a6a2bb196/).

The model achieves an accuracy of 0.87 on the test set and was only trained with `32` total examples (8 per class).


```bash
***** Running evaluation *****
model used: sentence-transformers/all-mpnet-base-v2
train dataset: 32 samples
accuracy: 0.8731578947368421
```

#### What is SetFit?

"SetFit" (https://arxiv.org/abs/2209.11055) is a new approach that can be used to create high accuracte text-classification models with limited labeled data. SetFit is outperforming GPT-3 in 7 out of 11 tasks, while being 1600x smaller.
Check out the blog to learn more: [Outperform OpenAI GPT-3 with SetFit for text-classifiation](https://www.philschmid.de/getting-started-setfit)



# Inference Endpoints 

The model repository also implements a generic custom `handler.py` as an example for how to use `SetFit` models with [inference-endpoints](https://hf.co/inference-endpoints). 

Code: https://huggingface.co/philschmid/setfit-ag-news-endpoint/blob/main/handler.py

## Send requests with Pyton

We are going to use requests to send our requests. (make your you have it installed `pip install requests`)


```python
import json
import requests as r

ENDPOINT_URL=""# url of your endpoint
HF_TOKEN=""

# payload samples
regular_payload = { "inputs": "Coming to The Rescue Got a unique problem? Not to worry: you can find a financial planner for every specialized need"}

# HTTP headers for authorization
headers= {
    "Authorization": f"Bearer {HF_TOKEN}",
    "Content-Type": "application/json"
}

# send request
response = r.post(ENDPOINT_URL, headers=headers, json=paramter_payload)
classified = response.json()

print(classified)
# [ { "label": "World", "score": 0.12341519122860946 }, { "label": "Sports", "score": 0.11741269832494523 }, { "label": "Business", "score": 0.6124446065942992 }, { "label": "Sci/Tech", "score": 0.14672750385214603 } ]
```


**curl example**

```bash
curl https://YOURDOMAIN.us-east-1.aws.endpoints.huggingface.cloud \
-X POST \
-d '{"inputs": "Coming to The Rescue Got a unique problem? Not to worry: you can find a financial planner for every specialized need"}' \
-H "Authorization: Bearer XXX" \
-H "Content-Type: application/json"
```