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---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# 😵💫🦙 Alpaca HalluciHunter
This is a cross-lingual SetFit model [SetFit model](https://github.com/huggingface/setfit) to detect potentially bad instructions from Alpaca (and likely other synthetically generated instruction datasets).
The model has been fine-tuned with 1,000 labeled examples from the AlpacaCleaned dataset. It leverages a multilingual sentence transformer `paraphrase-multilingual-mpnet-base-v2`, inspired by the findings from the SetFit paper (Section 6. Multilingual experiments.), where they trained models in English that performed well across languages.
It's a binary classifier with two labels:
- `ALL GOOD`, a given instruction, input, and output are correct,
- `BAD INSTRUCTION`, there's an issue with the instruction, and/or input and output.
This model can greatly speed up the validation of Alpaca Datasets, flagging examples that need to be fixed or simply discarded.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
Load your Alpaca Dataset:
```bash
from datasets import Dataset, load_dataset
import pandas as pd
# this can be a translation (e.g., Spanish, Camoscio Italian Alpaca, etc.)
dataset = pd.read_json("https://github.com/gururise/AlpacaDataCleaned/raw/main/alpaca_data_cleaned.json")
dataset["id"] = [i for i in range(len(dataset))]
ds = Dataset.from_pandas(dataset)
```
Create a text field containing the instruction, input and output to use for inference:
```python
def transform(r):
return {
"text": f"INSTRUCTION:\n{r['instruction']}\nINPUT:\n{r['input']}\nOUTPUT:\n{r['output']}\n"
}
ds = ds.map(transform)
```
Load the model:
```python
from setfit import SetFitModel
# Download from Hub
model = SetFitModel.from_pretrained("argilla/alpaca-hallucihunter-multilingual ")
```
Perform inference and prediction col to your dataset:
```python
labels = ["ALL GOOD", "BAD INSTRUCTION"]
def get_predictions(texts):
probas = model.predict_proba(texts, as_numpy=True)
for pred in probas:
yield [{"label": label, "score": score} for label, score in zip(labels, pred)]
ds = ds.map(lambda batch: {"prediction": list(get_predictions(batch["text"]))}, batched=True)
```
Load the data into Argilla for exploration and validation. You [need to launch Argilla](https://www.argilla.io/blog/launching-argilla-huggingface-hub):
```python
# Replace api_url with the url to your HF Spaces URL if using Spaces
# Replace api_key if you configured a custom API key
rg.init(
api_url="https://your-agilla-instance.hf.space",
api_key="team.apikey"
)
rg_dataset = rg.DatasetForTextClassification().from_datasets(ds)
rg.log(records=rg_dataset, name="alpaca_to_clean")
```
## Examples
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
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