README.md

---
license: afl-3.0

language: 
- yo

datasets:
- afriqa
- xlsum
- menyo20k_mt
- alpaca-gpt4
---

# Model Description
**mistral_7b_yo_instruct** is a **text generation** model in Yorùbá. 

## Intended uses & limitations
#### How to use

```python

import requests

API_URL = "https://i8nykns7vw253vx3.us-east-1.aws.endpoints.huggingface.cloud"
headers = {
	"Authorization": "Bearer hf_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx",
	"Content-Type": "application/json"
}

def query(payload):
	response = requests.post(API_URL, headers=headers, json=payload)
	return response.json()

# Prompt content: "Pẹlẹ o. Bawo ni o se wa?" ("Hello. How are you?")	
output = query({
	"inputs": "Pẹlẹ o. Bawo ni o se wa?",
})

# Model response: "O dabo. O jẹ ọjọ ti o dara." ("I am safe. It was a good day.")
print(output)
```

#### Eval results
Coming soon

#### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.  

#### Training data
This model is fine-tuned on 60k+ instruction-following demonstrations built from an aggregation of datasets ([AfriQA](https://huggingface.co/datasets/masakhane/afriqa), [XLSum](https://huggingface.co/datasets/csebuetnlp/xlsum), [MENYO-20k](https://huggingface.co/datasets/menyo20k_mt)), and translations of [Alpaca-gpt4](https://huggingface.co/datasets/vicgalle/alpaca-gpt4)).

### Use and safety
We emphasize that mistral_7b_yo_instruct is intended only for research purposes and is not ready to be deployed for general use, namely because we have not designed adequate safety measures.