---
license: mit
datasets:
- IslamQA/askimam
- IslamQA/hadithanswers
- IslamQA/islamqa
language:
- ar
- en
- fr
- tr
- fa
- id
- te
- ru
- hi
- es
- ur
- ch
- zh
- pt
- de
base_model: intfloat/multilingual-e5-small
tags:
- embedding
- retrieval
- islam
- multilingual
- model
- islamqa
library_name: transformers
description: >
  An embedding model optimized for retrieving passages that answer questions
  about Islam.   The passages are inherently multilingual, as they contain
  quotes from the Quran and Hadith.   They often include preambles like
  "Bismillah" in various languages and follow a specific writing style.
finetuned_on: >-
  180k multilingual questions and answers about Islam, using hard negative
  mining.
data_scraped_on: April 2024
sources:
- https://islamqa.info/
- https://islamweb.net/
- https://hadithanswers.com/
- https://askimam.org/
- https://sorularlaislamiyet.com/
format:
  question_prefix: 'query: '
  answer_prefix: 'passage: '
---

from transformers import AutoModel, AutoTokenizer

from peft import PeftModel

# Load the base model and tokenizer
base_model_name = "intfloat/multilingual-e5-small"

tokenizer = AutoTokenizer.from_pretrained(base_model_name)

base_model = AutoModel.from_pretrained(base_model_name)

# Load the LoRA adapter directly

adapter_repo = "IslamQA/multilingual-e5-small-finetuned"

model = PeftModel.from_pretrained(base_model, adapter_repo)


# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->

  An embedding model optimized for retrieving passages that answer questions
  about Islam.   The passages are inherently multilingual, as they contain
  quotes from the Quran and Hadith.   They often include preambles like
  "Bismillah" in various languages and follow a specific writing style.
## Model Details


### Model Sources [optional]

- https://islamqa.info/
- https://islamweb.net/
- https://hadithanswers.com/
- https://askimam.org/
- https://sorularlaislamiyet.com/

## Uses

- embedding
- retrieval
- islam
- multilingual
- q&a


from transformers import AutoModel, AutoTokenizer
from peft import PeftModel

# Load the base model and tokenizer
base_model_name = "intfloat/multilingual-e5-large-instruct"
tokenizer = AutoTokenizer.from_pretrained(base_model_name)
base_model = AutoModel.from_pretrained(base_model_name)

# Load the LoRA adapter directly
adapter_repo = "IslamQA/multilingual-e5-small-finetuned"
model = PeftModel.from_pretrained(base_model, adapter_repo)