Find answers in French texts using QAmemBERT models
Ask Harry Potter questions and get answers
Posez des questions sur l'islam et obtenez des réponses
pdf_reader
Search Wikipedia articles by query
Find... answers to questions from provided text
Ask questions about scientific papers
LLM service based on Search and Vector enhanced retrieval
Interact with a language model to solve math problems
Generate answers to your questions using text input
Answer science questions
Answer medical questions
QAmembert is a question answering (QA) model designed to find answers in French texts. It leverages the advanced language understanding capabilities of the MemBERT architecture, optimized for French language processing. This tool is specifically engineered to handle QA tasks efficiently, providing accurate answers by analyzing and understanding the context of the input text.
• Multi-lingual Support: While primarily designed for French, it supports other languages, making it versatile for diverse QA needs.
• Contextual Understanding: Advanced NLP capabilities to comprehend complex contexts and nuances in text.
• Efficient Processing: Optimized for quick and accurate responses, even with large volumes of text.
• Customizable: Allows fine-tuning for specific domains or styles, enhancing performance for specialized use cases.
• Open-Source: Accessible for developers and researchers, promoting transparency and collaboration.
Install the Required Library: Use pip to install the Hugging Face transformers library.
pip install transformers
Import the Model: Load the QAmembert model and tokenizer.
from transformers import AutoModelForQuestionAnswering, AutoTokenizer
model_name = "dfmartin/qamembert-base-french-cased"
model = AutoModelForQuestionAnswering.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
Load the Text and Question: Provide the text snippet and the question you want answered.
text = "Your French text here."
question = "Your question here."
Tokenize the Input: Use the tokenizer to prepare the input for the model.
inputs = tokenizer(question, text, return_tensors="pt")
Generate Answer: Use the model to extract the answer from the text.
outputs = model(**inputs)
answer_start = outputs.start_logits.argmax()
answer_end = outputs.end_logits.argmax() + 1
answer = tokenizer.convert_tokens_to_string(tokenizer.convert_ids_to_tokens(inputs.input_ids[0][answer_start:answer_end]))
1. What languages does QAmembert support?
While QAmembert is optimized for French, it also supports other languages to a lesser extent, making it a versatile tool for multi-lingual QA tasks.
2. How accurate is QAmembert?
The accuracy of QAmembert depends on the quality of the input text and how well the question aligns with the context. Fine-tuning the model on specific datasets can improve performance.
3. Can I use QAmembert for commercial projects?
Yes, QAmembert is open-source and can be used for both research and commercial applications, provided you adhere to the licensing terms.