trfaq

Google supported this work by providing Google Cloud credit. Thank you Google for supporting the open source! 🎉

What is this?

At this repo, I'm releasing the training script and a full working inference example for my model mys/bert-base-turkish-cased-nli-mean-faq-mnr published on HuggingFace. Please note that the training code at finetune_tf.py is a simplified version of the original, which is intended for educational purposes and not optimized for anything. However, it contains an implementation of the Multiple Negatives Symmetric Ranking loss, and you can use it in your own work. Additionally, I cleaned and filtered the Turkish subset of the clips/mqa dataset, as it contains lots of mis-encoded texts. You can download this cleaned dataset here.

Model

This is a finetuned version of mys/bert-base-turkish-cased-nli-mean for FAQ retrieval, which is itself a finetuned version of dbmdz/bert-base-turkish-cased for NLI. It maps questions & answers to 768 dimensional vectors to be used for FAQ-style chatbots and answer retrieval in question-answering pipelines. It was trained on the Turkish subset of clips/mqa dataset after some cleaning/ filtering and with a Multiple Negatives Symmetric Ranking loss. Before finetuning, I added two special tokens to the tokenizer (i.e., for questions and for answers) and resized the model embeddings, so you need to prepend the relevant tokens to the sequences before feeding them into the model. Please have a look at my accompanying repo to see how it was finetuned and how it can be used in inference. The following code snippet is an excerpt from the inference at the repo.

Usage

see inference.py for a full working example.

" + q for q in questions] answers = ["" + a for a in answers] def answer_faq(model, tokenizer, questions, answers, return_similarities=False): q_len = len(questions) tokens = tokenizer(questions + answers, padding=True, return_tensors='tf') embs = model(**tokens)[0] attention_masks = tf.cast(tokens['attention_mask'], tf.float32) sample_length = tf.reduce_sum(attention_masks, axis=-1, keepdims=True) masked_embs = embs * tf.expand_dims(attention_masks, axis=-1) masked_embs = tf.reduce_sum(masked_embs, axis=1) / tf.cast(sample_length, tf.float32) a = tf.math.l2_normalize(masked_embs[:q_len, :], axis=1) b = tf.math.l2_normalize(masked_embs[q_len:, :], axis=1) similarities = tf.matmul(a, b, transpose_b=True) scores = tf.nn.softmax(similarities) results = list(zip(answers, scores.numpy().squeeze().tolist())) sorted_results = sorted(results, key=lambda x: x[1], reverse=True) sorted_results = [{"answer": answer.replace("", ""), "score": f"{score:.4f}"} for answer, score in sorted_results] return sorted_results for question in questions: results = answer_faq(model, tokenizer, [question], answers) print(question.replace("", "")) print(results) print("---------------------") ">

questions = [
    "Merhaba",
    "Nasılsın?",
    "Bireysel araç kiralama yapıyor musunuz?",
    "Kurumsal araç kiralama yapıyor musunuz?"
]

answers = [
    "Merhaba, size nasıl yardımcı olabilirim?",
    "İyiyim, teşekkür ederim. Size nasıl yardımcı olabilirim?",
    "Hayır, sadece Kurumsal Araç Kiralama operasyonları gerçekleştiriyoruz. Size başka nasıl yardımcı olabilirim?",
    "Evet, kurumsal araç kiralama hizmetleri sağlıyoruz. Size nasıl yardımcı olabilirim?"
]


questions = ["" + q for q in questions]
answers = ["" + a for a in answers]


def answer_faq(model, tokenizer, questions, answers, return_similarities=False):
    q_len = len(questions)
    tokens = tokenizer(questions + answers, padding=True, return_tensors='tf')
    embs = model(**tokens)[0]

    attention_masks = tf.cast(tokens['attention_mask'], tf.float32)
    sample_length = tf.reduce_sum(attention_masks, axis=-1, keepdims=True)
    masked_embs = embs * tf.expand_dims(attention_masks, axis=-1)
    masked_embs = tf.reduce_sum(masked_embs, axis=1) / tf.cast(sample_length, tf.float32)
    a = tf.math.l2_normalize(masked_embs[:q_len, :], axis=1)
    b = tf.math.l2_normalize(masked_embs[q_len:, :], axis=1)

    similarities = tf.matmul(a, b, transpose_b=True)
        
    scores = tf.nn.softmax(similarities)
    results = list(zip(answers, scores.numpy().squeeze().tolist()))
    sorted_results = sorted(results, key=lambda x: x[1], reverse=True)
    sorted_results = [{"answer": answer.replace("", ""), "score": f"{score:.4f}"} for answer, score in sorted_results]
    return sorted_results


for question in questions:
    results = answer_faq(model, tokenizer, [question], answers)
    print(question.replace("", ""))
    print(results)
    print("---------------------")

And the output is:

Merhaba
[{'answer': 'Merhaba, size nasıl yardımcı olabilirim?', 'score': '0.2931'}, {'answer': 'İyiyim, teşekkür ederim. Size nasıl yardımcı olabilirim?', 'score': '0.2751'}, {'answer': 'Hayır, sadece Kurumsal Araç Kiralama operasyonları gerçekleştiriyoruz. Size başka nasıl yardımcı olabilirim?', 'score': '0.2200'}, {'answer': 'Evet, kurumsal araç kiralama hizmetleri sağlıyoruz. Size nasıl yardımcı olabilirim?', 'score': '0.2118'}]
---------------------
Nasılsın?
[{'answer': 'İyiyim, teşekkür ederim. Size nasıl yardımcı olabilirim?', 'score': '0.2808'}, {'answer': 'Merhaba, size nasıl yardımcı olabilirim?', 'score': '0.2623'}, {'answer': 'Hayır, sadece Kurumsal Araç Kiralama operasyonları gerçekleştiriyoruz. Size başka nasıl yardımcı olabilirim?', 'score': '0.2320'}, {'answer': 'Evet, kurumsal araç kiralama hizmetleri sağlıyoruz. Size nasıl yardımcı olabilirim?', 'score': '0.2249'}]
---------------------
Bireysel araç kiralama yapıyor musunuz?
[{'answer': 'Hayır, sadece Kurumsal Araç Kiralama operasyonları gerçekleştiriyoruz. Size başka nasıl yardımcı olabilirim?', 'score': '0.2861'}, {'answer': 'Evet, kurumsal araç kiralama hizmetleri sağlıyoruz. Size nasıl yardımcı olabilirim?', 'score': '0.2768'}, {'answer': 'İyiyim, teşekkür ederim. Size nasıl yardımcı olabilirim?', 'score': '0.2215'}, {'answer': 'Merhaba, size nasıl yardımcı olabilirim?', 'score': '0.2156'}]
---------------------
Kurumsal araç kiralama yapıyor musunuz?
[{'answer': 'Evet, kurumsal araç kiralama hizmetleri sağlıyoruz. Size nasıl yardımcı olabilirim?', 'score': '0.3060'}, {'answer': 'Hayır, sadece Kurumsal Araç Kiralama operasyonları gerçekleştiriyoruz. Size başka nasıl yardımcı olabilirim?', 'score': '0.2929'}, {'answer': 'İyiyim, teşekkür ederim. Size nasıl yardımcı olabilirim?', 'score': '0.2066'}, {'answer': 'Merhaba, size nasıl yardımcı olabilirim?', 'score': '0.1945'}]
---------------------

Question and answer retrieval in Turkish with BERT

Related tags

Overview

trfaq

What is this?

Model

Usage

Owner

M. Yusuf Sarıgöz

Différents programmes créant une interface graphique a l'aide de Tkinter pour simplifier la vie des étudiants.

Phomber is infomation grathering tool that reverse search phone numbers and get their details, written in python3.

Repository for Project Insight: NLP as a Service

This repository contains helper functions which can help you generate additional data points depending on your NLP task.

:id: A python library for accurate and scalable fuzzy matching, record deduplication and entity-resolution.

This repository will contain the code for the CVPR 2021 paper "GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields"

Twitter-NLP-Analysis - Twitter Natural Language Processing Analysis

뉴스 도메인 질의응답 시스템 (21-1학기 졸업 프로젝트)

Convolutional 2D Knowledge Graph Embeddings resources

Fastseq 基于ONNXRUNTIME的文本生成加速框架

ReCoin - Restoring our environment and businesses in parallel

Using BERT-based models for toxic span detection

SpeechBrain is an open-source and all-in-one speech toolkit based on PyTorch.

EasyTransfer is designed to make the development of transfer learning in NLP applications easier.

VMD Audio/Text control with natural language

Open source annotation tool for machine learning practitioners.

CDLA: A Chinese document layout analysis (CDLA) dataset

BMInf (Big Model Inference) is a low-resource inference package for large-scale pretrained language models (PLMs).

Snips Python library to extract meaning from text

Protein Language Model