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

Text-to-Speech for Belarusian language

Espial is an engine for automated organization and discovery of personal knowledge

To create a deep learning model which can explain the content of an image in the form of speech through caption generation with attention mechanism on Flickr8K dataset.

Asr abc - Automatic speech recognition(ASR),中文语音识别

Tools for curating biomedical training data for large-scale language modeling

The official repository of the ISBI 2022 KNIGHT Challenge

ADCS - Automatic Defect Classification System (ADCS) for SSMC

BeautyNet is an AI powered model which can tell you whether you're beautiful or not.

Official code for Spoken ObjectNet: A Bias-Controlled Spoken Caption Dataset

Open source code for AlphaFold.

SEJE is a prototype for the paper Learning Text-Image Joint Embedding for Efficient Cross-Modal Retrieval with Deep Feature Engineering.

华为商城抢购手机的Python脚本 Python script of Huawei Store snapping up mobile phones

Code for the ACL 2021 paper "Structural Guidance for Transformer Language Models"

A sample project that exists for PyPUG's "Tutorial on Packaging and Distributing Projects"

Predicting the usefulness of reviews given the review text and metadata surrounding the reviews.

A Fast Command Analyser based on Dict and Pydantic

A Python script which randomly chooses and prints a file from a directory.

Python Implementation of ``Modeling the Influence of Verb Aspect on the Activation of Typical Event Locations with BERT'' (Findings of ACL: ACL 2021)

Code for ACL 2020 paper "Rigid Formats Controlled Text Generation"

Twitter-Sentiment-Analysis - Analysis of twitter posts' positive and negative score.