Intent-Aware-Ranking-Transformers | aware Response Ranking with Transformers | Natural Language Processing library

 by   yangliuy Python Version: Current License: No License

kandi X-RAY | Intent-Aware-Ranking-Transformers Summary

kandi X-RAY | Intent-Aware-Ranking-Transformers Summary

Intent-Aware-Ranking-Transformers is a Python library typically used in Artificial Intelligence, Natural Language Processing, Deep Learning, Pytorch, Tensorflow, Transformer applications. Intent-Aware-Ranking-Transformers has no bugs, it has no vulnerabilities and it has low support. However Intent-Aware-Ranking-Transformers build file is not available. You can download it from GitHub.

iart: intent-aware response ranking with transformers in information-seeking conversation systems. this is the source code of the iart model (intent-aware response ranking with transformers), which is proposed for multi-turn response selection in the retrieval-based conversation systems. iart is built on top of the integration of user intent modeling and language representation learning with the transformer architecture, which relies entirely on a self-attention mechanism instead of recurrent nets. it incorporates intent-aware utterance attention to derive an importance weighting scheme of utterances in conversation context with the aim of better conversation history understanding. iart is published in www 2020. please find our paper for more details of this model. iart is built on the top of the dam model. the model incorporates intent-aware utterance attention to derive the importance weighting
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            kandi-support Support

              Intent-Aware-Ranking-Transformers has a low active ecosystem.
              It has 12 star(s) with 0 fork(s). There are 3 watchers for this library.
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              It had no major release in the last 6 months.
              There are 0 open issues and 3 have been closed. On average issues are closed in 277 days. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of Intent-Aware-Ranking-Transformers is current.

            kandi-Quality Quality

              Intent-Aware-Ranking-Transformers has 0 bugs and 0 code smells.

            kandi-Security Security

              Intent-Aware-Ranking-Transformers has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              Intent-Aware-Ranking-Transformers code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              Intent-Aware-Ranking-Transformers does not have a standard license declared.
              Check the repository for any license declaration and review the terms closely.
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              Without a license, all rights are reserved, and you cannot use the library in your applications.

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              Intent-Aware-Ranking-Transformers releases are not available. You will need to build from source code and install.
              Intent-Aware-Ranking-Transformers has no build file. You will be need to create the build yourself to build the component from source.
              Installation instructions are not available. Examples and code snippets are available.
              It has 2835 lines of code, 128 functions and 27 files.
              It has high code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed Intent-Aware-Ranking-Transformers and discovered the below as its top functions. This is intended to give you an instant insight into Intent-Aware-Ranking-Transformers implemented functionality, and help decide if they suit your requirements.
            • Train the model
            • Run preprocessing
            • Calculate statistics for words
            • Filter documents that are less than min_len
            • Run train and test validation
            • Calculate the id of the text
            • Parse a line of dmn file
            • Run SMN preprocessing
            • Load a 2D file
            • Transfers a list of docs into 2d lists
            • Return a list of lowercase words lowercase
            • Builds batch of data
            • Evaluate the equation of a file
            • Compute similarity
            • Convenience function for CNN
            • Run preprocess
            • Parse two corpus
            • Parse a text file and return a list of relations
            • Split train validation
            • Return the number of terms in words
            • Build a single_batch dictionary from data
            • Save words stats to a file
            • Load words stats from file
            • Read word2id file
            • Save a corpus to a file
            • Save a list of relations to a file
            Get all kandi verified functions for this library.

            Intent-Aware-Ranking-Transformers Key Features

            No Key Features are available at this moment for Intent-Aware-Ranking-Transformers.

            Intent-Aware-Ranking-Transformers Examples and Code Snippets

            No Code Snippets are available at this moment for Intent-Aware-Ranking-Transformers.

            Community Discussions

            QUESTION

            number of matches for keywords in specified categories
            Asked 2022-Apr-14 at 13:32

            For a large scale text analysis problem, I have a data frame containing words that fall into different categories, and a data frame containing a column with strings and (empty) counting columns for each category. I now want to take each individual string, check which of the defined words appear, and count them within the appropriate category.

            As a simplified example, given the two data frames below, i want to count how many of each animal type appear in the text cell.

            ...

            ANSWER

            Answered 2022-Apr-14 at 13:32

            Here's a way do to it in the tidyverse. First look at whether strings in df_texts$text contain animals, then count them and sum by text and type.

            Source https://stackoverflow.com/questions/71871613

            QUESTION

            Apple's Natural Language API returns unexpected results
            Asked 2022-Apr-01 at 20:30

            I'm trying to figure out why Apple's Natural Language API returns unexpected results.

            What am I doing wrong? Is it a grammar issue?

            I have the following four strings, and I want to extract each word's "stem form."

            ...

            ANSWER

            Answered 2022-Apr-01 at 20:30

            As for why the tagger doesn't find "accredit" from "accreditation", this is because the scheme .lemma finds the lemma of words, not actually the stems. See the difference between stem and lemma on Wikipedia.

            The stem is the part of the word that never changes even when morphologically inflected; a lemma is the base form of the word. For example, from "produced", the lemma is "produce", but the stem is "produc-". This is because there are words such as production and producing In linguistic analysis, the stem is defined more generally as the analyzed base form from which all inflected forms can be formed.

            The documentation uses the word "stem", but I do think that the lemma is what is intended here, and getting "accreditation" is the expected behaviour. See the Usage section of the Wikipedia article for "Word stem" for more info. The lemma is the dictionary form of a word, and "accreditation" has a dictionary entry, whereas something like "accredited" doesn't. Whatever you call these things, the point is that there are two distinct concepts, and the tagger gets you one of them, but you are expecting the other one.

            As for why the order of the words matters, this is because the tagger tries to analyse your words as "natural language", rather than each one individually. Naturally, word order matters. If you use .lexicalClass, you'll see that it thinks the third word in text2 is an adjective, which explains why it doesn't think its dictionary form is "accredit", because adjectives don't conjugate like that. Note that accredited is an adjective in the dictionary. So "is it a grammar issue?" Exactly.

            Source https://stackoverflow.com/questions/71711847

            QUESTION

            Tokenize text but keep compund hyphenated words together
            Asked 2022-Mar-29 at 09:16

            I am trying to clean up text using a pre-processing function. I want to remove all non-alpha characters such as punctuation and digits, but I would like to retain compound words that use a dash without splitting them (e.g. pre-tender, pre-construction).

            ...

            ANSWER

            Answered 2022-Mar-29 at 09:14

            To remove all non-alpha characters but - between letters, you can use

            Source https://stackoverflow.com/questions/71659125

            QUESTION

            Create new boolean fields based on specific bigrams appearing in a tokenized pandas dataframe
            Asked 2022-Feb-16 at 20:47

            Looping over a list of bigrams to search for, I need to create a boolean field for each bigram according to whether or not it is present in a tokenized pandas series. And I'd appreciate an upvote if you think this is a good question!

            List of bigrams:

            ...

            ANSWER

            Answered 2022-Feb-16 at 20:28

            You could use a regex and extractall:

            Source https://stackoverflow.com/questions/71147799

            QUESTION

            ModuleNotFoundError: No module named 'milvus'
            Asked 2022-Feb-15 at 19:23

            Goal: to run this Auto Labelling Notebook on AWS SageMaker Jupyter Labs.

            Kernels tried: conda_pytorch_p36, conda_python3, conda_amazonei_mxnet_p27.

            ...

            ANSWER

            Answered 2022-Feb-03 at 09:29

            I would recommend to downgrade your milvus version to a version before the 2.0 release just a week ago. Here is a discussion on that topic: https://github.com/deepset-ai/haystack/issues/2081

            Source https://stackoverflow.com/questions/70954157

            QUESTION

            Which model/technique to use for specific sentence extraction?
            Asked 2022-Feb-08 at 18:35

            I have a dataset of tens of thousands of dialogues / conversations between a customer and customer support. These dialogues, which could be forum posts, or long-winded email conversations, have been hand-annotated to highlight the sentence containing the customers problem. For example:

            Dear agent, I am writing to you because I have a very annoying problem with my washing machine. I bought it three weeks ago and was very happy with it. However, this morning the door does not lock properly. Please help

            Dear customer.... etc

            The highlighted sentence would be:

            However, this morning the door does not lock properly.

            1. What approaches can I take to model this, so that in future I can automatically extract the customers problem? The domain of the datasets are broad, but within the hardware space, so it could be appliances, gadgets, machinery etc.
            2. What is this type of problem called? I thought this might be called "intent recognition", but most guides seem to refer to multiclass classification. The sentence either is or isn't the customers problem. I considered analysing each sentence and performing binary classification, but I'd like to explore options that take into account the context of the rest of the conversation if possible.
            3. What resources are available to research how to implement this in Python (using tensorflow or pytorch)

            I found a model on HuggingFace which has been pre-trained with customer dialogues, and have read the research paper, so I was considering fine-tuning this as a starting point, but I only have experience with text (multiclass/multilabel) classification when it comes to transformers.

            ...

            ANSWER

            Answered 2022-Feb-07 at 10:21

            This type of problem where you want to extract the customer problem from the original text is called Extractive Summarization and this type of task is solved by Sequence2Sequence models.

            The main reason for this type of model being called Sequence2Sequence is because the input and the output of this model would both be text.

            I recommend you to use a transformers model called Pegasus which has been pre-trained to predict a masked text, but its main application is to be fine-tuned for text summarization (extractive or abstractive).

            This Pegasus model is listed on Transformers library, which provides you with a simple but powerful way of fine-tuning transformers with custom datasets. I think this notebook will be extremely useful as guidance and for understanding how to fine-tune this Pegasus model.

            Source https://stackoverflow.com/questions/70990722

            QUESTION

            Assigning True/False if a token is present in a data-frame
            Asked 2022-Jan-06 at 12:38

            My current data-frame is:

            ...

            ANSWER

            Answered 2022-Jan-06 at 12:13

            QUESTION

            How to calculate perplexity of a sentence using huggingface masked language models?
            Asked 2021-Dec-25 at 21:51

            I have several masked language models (mainly Bert, Roberta, Albert, Electra). I also have a dataset of sentences. How can I get the perplexity of each sentence?

            From the huggingface documentation here they mentioned that perplexity "is not well defined for masked language models like BERT", though I still see people somehow calculate it.

            For example in this SO question they calculated it using the function

            ...

            ANSWER

            Answered 2021-Dec-25 at 21:51

            There is a paper Masked Language Model Scoring that explores pseudo-perplexity from masked language models and shows that pseudo-perplexity, while not being theoretically well justified, still performs well for comparing "naturalness" of texts.

            As for the code, your snippet is perfectly correct but for one detail: in recent implementations of Huggingface BERT, masked_lm_labels are renamed to simply labels, to make interfaces of various models more compatible. I have also replaced the hard-coded 103 with the generic tokenizer.mask_token_id. So the snippet below should work:

            Source https://stackoverflow.com/questions/70464428

            QUESTION

            Mapping values from a dictionary's list to a string in Python
            Asked 2021-Dec-21 at 16:45

            I am working on some sentence formation like this:

            ...

            ANSWER

            Answered 2021-Dec-12 at 17:53

            You can first replace the dictionary keys in sentence to {} so that you can easily format a string in loop. Then you can use itertools.product to create the Cartesian product of dictionary.values(), so you can simply loop over it to create your desired sentences.

            Source https://stackoverflow.com/questions/70325758

            QUESTION

            What are differences between AutoModelForSequenceClassification vs AutoModel
            Asked 2021-Dec-05 at 09:07

            We can create a model from AutoModel(TFAutoModel) function:

            ...

            ANSWER

            Answered 2021-Dec-05 at 09:07

            The difference between AutoModel and AutoModelForSequenceClassification model is that AutoModelForSequenceClassification has a classification head on top of the model outputs which can be easily trained with the base model

            Source https://stackoverflow.com/questions/69907682

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network

            Vulnerabilities

            No vulnerabilities reported

            Install Intent-Aware-Ranking-Transformers

            You can download it from GitHub.
            You can use Intent-Aware-Ranking-Transformers like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

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