CDial-GPT | scale Chinese Short-Text Conversation Dataset | Natural Language Processing library

 by   thu-coai Python Version: Current License: MIT

kandi X-RAY | CDial-GPT Summary

kandi X-RAY | CDial-GPT Summary

CDial-GPT is a Python library typically used in Artificial Intelligence, Natural Language Processing, Deep Learning, Pytorch, Neural Network, Transformer applications. CDial-GPT has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can download it from GitHub.

A Large-scale Chinese Short-Text Conversation Dataset and Chinese pre-training dialog models
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            kandi-support Support

              CDial-GPT has a medium active ecosystem.
              It has 1506 star(s) with 225 fork(s). There are 26 watchers for this library.
              OutlinedDot
              It had no major release in the last 6 months.
              There are 24 open issues and 82 have been closed. On average issues are closed in 192 days. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of CDial-GPT is current.

            kandi-Quality Quality

              CDial-GPT has 0 bugs and 0 code smells.

            kandi-Security Security

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

            kandi-License License

              CDial-GPT is licensed under the MIT License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              CDial-GPT releases are not available. You will need to build from source code and install.
              Build file is available. You can build the component from source.
              Installation instructions, examples and code snippets are available.
              It has 901 lines of code, 53 functions and 10 files.
              It has high code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed CDial-GPT and discovered the below as its top functions. This is intended to give you an instant insight into CDial-GPT implemented functionality, and help decide if they suit your requirements.
            • Start training
            • Run the program
            • Filtering logits with a given threshold
            • Sample from history
            • Build an input sequence
            • Command line interface
            • Sample a sequence of tokens from the history
            • Build train and validation datasets
            • Get data from LCCA dataset
            • Check if training files are empty
            • Prepare a list of files offset by offset
            • Output a text message
            • Generate chat response
            • Setup random seed
            • Return test data
            Get all kandi verified functions for this library.

            CDial-GPT Key Features

            No Key Features are available at this moment for CDial-GPT.

            CDial-GPT Examples and Code Snippets

            GPT-2 PyTorch Implementation,Usage,How to train?
            Pythondot img1Lines of Code : 72dot img1License : Permissive (Apache-2.0)
            copy iconCopy
            $ python -m gpt2 train --train_corpus           build/corpus.train.txt \
                                   --eval_corpus            build/corpus.test.txt \
                                   --vocab_path             build/vocab.txt \
                                   --save_checkpoin  
            sent-bias,Setup,GPT (OpenAI)
            Pythondot img2Lines of Code : 28dot img2License : Non-SPDX (NOASSERTION)
            copy iconCopy
            git submodule update --init --recursive
            
            cd jiant
            
            conda env create -f environment.yml
            
            source activate jiant
            python -m nltk.downloader perluniprops nonbreaking_prefixes punkt
            pip install python-Levenshtein ftfy
            conda install tensorflow
            python -m spa  
            GPT-2 PyTorch Implementation,Usage,Generate sentences!
            Pythondot img3Lines of Code : 26dot img3License : Permissive (Apache-2.0)
            copy iconCopy
            $ python -m gpt2 generate --vocab_path      build/vocab.txt \
                                      --model_path      model.pth \
                                      --seq_len         64 \
                                      --nucleus_prob    0.8
            
            usage: gpt2 generate [-h] --vocab_  

            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 CDial-GPT

            Install from the source codes:.
            Step 1: Prepare the data for fine-tuning (E.g., STC dataset or "data/toy_data.json" in our respository) and the pre-trianed model:. Step 2: Train the model. Note: We have also provided train_path argument in the training script to read dataset in plain text, which will be sliced and handled distributionally. You can consider to use this argument if the dataset is too large for your system's memory. (also, remember to leave the data_path argument empty if you are using train_path). Step 3: Inference mode. Evaluation is performed on results generated by models fine-tuned on STC dataset. All responses are generated using the Nucleus Sampling scheme with a threshold 0.9 and temperature 0.7. We also employed 3 crowd-sourced annotators to evaluate 200 samples of each model about fluency, relevance, and diversity based on a 2/1/0 score schema. Refer to our paper for more details.

            Support

            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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