text-classification | 中文文本分类（支持 API 部署）

by Ailln Python Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions(10)Vulnerabilities Install Support

kandi X-RAY | text-classification Summary

text-classification is a Python library. text-classification has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However text-classification build file is not available. You can download it from GitHub.

中文文本分类（支持 API 部署）

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Quality

Security

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Support

text-classification has a low active ecosystem.

It has 11 star(s) with 2 fork(s). There are 3 watchers for this library.

It had no major release in the last 6 months.

text-classification has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of text-classification is current.

Quality

text-classification has 0 bugs and 0 code smells.

Security

text-classification has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

text-classification code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

text-classification is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

text-classification releases are not available. You will need to build from source code and install.

text-classification 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.

Top functions reviewed by kandi - BETA

kandi has reviewed text-classification and discovered the below as its top functions. This is intended to give you an instant insight into text-classification implemented functionality, and help decide if they suit your requirements.

Generate training data
Convert a sequence of words into a sequence of ids
Reads the data files in the input_data_path
Create a list of target ids
Get a list of vocab
Train the model
Embedding layer
Make a batch of input data
Shuffle a batch
Copy the configuration to the given path
Check if a path exists
Train the v2 model
Build the model
Generate test data
Build a server session
Generate infer data
Runs test

Get all kandi verified functions for this library.

text-classification Key Features

No Key Features are available at this moment for text-classification.

text-classification Examples and Code Snippets

No Code Snippets are available at this moment for text-classification.

Community Discussions

Trending Discussions on text-classification

How to build a custom question-answering head when using hugginface transformers?

TypeError: an integer is required (got type NoneType)

IndexError: Target is out of bounds

attributeerror: 'dataframe' object has no attribute 'data_type'

ValueError: You must include at least one label and at least one sequence

logistic regression and GridSearchCV using python sklearn

RuntimeError: CUDA out of memory | Elastic Search

sklearn.feature_selection.chi2 returns list of NaN values

Error in 'from torchtext.data import Field, TabularDataset, BucketIterator, Iterator'

how to format data using Pandas (format the data format of the results of sentiment analysis)

QUESTION

How to build a custom question-answering head when using hugginface transformers?

Asked 2022-Apr-03 at 22:24

Using the TFBertForQuestionAnswering.from_pretrained() function, we get a predefined head on top of BERT together with a loss function that are suitable for this task.

My question is how to create a custom head without relying on TFAutoModelForQuestionAnswering.from_pretrained().

I want to do this because there is no place where the architecture of the head is explained clearly. By reading the code here we can see the architecture they are using, but I can't be sure I understand their code 100%.

Starting from How to Fine-tune HuggingFace BERT model for Text Classification is good. However, it covers only the classification task, which is much simpler.

'start_positions' and 'end_positions' are created following this tutorial.

So far, I've got the following:

...

ANSWER

Answered 2022-Apr-03 at 22:24

For future reference, I actually found a solution, which is just editing the TFBertForQuestionAnswering class itself. For example, I added an additional layer in the following code and trained the model as usual and it worked.

Source https://stackoverflow.com/questions/71603492

QUESTION

TypeError: an integer is required (got type NoneType)

Asked 2022-Jan-14 at 10:23

Goal: Amend this Notebook to work with distilbert-base-uncased model

Error occurs in Section 1.3.

Kernel: conda_pytorch_p36. I did Restart & Run All, and refreshed file view in working directory.

Section 1.3:

...

ANSWER

Answered 2022-Jan-14 at 10:23

A Dev explains this predicament at this Git Issue.

The Notebook experiments with BERT, which uses token_type_ids.

DistilBERT does not use token_type_ids for training.

So, this would require re-developing the notebook; removing/ conditioning all mentions of token_type_ids for this model specifically.

Source https://stackoverflow.com/questions/70699247

QUESTION

IndexError: Target is out of bounds

Asked 2022-Jan-12 at 14:00

I am currently trying to replicate the article

https://towardsdatascience.com/text-classification-with-bert-in-pytorch-887965e5820f

to get an introduction to PyTorch and BERT.

I used some own sample corpus and corresponding tragets as practise, but the code throws the following:

...

ANSWER

Answered 2022-Jan-12 at 14:00

You're creating a list of length 33 in your __getitem__ call which is one more than the length of the labels list, hence the out of bounds error. In fact, you create the same list each time this method is called. You're supposed to fetch the associated y with the X found at idx.

If you replace batch_y = np.array(range(...)) with batch_y = np.array(self.labels[idx]), you'll fix your error. Indeed, this is already implemented in your get_batch_labels method.

Source https://stackoverflow.com/questions/70680290

QUESTION

attributeerror: 'dataframe' object has no attribute 'data_type'

Asked 2022-Jan-10 at 08:41

I am getting the following error : attributeerror: 'dataframe' object has no attribute 'data_type'" . I am trying to recreate the code from this link which is based on this article with my own dataset which is similar to the article

...

ANSWER

Answered 2022-Jan-10 at 08:41

The error means you have no data_type column in your dataframe because you missed this step

Source https://stackoverflow.com/questions/70649379

QUESTION

ValueError: You must include at least one label and at least one sequence

Asked 2021-Dec-14 at 09:15

I'm using this Notebook, where section Apply DocumentClassifier is altered as below.

Jupyter Labs, kernel: conda_mxnet_latest_p37.

Error appears to be an ML standard practice response. However, I pass/ create the same parameter and the variable names as the original code. So it's something to do with their values in my code.

My Code:

...

ANSWER

Answered 2021-Dec-08 at 21:05

Reading official docs and analyzing that the error is generated when calling .predict(docs_to_classify) I could recommend that you try to do basic tests such as using the parameter labels = ["negative", "positive"] , and correct if it is caused by string values of the external file and optionally you should also check where it indicates the use of pipelines.

Source https://stackoverflow.com/questions/70278323

QUESTION

logistic regression and GridSearchCV using python sklearn

Asked 2021-Dec-10 at 14:14

I am trying code from this page. I ran up to the part LR (tf-idf) and got the similar results

After that I decided to try GridSearchCV. My questions below:

...

ANSWER

Answered 2021-Dec-09 at 23:12

You end up with the error with precision because some of your penalization is too strong for this model, if you check the results, you get 0 for f1 score when C = 0.001 and C = 0.01

Source https://stackoverflow.com/questions/70264157

QUESTION

RuntimeError: CUDA out of memory | Elastic Search

Asked 2021-Dec-09 at 11:53

I'm fairly new to Machine Learning. I've successfully solved errors to do with parameters and model setup.

I'm using this Notebook, where section Apply DocumentClassifier is altered as below.

Jupyter Labs, kernel: conda_mxnet_latest_p37.

Error seems to be more about my laptop's hardware, rather than my code being broken.

Update: I changed batch_size=4, it ran for ages only to crash.

What should be my standard approach to solving this error?

My Code:

...

ANSWER

Answered 2021-Dec-09 at 11:53

Reducing the batch_size helped me:

Source https://stackoverflow.com/questions/70288528

QUESTION

sklearn.feature_selection.chi2 returns list of NaN values

Asked 2021-Dec-03 at 17:36

I have the following dataset (I will upload only a sample of 4 rows, the real one has 15,000 rows):

...

ANSWER

Answered 2021-Dec-03 at 17:36

I don't think it's really meaningful to compute the chi-squared statistic without having the classes attached. The code chi2(X_train, y_neutral) is asking "Assuming that class and the parameter are independent, what are the odds of getting this distribution?" But all of the examples you're showing it are the same class.

I would suggest this instead:

Source https://stackoverflow.com/questions/70218171

QUESTION

Error in 'from torchtext.data import Field, TabularDataset, BucketIterator, Iterator'

Asked 2021-Nov-01 at 02:55

I am trying to implement this article https://towardsdatascience.com/bert-text-classification-using-pytorch-723dfb8b6b5b, but I have the following problem.

...

ANSWER

Answered 2021-Nov-01 at 02:55

Try

Source https://stackoverflow.com/questions/69765669

QUESTION

how to format data using Pandas (format the data format of the results of sentiment analysis)

Asked 2021-Oct-22 at 06:37

I am doing sentiment analysis using BERT. I want to convert the result to DataFrame format, but I don't know how. If anyone knows, please let me know.

The related web pages are as follows https://huggingface.co/transformers/main_classes/pipelines.html

...

ANSWER

Answered 2021-Oct-22 at 06:37

Try this:

Source https://stackoverflow.com/questions/69670480

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

Vulnerabilities

No vulnerabilities reported

Install text-classification

You can download it from GitHub.
You can use text-classification 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.

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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