gpt-2-output-dataset | Dataset of GPT-2 outputs for research in detection , biases | Natural Language Processing library

by openai Python Version: Current License: MIT

X-Ray Key Features Code Snippets(1)Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | gpt-2-output-dataset Summary

gpt-2-output-dataset is a Python library typically used in Artificial Intelligence, Natural Language Processing, Deep Learning applications. gpt-2-output-dataset has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. However gpt-2-output-dataset has 3 bugs. You can download it from GitHub.

Dataset of GPT-2 outputs for research in detection, biases, and more

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Quality

Security

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gpt-2-output-dataset has a medium active ecosystem.

It has 1723 star(s) with 500 fork(s). There are 72 watchers for this library.

It had no major release in the last 6 months.

There are 26 open issues and 20 have been closed. On average issues are closed in 49 days. There are 2 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of gpt-2-output-dataset is current.

Quality

gpt-2-output-dataset has 3 bugs (0 blocker, 0 critical, 2 major, 1 minor) and 11 code smells.

Security

gpt-2-output-dataset has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

gpt-2-output-dataset code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

gpt-2-output-dataset 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

gpt-2-output-dataset 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 are available. Examples and code snippets are not available.

gpt-2-output-dataset saves you 285 person hours of effort in developing the same functionality from scratch.

It has 688 lines of code, 26 functions and 8 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed gpt-2-output-dataset and discovered the below as its top functions. This is intended to give you an instant insight into gpt-2-output-dataset implemented functionality, and help decide if they suit your requirements.

Runs a neural network
Setup distributed device group
Return whether distributed is distributed
Print a summary of the model
Helper function for all_reduce
Compute accuracy
Download one or more datasets
Train a model
Validate the given model
Load and train neural networks
Handle GET request
Start the response header
Serve a model
Log the given arguments to stderr
Load a text file into a list of texts and labels
Load a list of texts from source
Logs a message

Get all kandi verified functions for this library.

gpt-2-output-dataset Key Features

No Key Features are available at this moment for gpt-2-output-dataset.

gpt-2-output-dataset Examples and Code Snippets

MAUVE,Quick Start

Python

Lines of Code : 13

License : Non-SPDX (NOASSERTION)

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python examples/download_gpt2_dataset.py


from examples import load_gpt2_dataset
p_text = load_gpt2_dataset('data/amazon.valid.jsonl', num_examples=100) # human
q_text = load_gpt2_dataset('data/amazon-xl-1542M.valid.jsonl', num_examples=100) # machi

Community Discussions

Trending Discussions on gpt-2-output-dataset

What do the logits and probabilities from RobertaForSequenceClassification represent?

QUESTION

What do the logits and probabilities from RobertaForSequenceClassification represent?

Asked 2020-Dec-10 at 23:53

Being new to the "Natural Language Processing" scene, I am experimentally learning and have implemented the following segment of code:

...

ANSWER

Answered 2020-Dec-10 at 23:53

You have initialized a RobertaForSequenceClassification model that per default (in case of roberta-base and roberta-large which have no trained output layers for sequence classification) tries to classify if a sequence belongs to one class or another. I used the expression "belongs to one class or another" because these classes have no meaning yet. The output layer is untrained and it requires a finetuning to give these classes a meaning. Class 0 could be X and Class 1 could be Y or the other way around. For example, the tutorial for finetuning a sequence classification model for the IMDb review dataset defines negative reviews as Class 0 and positive reviews as Class 1 (link).

You can check the number of supported classes with:

Source https://stackoverflow.com/questions/65221079

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

Vulnerabilities

No vulnerabilities reported

Install gpt-2-output-dataset

For each model, we have a training split of 250K generated examples, as well as validation and test splits of 5K examples. All data is located in Google Cloud Storage, under the directory gs://gpt-2/output-dataset/v1.
webtext.${split}.jsonl
small-117M.${split}.jsonl
small-117M-k40.${split}.jsonl
medium-345M.${split}.jsonl
medium-345M-k40.${split}.jsonl
large-762M.${split}.jsonl
large-762M-k40.${split}.jsonl
xl-1542M.${split}.jsonl
xl-1542M-k40.${split}.jsonl

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 .

Find more information at: