bert-sklearn
adaptnlp
| bert-sklearn | adaptnlp | |
|---|---|---|
| 1 | 2 | |
| 293 | 414 | |
| - | 0.0% | |
| 0.0 | 0.0 | |
| over 1 year ago | over 2 years ago | |
| Jupyter Notebook | Jupyter Notebook | |
| Apache License 2.0 | Apache License 2.0 |
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bert-sklearn
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Quick BERT Pre-Trained Model for Sentiment Analysis with Scikit Wrapper
Sckit-learn wrapper provided by Charles Nainan. GitHub of Scikit Learn BERT wrapper.
adaptnlp
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Tools to use for Semantic-searching Question Answering System
Check out adaptnlp
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Case Sensitivity using HuggingFace & Google's T5 model (base)
Yes, there are capitals in the tokenizer vocabulary of t5-base and t5-small, so both support capitalization. A few days ago I was using t5-small through adaptnlp for extractive summarization and capitalization was working fine (https://github.com/Novetta/adaptnlp). AdaptNLP is basically just a transformers wrapper, so if you can't figure out a solution, you could just dissect their source code.
What are some alternatives?
bert - TensorFlow code and pre-trained models for BERT
Basic-UI-for-GPT-J-6B-with-low-vram - A repository to run gpt-j-6b on low vram machines (4.2 gb minimum vram for 2000 token context, 3.5 gb for 1000 token context). Model loading takes 12gb free ram.
OpenAI-CLIP - Simple implementation of OpenAI CLIP model in PyTorch.
keytotext - Keywords to Sentences
kruk - Ukrainian instruction-tuned language models and datasets
fastai - The fastai deep learning library
NLU-engine-prototype-benchmarks - Demo and benchmarks for building an NLU engine similar to those in voice assistants. Several intent classifiers are implemented and benchmarked. Conditional Random Fields (CRFs) are used for entity extraction.
gector - Official implementation of the papers "GECToR – Grammatical Error Correction: Tag, Not Rewrite" (BEA-20) and "Text Simplification by Tagging" (BEA-21)
fake-news - Building a fake news detector from initial ideation to model deployment
browser-ml-inference - Edge Inference in Browser with Transformer NLP model
ABSA_Project_4 - This project takes advantange of the parsing and part of speech tagging capabilites of Spacy's pipeline in order to extract aspect/opinion/sentiment triplets. Cluster aspects using unsupervised learning to process sentiment for large amazon review datasets.
Transformers-Tutorials - This repository contains demos I made with the Transformers library by HuggingFace.