keras-xlnet
gector
keras-xlnet | gector | |
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1 | 2 | |
172 | 864 | |
- | 0.8% | |
0.0 | 0.0 | |
over 2 years ago | 9 months ago | |
Python | Python | |
MIT License | Apache License 2.0 |
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keras-xlnet
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[Research] How do you train on and evaluate GLUE STS-B?
I've found this online looking for the very same answer. It's from 2019 and I haven't tried it (yet) but this should hopefully help as an example: Github link.
gector
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ML application for grammar correction
If you capture it, you might as well correct it. Check out Gramformer or Grammarly's Gector. You can do scoring based on number of mistakes proposed by these models i.e. the fewer, the better.
- Is there any way to detect grammatical errors and classify text as being either grammatically correct/incorrect?
What are some alternatives?
BERT-pytorch - Google AI 2018 BERT pytorch implementation
Gramformer - A framework for detecting, highlighting and correcting grammatical errors on natural language text. Created by Prithiviraj Damodaran. Open to pull requests and other forms of collaboration.
transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
haystack - :mag: LLM orchestration framework to build customizable, production-ready LLM applications. Connect components (models, vector DBs, file converters) to pipelines or agents that can interact with your data. With advanced retrieval methods, it's best suited for building RAG, question answering, semantic search or conversational agent chatbots.
spark-nlp - State of the Art Natural Language Processing
trankit - Trankit is a Light-Weight Transformer-based Python Toolkit for Multilingual Natural Language Processing
DeBERTa - The implementation of DeBERTa
happy-transformer - Happy Transformer makes it easy to fine-tune and perform inference with NLP Transformer models.
adaptnlp - An easy to use Natural Language Processing library and framework for predicting, training, fine-tuning, and serving up state-of-the-art NLP models.
bertviz - BertViz: Visualize Attention in NLP Models (BERT, GPT2, BART, etc.)
TEAM - Our EMNLP 2022 paper on MCQA