converse
PLOD-AbbreviationDetection
| converse | PLOD-AbbreviationDetection | |
|---|---|---|
| 6 | 1 | |
| 176 | 9 | |
| 0.0% | - | |
| 0.0 | 0.0 | |
| 11 months ago | over 1 year ago | |
| Jupyter Notebook | Jupyter Notebook | |
| Apache License 2.0 | Creative Commons Attribution Share Alike 4.0 |
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converse
- Pyconverse – Conversational transcript text analysis library
-
[P] Pyconverse - Conversational Text transcript analysis library
Github project link: pyconverse
- GitHub Project Showcase: Conversational Transcript Analysis using various NLP techniques
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PyConverse: Conversational Text Transcript Analysis library.
Github project link: https://github.com/AnjanaRita/converse
- Conversational Text analysis library in Python: PyConverse
PLOD-AbbreviationDetection
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Clustering to find abbreviations
Finally, the main problem with unsupervised learning is that you won't be able to reliably measure system performance or improvement. In my view, any time you can spend annotating and collecting data for a (semi-)supervised solution will be well-spent. Existing datasets can also get you started with model development, such as https://github.com/surrey-nlp/PLOD-AbbreviationDetection. Once you have a good model on a conventional dataset, you should be able to start generalizing it to your specific task/dataset.
What are some alternatives?
FinBERT - A Pretrained BERT Model for Financial Communications. https://arxiv.org/abs/2006.08097
hate-speech-and-offensive-language - Repository for the paper "Automated Hate Speech Detection and the Problem of Offensive Language", ICWSM 2017
soxan - Wav2Vec for speech recognition, classification, and audio classification
ThoughtSource - A central, open resource for data and tools related to chain-of-thought reasoning in large language models. Developed @ Samwald research group: https://samwald.info/
Speech-Emotion-Classification-by-utilizing-a-Convolutional-Neural-Network - Enhancing Speech Emotion Classification with CNNs: This project seeks to overcome the limitations of traditional approaches and improve the accuracy of emotion recognition. CNNs automatically extract features from speech signals, capturing complex patterns and nuances, leading to enhanced performance compared to traditional methods.
nlp - Repository for all things Natural Language Processing
TopMost - A Topic Modeling System Toolkit
transformers-interpret - Model explainability that works seamlessly with 🤗 transformers. Explain your transformers model in just 2 lines of code.
browser-ml-inference - Edge Inference in Browser with Transformer NLP model
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.
nlphose - Enables creation of complex NLP pipelines in seconds, for processing static files or streaming text, using a set of simple command line tools. Perform multiple operation on text like NER, Sentiment Analysis, Chunking, Language Identification, Q&A, 0-shot Classification and more by executing a single command in the terminal. Can be used as a low code or no code Natural Language Processing solution. Also works with Kubernetes and PySpark !