Sentence Similarity
sentence-transformers
BERTopic
English
topic-modeling
unsupervised-learning
document-clustering
document-archiving
human-in-the-loop
Instructions to use patpatrick/archivAI_document_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use patpatrick/archivAI_document_classification with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("patpatrick/archivAI_document_classification") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - BERTopic
How to use patpatrick/archivAI_document_classification with BERTopic:
from bertopic import BERTopic model = BERTopic.load("patpatrick/archivAI_document_classification") - Notebooks
- Google Colab
- Kaggle
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Download README.md from patpatrick/archivAI_document_classification: direct link, hf CLI and curl.
- Browser
- Download file 279 Bytes
-
https://huggingface.co/patpatrick/archivAI_document_classification/resolve/main/README.md
- Command line
-
hf download hf://patpatrick/archivAI_document_classification/README.md
-
curl -L -o README.md https://huggingface.co/patpatrick/archivAI_document_classification/resolve/main/README.md
279 Bytes
metadata
license: mit
language:
- en
base_model:
- sentence-transformers/all-MiniLM-L6-v2
pipeline_tag: sentence-similarity
library_name: sentence-transformers
tags:
- bertopic
- topic-modeling
- unsupervised-learning
- document-clustering
- document-archiving
- human-in-the-loop