Sentence Similarity
sentence-transformers
PyTorch
Transformers
mpnet
feature-extraction
text-embeddings-inference
Instructions to use JoBeer/all-mpnet-base-v2-eclass with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use JoBeer/all-mpnet-base-v2-eclass with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("JoBeer/all-mpnet-base-v2-eclass") 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] - Transformers
How to use JoBeer/all-mpnet-base-v2-eclass with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("JoBeer/all-mpnet-base-v2-eclass") model = AutoModel.from_pretrained("JoBeer/all-mpnet-base-v2-eclass", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Librarian Bot: Update dataset YAML metadata for model
This is a pull request to add a dataset, [`JoBeer/eclassTrainST`](https://huggingface.co/datasets/JoBeer/eclassTrainST), to the metadata for your model (defined in the `YAML` block of your model's `README.md`). The pull request was made by [librarian-bot](https://huggingface.co/librarian-bot) and used a combination of rules and/or machine learning to suggest this additional metadata. If this suggestion is incorrect, feel free to close this pull request. Librarian Bot was made by [@davanstrien](https://huggingface.co/davanstrien); feel free to get in touch with feedback.837ca22
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