Sentence Similarity
sentence-transformers
PyTorch
Transformers
mpnet
feature-extraction
text-embeddings-inference
Instructions to use teven/cross_all_bs320_vanilla_finetuned_WebNLG2020_data_coverage with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use teven/cross_all_bs320_vanilla_finetuned_WebNLG2020_data_coverage with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("teven/cross_all_bs320_vanilla_finetuned_WebNLG2020_data_coverage") 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 teven/cross_all_bs320_vanilla_finetuned_WebNLG2020_data_coverage with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("teven/cross_all_bs320_vanilla_finetuned_WebNLG2020_data_coverage") model = AutoModel.from_pretrained("teven/cross_all_bs320_vanilla_finetuned_WebNLG2020_data_coverage") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 35f1c83e8b1c975434259c99d6539b2c5d8826a83f8b727414c3223425f16b94
- Size of remote file:
- 438 MB
- SHA256:
- 502d83fcb0b8e27a2a039356ef0ac50e6aa2ad2d58ea8a4e34a56b17229af84f
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