Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use gubartz/st_scibert_abstruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use gubartz/st_scibert_abstruct with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("gubartz/st_scibert_abstruct") 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 gubartz/st_scibert_abstruct with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("gubartz/st_scibert_abstruct") model = AutoModel.from_pretrained("gubartz/st_scibert_abstruct") - Notebooks
- Google Colab
- Kaggle
File size: 190 Bytes
409c35b | 1 2 3 4 5 6 7 | {
"word_embedding_dimension": 768,
"pooling_mode_cls_token": false,
"pooling_mode_mean_tokens": true,
"pooling_mode_max_tokens": false,
"pooling_mode_mean_sqrt_len_tokens": false
} |