Sentence Similarity
sentence-transformers
PyTorch
TensorBoard
Safetensors
Transformers
English
German
t5
text-embedding
embeddings
information-retrieval
beir
text-classification
language-model
text-clustering
text-semantic-similarity
text-evaluation
prompt-retrieval
text-reranking
feature-extraction
English
Sentence Similarity
natural_questions
ms_marco
fever
hotpot_qa
mteb
Instructions to use pascalhuerten/instructor-skillfit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use pascalhuerten/instructor-skillfit with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("pascalhuerten/instructor-skillfit") 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 pascalhuerten/instructor-skillfit with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("pascalhuerten/instructor-skillfit") model = AutoModel.from_pretrained("pascalhuerten/instructor-skillfit") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c6fd75fbd493e07c1f28d3e476d1b7af58f0fcee48ae5e0c75c68199f555ed65
- Size of remote file:
- 439 MB
- SHA256:
- d3d8811c2dba395b7e56a505adfeea6becd758654349aa42abe042520873e594
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