Sentence Similarity
sentence-transformers
PyTorch
Transformers
English
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
Eval Results (legacy)
Instructions to use retrainai/instructor-xl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use retrainai/instructor-xl with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("retrainai/instructor-xl") 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 retrainai/instructor-xl with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("retrainai/instructor-xl") model = AutoModel.from_pretrained("retrainai/instructor-xl") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ca1b0ab47b7aa96601e95e9a5f16ec9045d7d29f0ae3d05c328a8aec023cb522
- Size of remote file:
- 4.96 GB
- SHA256:
- 9b065dd74cb4423b155cada6ebf11f97cdcb67b2f10841a8a810d674b10bbf99
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