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 hkunlp/instructor-xl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use hkunlp/instructor-xl with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("hkunlp/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 hkunlp/instructor-xl with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hkunlp/instructor-xl") model = AutoModel.from_pretrained("hkunlp/instructor-xl", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download sentence_bert_config.json from hkunlp/instructor-xl: direct link, hf CLI and curl.
- Browser
- Download file 53 Bytes
-
https://huggingface.co/hkunlp/instructor-xl/resolve/main/sentence_bert_config.json
- Command line
-
hf download hf://hkunlp/instructor-xl/sentence_bert_config.json
-
curl -L -o sentence_bert_config.json https://huggingface.co/hkunlp/instructor-xl/resolve/main/sentence_bert_config.json
53 Bytes
| { | |
| "max_seq_length": 512, | |
| "do_lower_case": false | |
| } |