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
Inference Endpoints
pipeline_tag: sentence-similarity | |
language: en | |
license: apache-2.0 | |
tags: | |
- sentence-transformers | |
- feature-extraction | |
- sentence-similarity | |
- transformers | |
# hku-nlp/instructor-xl | |
This is a general embedding model: It maps sentences & paragraphs to a 768 dimensional dense vector space. | |
The model was trained on diverse tasks. | |
It takes customized instructions and text inputs, and generates task-specific embeddings for general purposes, e.g., information retrieval, classification, clustering, etc. | |
``` | |
git clone https://github.com/HKUNLP/instructor-embedding | |
cd sentence-transformers | |
pip instal -e . | |
``` | |
Then you can use the model like this: | |
```python | |
from sentence_transformers import SentenceTransformer | |
sentence = "3D ActionSLAM: wearable person tracking in multi-floor environments" | |
instruction = "Represent the Science title; Input:" | |
model = SentenceTransformer('instructor-xl') | |
embeddings = model.encode([[instruction,sentence,0]]) | |
print(embeddings) | |
``` |