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:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("retrainai/instructor-xl") model = AutoModel.from_pretrained("retrainai/instructor-xl", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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embeddings = model.encode(sentences)
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print(embeddings)
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```
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The model requires sentence-transformers version 2.2.0 or newer.
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## Evaluation Results
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For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/gtr-t5-xl)
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## Citing & Authors
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If you find this model helpful, please cite the respective publication:
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[Large Dual Encoders Are Generalizable Retrievers](https://arxiv.org/abs/2112.07899)
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embeddings = model.encode(sentences)
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print(embeddings)
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```
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