Sentence Similarity
sentence-transformers
Safetensors
Transformers
roberta
feature-extraction
text-embeddings-inference
Instructions to use djsull/sentence-roberta-multitask with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use djsull/sentence-roberta-multitask with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("djsull/sentence-roberta-multitask") 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 djsull/sentence-roberta-multitask with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("djsull/sentence-roberta-multitask") model = AutoModel.from_pretrained("djsull/sentence-roberta-multitask", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload tokenizer
Browse files
README.md
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library_name: sentence-transformers
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pipeline_tag: sentence-similarity
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- sentence-transformers
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- feature-extraction
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- sentence-similarity
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- transformers
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# djsull/sentence-roberta-multitask
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library_name: sentence-transformers
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tags:
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- sentence-transformers
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- feature-extraction
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- sentence-similarity
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- transformers
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pipeline_tag: sentence-similarity
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---
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# djsull/sentence-roberta-multitask
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