Sentence Similarity
sentence-transformers
PyTorch
ONNX
Safetensors
OpenVINO
Transformers
English
roberta
fill-mask
feature-extraction
text-embeddings-inference
Instructions to use sentence-transformers/all-roberta-large-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sentence-transformers/all-roberta-large-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sentence-transformers/all-roberta-large-v1") 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 sentence-transformers/all-roberta-large-v1 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/all-roberta-large-v1") model = AutoModelForMaskedLM.from_pretrained("sentence-transformers/all-roberta-large-v1", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Remove deprecated (SEB) evaluation results section
Browse files
README.md
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print(sentence_embeddings)
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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/all-roberta-large-v1)
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## Background
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print(sentence_embeddings)
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```
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## Background
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