Sentence Similarity
sentence-transformers
Safetensors
Transformers
roberta
feature-extraction
text-embeddings-inference
Instructions to use Nerdofdot/roberta-base_TM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Nerdofdot/roberta-base_TM with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Nerdofdot/roberta-base_TM") 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 Nerdofdot/roberta-base_TM with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Nerdofdot/roberta-base_TM") model = AutoModel.from_pretrained("Nerdofdot/roberta-base_TM") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5d15e458bef6cbc037efd1c9246f47181e802b4228152499eaf7871b88393368
- Size of remote file:
- 499 MB
- SHA256:
- 56090c7a563b8dc4ee751bfc585b961edd5c2f87fba069ff656ac3b01a3e8a11
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