Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use peter2000/tsdae_model_policy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use peter2000/tsdae_model_policy with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("peter2000/tsdae_model_policy") 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 peter2000/tsdae_model_policy with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("peter2000/tsdae_model_policy") model = AutoModel.from_pretrained("peter2000/tsdae_model_policy", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8f0ede2007e3173b6bf49108efb4c19b862c199e419161f882b6cda3272308fc
- Size of remote file:
- 438 MB
- SHA256:
- 31fd5da37815337ed02d0eeafac16b0f41cfcff69771f3c6fc10a34c96ff4753
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