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") - Notebooks
- Google Colab
- Kaggle
Pushing tsdae fine tuned model
Browse filesModel trained on 500k policy sentencey
- pytorch_model.bin +3 -0
pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:31fd5da37815337ed02d0eeafac16b0f41cfcff69771f3c6fc10a34c96ff4753
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size 438000173
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