Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use seongwoon/relation-learning-step2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use seongwoon/relation-learning-step2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("seongwoon/relation-learning-step2") 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 seongwoon/relation-learning-step2 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("seongwoon/relation-learning-step2") model = AutoModel.from_pretrained("seongwoon/relation-learning-step2") - Notebooks
- Google Colab
- Kaggle
Upload 1_Pooling with huggingface_hub
Browse files- 1_Pooling/config.json +7 -0
1_Pooling/config.json
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{
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"word_embedding_dimension": 768,
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"pooling_mode_cls_token": false,
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"pooling_mode_mean_tokens": true,
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"pooling_mode_max_tokens": false,
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"pooling_mode_mean_sqrt_len_tokens": false
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}
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