Sentence Similarity
sentence-transformers
PyTorch
Transformers
English
bert
fill-mask
feature-extraction
domain-specific
text-embeddings-inference
Instructions to use jkswin/YGO_MiniLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use jkswin/YGO_MiniLM with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("jkswin/YGO_MiniLM") 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 jkswin/YGO_MiniLM with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("jkswin/YGO_MiniLM") model = AutoModelForMaskedLM.from_pretrained("jkswin/YGO_MiniLM") - Notebooks
- Google Colab
- Kaggle
[DOCS] Remove mask examples in favor of sentence sim task
Browse files
README.md
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- sentence-similarity
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- transformers
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- domain-specific
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widget:
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- text: Marshmallon should be [MASK]. It allows a 1 card FTK.
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example_title: Banlist Speculation
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- text: 'Once per [MASK]: You can toss a coin and call it.'
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example_title: Time Wizard
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- text: You [MASK] so hard on turn 2.
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example_title: Misplay
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library_name: sentence-transformers
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- sentence-similarity
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library_name: sentence-transformers
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