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
Push jkswin/YGO_MiniLM
Browse filesModel trained on Yugioh Card Text and Yugitube Comments
- config.json +26 -0
config.json
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{
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"_name_or_path": "sentence-transformers/paraphrase-MiniLM-L3-v2",
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"architectures": [
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"BertForMaskedLM"
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],
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"attention_probs_dropout_prob": 0.1,
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"classifier_dropout": null,
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"gradient_checkpointing": false,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 384,
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"initializer_range": 0.02,
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"intermediate_size": 1536,
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"layer_norm_eps": 1e-12,
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"max_position_embeddings": 512,
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"model_type": "bert",
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"num_attention_heads": 12,
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"num_hidden_layers": 3,
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"pad_token_id": 0,
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"position_embedding_type": "absolute",
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"torch_dtype": "float32",
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"transformers_version": "4.29.2",
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"type_vocab_size": 2,
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"use_cache": true,
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"vocab_size": 30522
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}
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