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
labor-specter
Browse fileslabor-specter trained by labor_triplet
- tokenizer_config.json +14 -0
tokenizer_config.json
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{
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"cls_token": "[CLS]",
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"do_lower_case": true,
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"mask_token": "[MASK]",
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"model_max_length": 512,
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"name_or_path": "/mnt/user2/.cache/torch/sentence_transformers/seongwoon_LAbert-triplet/",
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"special_tokens_map_file": null,
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"strip_accents": null,
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"tokenize_chinese_chars": true,
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"tokenizer_class": "BertTokenizer",
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"unk_token": "[UNK]"
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}
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