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IrisCHEN04
/
Sentence-Transformer_Roberta_Reddit_ed

Sentence Similarity
sentence-transformers
Safetensors
roberta
feature-extraction
dense
Generated from Trainer
dataset_size:6000
loss:ContrastiveLoss
Eval Results (legacy)
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use IrisCHEN04/Sentence-Transformer_Roberta_Reddit_ed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use IrisCHEN04/Sentence-Transformer_Roberta_Reddit_ed with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("IrisCHEN04/Sentence-Transformer_Roberta_Reddit_ed")
    
    sentences = [
        " \"you only have minor depression \\[I didn't\\], why are you acting so miserable?\" Or by reminding me of the damage I was causing my loved ones like saying \"you're selfish, cruel and tearing this family apart",
        " (I have lost my period a couple more times in the past during my ED and in early recovery",
        " I find myself angry when people keep commenting and questioning me about eating and weight loss!! At first I was angry because they were acting like I was starving myself, but now I'm seeing that maybe I am",
        " My now two-year long treatment is already being held back by my attachment to my eating disorder, and I know a hospitalisation is just going trigger me into being more defensive of it"
    ]
    embeddings = model.encode(sentences)
    
    similarities = model.similarity(embeddings, embeddings)
    print(similarities.shape)
    # [4, 4]
  • Notebooks
  • Google Colab
  • Kaggle
Sentence-Transformer_Roberta_Reddit_ed
1.43 GB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 2 commits
IrisCHEN04's picture
IrisCHEN04
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  • .ipynb_checkpoints
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  • 1_Pooling
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  • .gitattributes
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  • README.md
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  • config.json
    717 Bytes
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  • config_sentence_transformers.json
    283 Bytes
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  • merges.txt
    456 kB
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  • model.safetensors
    1.42 GB
    xet
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  • modules.json
    349 Bytes
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  • sentence_bert_config.json
    57 Bytes
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  • special_tokens_map.json
    964 Bytes
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  • tokenizer.json
    3.56 MB
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  • tokenizer_config.json
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  • vocab.json
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