Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
Generated from Trainer
dataset_size:14737
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use JianLiao/spectrum-doc-fine-tuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use JianLiao/spectrum-doc-fine-tuned with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("JianLiao/spectrum-doc-fine-tuned") sentences = [ "Represent this sentence for searching relevant passages: What are some best practices for ensuring images in horizontal cards are visually appealing despite being cropped to fit a square format?", "Tree view\nUsage guidelines\nHorizontal scrolling: If you have a layout that doesn't allow for users to adjust the width of the container for a tree view, allow them to horizontally scroll in order to see the full depth of the hierarchy.\nDo: Allow horizontal scrolling in a fixed layout.\n", "Cards\nOptions\nVertical or horizontal : Standard cards can be laid out vertically (components are organized in a column) or horizontally (components are organized in a row).\n\nHorizontal cards always have a square preview, and the image is cropped to fit inside the square. These can only be laid out in a tile grid where every card is the same size.", "Alert dialog\nBehaviors\nButton group overflow: An alert dialog can have up to 3 buttons. When horizontal space is limited, button groups stack vertically. They should appear in ascending order based on importance, with the most critical action at the bottom." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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| "do_lower_case": true, | |
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| "max_length": 512, | |
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| "pad_token": "[PAD]", | |
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| "padding_side": "right", | |
| "sep_token": "[SEP]", | |
| "stride": 0, | |
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| "tokenize_chinese_chars": true, | |
| "tokenizer_class": "BertTokenizer", | |
| "truncation_side": "right", | |
| "truncation_strategy": "longest_first", | |
| "unk_token": "[UNK]" | |
| } | |