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
File size: 731 Bytes
0c589a5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 | {
"_name_or_path": "./ft-v3.0.0",
"architectures": [
"BertModel"
],
"attention_probs_dropout_prob": 0.1,
"classifier_dropout": null,
"gradient_checkpointing": false,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 1024,
"id2label": {
"0": "LABEL_0"
},
"initializer_range": 0.02,
"intermediate_size": 4096,
"label2id": {
"LABEL_0": 0
},
"layer_norm_eps": 1e-12,
"max_position_embeddings": 512,
"model_type": "bert",
"num_attention_heads": 16,
"num_hidden_layers": 24,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"torch_dtype": "float32",
"transformers_version": "4.47.1",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 30522
}
|