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
| { | |
| "_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 | |
| } | |