Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:1760
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use cyberbabooshka/bge_large_ft1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use cyberbabooshka/bge_large_ft1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("cyberbabooshka/bge_large_ft1") sentences = [ "What is the relationship between the x- and y-coordinates in a linear relationship, and how can this relationship be represented visually on a graph?", "\"A linear relationship is a relationship between variables such that when plotted on a coordinate plane, the points lie on a line.\" Additionally, \"You can think of a line, then, as a collection of an infinite number of individual points that share the same mathematical relationship.\"", "\"A 'model' is a situation-specific description of a phenomenon based on a theory, that allows us to make a specific prediction.\" and \"In physics, it is particularly important to distinguish between these two terms. A model provides an immediate understanding of something based on a theory.\"", "\"Use capital letters to denote sets, $A,B, C, X, Y$ etc. [...] if you stick with these conventions people reading your work (including the person marking your exams) will know — 'Oh $A$ is that set they are talking about' and '$a$ is an element of that set.'\"" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| { | |
| "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": "4.52.3", | |
| "type_vocab_size": 2, | |
| "use_cache": true, | |
| "vocab_size": 30522 | |
| } | |