Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
sts
embeddings
symmetric-augmentation
fine-tuned
text-embeddings-inference
Instructions to use blueprint-ai/SymSTS-MiniLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use blueprint-ai/SymSTS-MiniLM with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("blueprint-ai/SymSTS-MiniLM") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Upload 9 files
Browse files- 1_Pooling/config.json +5 -0
- 2_Normalize/config.json +4 -0
- config.json +30 -0
- config_sentence_transformers.json +14 -0
- model.safetensors +3 -0
- modules.json +20 -0
- sentence_bert_config.json +10 -0
- tokenizer.json +0 -0
- tokenizer_config.json +24 -0
1_Pooling/config.json
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{
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"embedding_dimension": 384,
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"pooling_mode": "mean",
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"include_prompt": true
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}
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2_Normalize/config.json
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{
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"module_input_name": "sentence_embedding",
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"module_output_name": "sentence_embedding"
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}
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config.json
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{
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"add_cross_attention": false,
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"architectures": [
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"BertModel"
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],
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"attention_probs_dropout_prob": 0.1,
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"bos_token_id": null,
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"classifier_dropout": null,
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"dtype": "float32",
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"eos_token_id": null,
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"gradient_checkpointing": false,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 384,
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"initializer_range": 0.02,
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"intermediate_size": 1536,
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"is_decoder": false,
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"layer_norm_eps": 1e-12,
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"max_position_embeddings": 512,
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"model_type": "bert",
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"num_attention_heads": 12,
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"num_hidden_layers": 6,
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"pad_token_id": 0,
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"position_embedding_type": "absolute",
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"tie_word_embeddings": true,
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"transformers_version": "5.15.1",
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"type_vocab_size": 2,
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"use_cache": false,
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"vocab_size": 30522
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}
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config_sentence_transformers.json
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{
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"__version__": {
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"pytorch": "2.13.0+cu130",
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"sentence_transformers": "6.0.0",
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"transformers": "5.15.1"
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},
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"default_prompt_name": null,
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"model_type": "SentenceTransformer",
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"prompts": {
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"document": "",
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"query": ""
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},
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"similarity_fn_name": "cosine"
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:66cf6335eaeb7e9124a46f8fd1d4093fc41f90b15b849d7f187506dbe429e02f
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size 90864192
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modules.json
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[
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{
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"idx": 0,
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"name": "0",
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"path": "",
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"type": "sentence_transformers.base.modules.transformer.Transformer"
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},
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{
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"idx": 1,
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"name": "1",
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"path": "1_Pooling",
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"type": "sentence_transformers.sentence_transformer.modules.pooling.Pooling"
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},
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{
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"idx": 2,
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"name": "2",
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"path": "2_Normalize",
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"type": "sentence_transformers.base.modules.normalize.Normalize"
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}
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]
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sentence_bert_config.json
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{
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"transformer_task": "feature-extraction",
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"modality_config": {
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"text": {
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"method": "forward",
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"method_output_name": "last_hidden_state"
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}
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},
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"module_output_name": "token_embeddings"
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}
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tokenizer.json
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tokenizer_config.json
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{
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"backend": "tokenizers",
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"cls_token": "[CLS]",
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"do_basic_tokenize": true,
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"do_lower_case": true,
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"is_local": false,
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"local_files_only": false,
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"mask_token": "[MASK]",
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"max_length": 128,
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"model_max_length": 256,
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"never_split": null,
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"pad_to_multiple_of": null,
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"pad_token": "[PAD]",
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"pad_token_type_id": 0,
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"padding_side": "right",
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"sep_token": "[SEP]",
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"stride": 0,
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"strip_accents": null,
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"tokenize_chinese_chars": true,
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"tokenizer_class": "BertTokenizer",
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"truncation_side": "right",
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"truncation_strategy": "longest_first",
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"unk_token": "[UNK]"
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}
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