Feature Extraction
sentence-transformers
Safetensors
English
modernbert
multi-vector
colbert
late-interaction
Generated from Trainer
dataset_size:99000
loss:CachedMultiVectorMultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use tomaarsen/multivector-ModernBERT-base-msmarco-cached-contrastive-no-query-expansion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use tomaarsen/multivector-ModernBERT-base-msmarco-cached-contrastive-no-query-expansion with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("tomaarsen/multivector-ModernBERT-base-msmarco-cached-contrastive-no-query-expansion") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
Add new MultiVectorEncoder model
Browse files- 1_Dense/config.json +8 -0
- 1_Dense/model.safetensors +3 -0
- 2_MultiVectorMask/config.json +4 -0
- 3_Normalize/config.json +4 -0
- README.md +0 -0
- config.json +78 -0
- config_sentence_transformers.json +14 -0
- model.safetensors +3 -0
- modules.json +26 -0
- sentence_bert_config.json +10 -0
- tokenizer.json +0 -0
- tokenizer_config.json +17 -0
1_Dense/config.json
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{
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"in_features": 768,
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"out_features": 128,
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"bias": false,
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"activation_function": "torch.nn.modules.linear.Identity",
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"module_input_name": "token_embeddings",
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"module_output_name": "token_embeddings"
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}
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1_Dense/model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:1ce6a3877a7f57ff4d7637547b94914cca53a443d0c515a957b7a1d19da7ee75
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size 393304
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2_MultiVectorMask/config.json
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{
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"skiplist_words": [],
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"keep_only_token_ids": null
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}
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3_Normalize/config.json
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{
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"module_input_name": "token_embeddings",
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"module_output_name": "token_embeddings"
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}
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README.md
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config.json
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{
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"architectures": [
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"ModernBertModel"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": null,
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"classifier_activation": "gelu",
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"classifier_bias": false,
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"classifier_dropout": 0.0,
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"classifier_pooling": "mean",
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"cls_token_id": 50281,
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"decoder_bias": true,
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"deterministic_flash_attn": false,
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"dtype": "float32",
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"embedding_dropout": 0.0,
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"eos_token_id": null,
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"global_attn_every_n_layers": 3,
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"gradient_checkpointing": false,
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"hidden_activation": "gelu",
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"hidden_size": 768,
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"initializer_cutoff_factor": 2.0,
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"initializer_range": 0.02,
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"intermediate_size": 1152,
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"layer_norm_eps": 1e-05,
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"layer_types": [
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"full_attention",
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"sliding_attention",
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"sliding_attention",
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"full_attention",
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"sliding_attention",
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"sliding_attention",
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"full_attention",
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"sliding_attention",
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"sliding_attention",
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"full_attention",
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"sliding_attention",
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"sliding_attention",
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"full_attention",
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"sliding_attention",
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"sliding_attention",
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"full_attention",
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"sliding_attention",
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"sliding_attention",
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"full_attention",
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"sliding_attention",
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"sliding_attention",
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"full_attention"
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],
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"local_attention": 128,
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"max_position_embeddings": 8192,
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"mlp_bias": false,
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"mlp_dropout": 0.0,
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"model_type": "modernbert",
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"norm_bias": false,
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"norm_eps": 1e-05,
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"num_attention_heads": 12,
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"num_hidden_layers": 22,
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"pad_token_id": 50283,
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"position_embedding_type": "absolute",
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"rope_parameters": {
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"full_attention": {
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"rope_theta": 160000.0,
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"rope_type": "default"
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},
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"sliding_attention": {
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"rope_theta": 10000.0,
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"rope_type": "default"
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}
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},
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"sep_token_id": 50282,
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"sparse_pred_ignore_index": -100,
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"sparse_prediction": false,
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"tie_word_embeddings": true,
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"transformers_version": "5.13.1",
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"use_cache": false,
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"vocab_size": 50368
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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": "2.10.0+cu128",
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"sentence_transformers": "5.7.0.dev0",
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"transformers": "5.13.1"
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},
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"default_prompt_name": null,
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"model_type": "MultiVectorEncoder",
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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": "maxsim"
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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:b99d041116c2b4a2f25c234158941cd9546e3057bd626dea3bc705c612c4a303
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size 596070136
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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_Dense",
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"type": "sentence_transformers.base.modules.dense.Dense"
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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_MultiVectorMask",
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"type": "sentence_transformers.multi_vector_encoder.modules.multi_vector_mask.MultiVectorMask"
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},
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{
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"idx": 3,
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"name": "3",
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"path": "3_Normalize",
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"type": "sentence_transformers.sentence_transformer.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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"clean_up_tokenization_spaces": true,
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"cls_token": "[CLS]",
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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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"model_input_names": [
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"input_ids",
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"attention_mask"
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],
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"model_max_length": 8192,
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"tokenizer_class": "TokenizersBackend",
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"unk_token": "[UNK]"
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}
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