README.md

metadata

language:
  - en
license: apache-2.0
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:50
  - loss:MultipleNegativesRankingLoss
base_model: sentence-transformers/all-MiniLM-L6-v2
widget:
  - source_sentence: Two men on bicycles competing in a race.
    sentences:
      - People are riding bikes.
      - A woman is doing a cartwheel.
      - A few people are catching fish.
  - source_sentence: >-
      A man selling donuts to a customer during a world exhibition event held in
      the city of Angeles
    sentences:
      - An Indian woman is doing her laundry in a lake.
      - A man selling donuts to a customer.
      - A woman drinks her coffee in a small cafe.
  - source_sentence: Kids are on a amusement ride.
    sentences:
      - A car is broke down on the side of the road.
      - A man is wearing a blue shirt
      - Kids ride an amusement ride.
  - source_sentence: >-
      Two young children in blue jerseys, one with the number 9 and one with the
      number 2 are standing on wooden steps in a bathroom and washing their
      hands in a sink.
    sentences:
      - A woman is doing a cartwheel.
      - Two kids in jackets walk to school.
      - Two kids in numbered jerseys wash their hands.
  - source_sentence: Two women having drinks and smoking cigarettes at the bar.
    sentences:
      - boys play football
      - Three women are at a bar.
      - Two women are at a bar.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
  - cosine_accuracy@1
  - cosine_accuracy@3
  - cosine_accuracy@5
  - cosine_accuracy@10
  - cosine_precision@1
  - cosine_precision@3
  - cosine_precision@5
  - cosine_precision@10
  - cosine_recall@1
  - cosine_recall@3
  - cosine_recall@5
  - cosine_recall@10
  - cosine_ndcg@10
  - cosine_mrr@10
  - cosine_map@100
model-index:
  - name: all-MiniLM-L6-v2 finetuned on AllNLI
    results:
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoMSMARCO
          type: NanoMSMARCO
        metrics:
          - type: cosine_accuracy@1
            value: 0.36
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.52
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.58
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.36
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.1733333333333333
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.11599999999999999
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.36
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.52
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.58
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.5537649594026555
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.47934920634920636
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.4904052935766491
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoNFCorpus
          type: NanoNFCorpus
        metrics:
          - type: cosine_accuracy@1
            value: 0.4
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.6
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.62
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.72
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.4
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.35999999999999993
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.324
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.276
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.03458800957047009
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.06249836236458624
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.08046568973676278
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.13259147712553063
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.32368179953782333
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.4918571428571428
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.1389531634193327
            name: Cosine Map@100
      - task:
          type: nano-beir
          name: Nano BEIR
        dataset:
          name: NanoBEIR mean
          type: NanoBEIR_mean
        metrics:
          - type: cosine_accuracy@1
            value: 0.38
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.56
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.6
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.76
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.38
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2666666666666666
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.22
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.17800000000000002
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.19729400478523504
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.29124918118229315
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.3302328448683814
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.46629573856276535
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.4387233794702394
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.4856031746031746
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.3146792284979909
            name: Cosine Map@100

all-MiniLM-L6-v2 finetuned on AllNLI

This is a sentence-transformers model finetuned from sentence-transformers/all-MiniLM-L6-v2 on the all-nli dataset. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for retrieval.

Model Details

Model Description

• Model Type: Sentence Transformer
• Base model: sentence-transformers/all-MiniLM-L6-v2
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 384 dimensions
• Similarity Function: Cosine Similarity
• Supported Modality: Text
• Training Dataset:
  • all-nli
• Language: en
• License: apache-2.0

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'BertModel'})
  (1): Pooling({'embedding_dimension': 384, 'pooling_mode': 'mean', 'include_prompt': True})
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("Hyperakan/all-MiniLM-L6-v2-smoke")
# Run inference
queries = [
    'Two women having drinks and smoking cigarettes at the bar.',
]
documents = [
    'Two women are at a bar.',
    'Three women are at a bar.',
    'boys play football',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 384] [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[ 0.7893,  0.6667, -0.1284]])

Evaluation

Metrics

Information Retrieval

• Datasets: NanoMSMARCO and NanoNFCorpus
• Evaluated with InformationRetrievalEvaluator

Metric	NanoMSMARCO	NanoNFCorpus
cosine_accuracy@1	0.36	0.4
cosine_accuracy@3	0.52	0.6
cosine_accuracy@5	0.58	0.62
cosine_accuracy@10	0.8	0.72
cosine_precision@1	0.36	0.4
cosine_precision@3	0.1733	0.36
cosine_precision@5	0.116	0.324
cosine_precision@10	0.08	0.276
cosine_recall@1	0.36	0.0346
cosine_recall@3	0.52	0.0625
cosine_recall@5	0.58	0.0805
cosine_recall@10	0.8	0.1326
cosine_ndcg@10	0.5538	0.3237
cosine_mrr@10	0.4793	0.4919
cosine_map@100	0.4904	0.139

Nano BEIR

• Dataset: NanoBEIR_mean
• Evaluated with NanoBEIREvaluator with these parameters:

  {
      "dataset_names": [
          "msmarco",
          "nfcorpus"
      ],
      "dataset_id": "sentence-transformers/NanoBEIR-en"
  }
  

Metric	Value
cosine_accuracy@1	0.38
cosine_accuracy@3	0.56
cosine_accuracy@5	0.6
cosine_accuracy@10	0.76
cosine_precision@1	0.38
cosine_precision@3	0.2667
cosine_precision@5	0.22
cosine_precision@10	0.178
cosine_recall@1	0.1973
cosine_recall@3	0.2912
cosine_recall@5	0.3302
cosine_recall@10	0.4663
cosine_ndcg@10	0.4387
cosine_mrr@10	0.4856
cosine_map@100	0.3147

Training Details

Training Dataset

all-nli

• Dataset: all-nli
• Size: 50 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 50 samples:

  	anchor	positive	negative
  type	string	string	string
  modality	text	text	text
  details	min: 8 tokens mean: 21.7 tokens max: 30 tokens	min: 6 tokens mean: 10.4 tokens max: 18 tokens	min: 5 tokens mean: 13.34 tokens max: 30 tokens

• Samples:

  anchor	positive	negative
  A person on a horse jumps over a broken down airplane.	A person is outdoors, on a horse.	A person is at a diner, ordering an omelette.
  Children smiling and waving at camera	There are children present	The kids are frowning
  A boy is jumping on skateboard in the middle of a red bridge.	The boy does a skateboarding trick.	The boy skates down the sidewalk.

• Loss: MultipleNegativesRankingLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim",
      "gather_across_devices": false,
      "directions": [
          "query_to_doc"
      ],
      "partition_mode": "joint",
      "hardness_mode": null,
      "hardness_strength": 0.0
  }
  

Evaluation Dataset

all-nli

• Dataset: all-nli
• Size: 20 evaluation samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 20 samples:

  	anchor	positive	negative
  type	string	string	string
  modality	text	text	text
  details	min: 9 tokens mean: 19.3 tokens max: 36 tokens	min: 5 tokens mean: 9.55 tokens max: 14 tokens	min: 5 tokens mean: 10.05 tokens max: 15 tokens

• Samples:

  anchor	positive	negative
  Two women are embracing while holding to go packages.	Two woman are holding packages.	The men are fighting outside a deli.
  Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.	Two kids in numbered jerseys wash their hands.	Two kids in jackets walk to school.
  A man selling donuts to a customer during a world exhibition event held in the city of Angeles	A man selling donuts to a customer.	A woman drinks her coffee in a small cafe.

• Loss: MultipleNegativesRankingLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim",
      "gather_across_devices": false,
      "directions": [
          "query_to_doc"
      ],
      "partition_mode": "joint",
      "hardness_mode": null,
      "hardness_strength": 0.0
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• per_device_train_batch_size: 16
• per_device_eval_batch_size: 16
• learning_rate: 2e-05
• weight_decay: 0.01
• num_train_epochs: 1
• max_steps: 1
• warmup_ratio: 0.1
• seed: 12
• bf16: True
• load_best_model_at_end: True
• batch_sampler: no_duplicates

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• prediction_loss_only: True
• per_device_train_batch_size: 16
• per_device_eval_batch_size: 16
• per_gpu_train_batch_size: None
• per_gpu_eval_batch_size: None
• gradient_accumulation_steps: 1
• eval_accumulation_steps: None
• torch_empty_cache_steps: None
• learning_rate: 2e-05
• weight_decay: 0.01
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• max_grad_norm: 1.0
• num_train_epochs: 1
• max_steps: 1
• lr_scheduler_type: linear
• lr_scheduler_kwargs: None
• warmup_ratio: 0.1
• warmup_steps: 0
• log_level: passive
• log_level_replica: warning
• log_on_each_node: True
• logging_nan_inf_filter: True
• save_safetensors: True
• save_on_each_node: False
• save_only_model: False
• restore_callback_states_from_checkpoint: False
• no_cuda: False
• use_cpu: False
• use_mps_device: False
• seed: 12
• data_seed: None
• jit_mode_eval: False
• bf16: True
• fp16: False
• fp16_opt_level: O1
• half_precision_backend: auto
• bf16_full_eval: False
• fp16_full_eval: False
• tf32: None
• local_rank: 0
• ddp_backend: None
• tpu_num_cores: None
• tpu_metrics_debug: False
• debug: []
• dataloader_drop_last: False
• dataloader_num_workers: 0
• dataloader_prefetch_factor: None
• past_index: -1
• disable_tqdm: False
• remove_unused_columns: True
• label_names: None
• load_best_model_at_end: True
• ignore_data_skip: False
• fsdp: []
• fsdp_min_num_params: 0
• fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
• fsdp_transformer_layer_cls_to_wrap: None
• accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
• parallelism_config: None
• deepspeed: None
• label_smoothing_factor: 0.0
• optim: adamw_torch
• optim_args: None
• adafactor: False
• group_by_length: False
• length_column_name: length
• project: huggingface
• trackio_space_id: trackio
• ddp_find_unused_parameters: None
• ddp_bucket_cap_mb: None
• ddp_broadcast_buffers: False
• dataloader_pin_memory: True
• dataloader_persistent_workers: False
• skip_memory_metrics: True
• use_legacy_prediction_loop: False
• push_to_hub: False
• resume_from_checkpoint: None
• hub_model_id: None
• hub_strategy: every_save
• hub_private_repo: None
• hub_always_push: False
• hub_revision: None
• gradient_checkpointing: False
• gradient_checkpointing_kwargs: None
• include_inputs_for_metrics: False
• include_for_metrics: []
• eval_do_concat_batches: True
• fp16_backend: auto
• push_to_hub_model_id: None
• push_to_hub_organization: None
• mp_parameters:
• auto_find_batch_size: False
• full_determinism: False
• torchdynamo: None
• ray_scope: last
• ddp_timeout: 1800
• torch_compile: False
• torch_compile_backend: None
• torch_compile_mode: None
• include_tokens_per_second: False
• include_num_input_tokens_seen: no
• neftune_noise_alpha: None
• optim_target_modules: None
• batch_eval_metrics: False
• eval_on_start: False
• use_liger_kernel: False
• liger_kernel_config: None
• eval_use_gather_object: False
• average_tokens_across_devices: True
• prompts: None
• batch_sampler: no_duplicates
• multi_dataset_batch_sampler: proportional
• router_mapping: {}
• learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss	Validation Loss	NanoMSMARCO_cosine_ndcg@10	NanoNFCorpus_cosine_ndcg@10	NanoBEIR_mean_cosine_ndcg@10
-1	-1	-	-	0.5538	0.3237	0.4387
0.25	1	0.5675	0.1029	0.554	0.3235	0.4387
-1	-1	-	-	0.5538	0.3237	0.4387

• The bold row denotes the saved checkpoint.

Training Time

• Training: 12.6 seconds
• Evaluation: 8.9 seconds
• Total: 21.5 seconds

Framework Versions

• Python: 3.12.13
• Sentence Transformers: 5.6.0
• Transformers: 4.57.6
• PyTorch: 2.5.1+cu121
• Accelerate: 1.14.0
• Datasets: 5.0.0
• Tokenizers: 0.22.2

Citation

BibTeX

Sentence Transformers

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}

MultipleNegativesRankingLoss

@misc{oord2019representationlearningcontrastivepredictive,
      title={Representation Learning with Contrastive Predictive Coding},
      author={Aaron van den Oord and Yazhe Li and Oriol Vinyals},
      year={2019},
      eprint={1807.03748},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/1807.03748},
}