ayaat / mpnet-base-all-mqp-binary

huggingface.co
Total runs: 104
24-hour runs: -1
7-day runs: 33
30-day runs: 21
Model's Last Updated: February 06 2025
sentence-similarity

Introduction of mpnet-base-all-mqp-binary

Model Details of mpnet-base-all-mqp-binary

SentenceTransformer based on sentence-transformers/all-mpnet-base-v2

This is a sentence-transformers model finetuned from sentence-transformers/all-mpnet-base-v2 . It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details
Model Description
Model Sources
Full Model Architecture
SentenceTransformer(
  (0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
)
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("mpnet-base-all-mqp-binary")
# Run inference
sentences = [
    'My bm aren\'t solid but not quite loose. Looks more like for lack of better word "shredded" the why is this?',
    'What causes stringy and shredded stools?',
    'I have been taking treatment for anxiety and depression. I was given a new medication and have experienced heart flutters, can this medication cause it?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
Evaluation
Metrics
Binary Classification
Metric Value
cosine_accuracy 0.8787
cosine_accuracy_threshold 0.7678
cosine_f1 0.8796
cosine_f1_threshold 0.7446
cosine_precision 0.881
cosine_recall 0.8782
cosine_ap 0.9474
Training Details
Training Dataset
Unnamed Dataset
  • Size: 2,437 training samples
  • Columns: text1 , text2 , and label
  • Approximate statistics based on the first 1000 samples:
    text1 text2 label
    type string string int
    details
    • min: 7 tokens
    • mean: 26.53 tokens
    • max: 75 tokens
    • min: 7 tokens
    • mean: 28.18 tokens
    • max: 119 tokens
    • 0: ~49.00%
    • 1: ~51.00%
  • Samples:
    text1 text2 label
    I discovered I get this weakness in my hand whenever I try to snap my fingers, slight pain runs across elbow and wrist? When I try to snap my fingers there is weakness and pain across elbow and wrist? May I know what are the causes? 1
    If a mother has celiac should the daughter be tested? What is Celiac disease? 0
    Hi im 18 and I would like to know what I would use or take to get taller? Can growth hormone taken in minimal quantities increase height after 21 years in a male? 0
  • Loss: ContrastiveLoss with these parameters:
    {
        "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
        "margin": 0.5,
        "size_average": true
    }
    
Evaluation Dataset
Unnamed Dataset
  • Size: 610 evaluation samples
  • Columns: text1 , text2 , and label
  • Approximate statistics based on the first 610 samples:
    text1 text2 label
    type string string int
    details
    • min: 8 tokens
    • mean: 27.56 tokens
    • max: 70 tokens
    • min: 8 tokens
    • mean: 27.88 tokens
    • max: 91 tokens
    • 0: ~48.85%
    • 1: ~51.15%
  • Samples:
    text1 text2 label
    Okay so i'm on bc and I have had sex (it hurts) i'm bleeding brown and my vagina hurts almost itchy but it hurts? I noticed a brown discharge and itching in my vaginal area to the point that it hurts. I am also on birth control and have sexual intercourse. What do you think is causing this? 1
    I've had body aches, blocked stuffy nose, headaches, pressure in my face and throat tightness and it feels dry for 6 months is it a bad cold? For the last 6 months, I've noticed symptoms like body aches, stuffy nose, headaches, pressure sensation in the face, throat tightness and feels dry. Can a cold last this long or should I be looking for something else? 1
    Is there any way to stop my period for a little while without a prescription? Are there any natural ways to stop my period without having to visit a local doctor? 1
  • Loss: ContrastiveLoss with these parameters:
    {
        "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
        "margin": 0.5,
        "size_average": true
    }
    
Training Hyperparameters
Non-Default Hyperparameters
  • eval_strategy : steps
  • per_device_train_batch_size : 16
  • per_device_eval_batch_size : 16
  • num_train_epochs : 1
  • warmup_ratio : 0.1
  • fp16 : True
  • push_to_hub : True
  • batch_sampler : no_duplicates
All Hyperparameters
Click to expand
  • overwrite_output_dir : False
  • do_predict : False
  • eval_strategy : steps
  • 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 : 5e-05
  • weight_decay : 0.0
  • 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 : {}
  • 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 : 42
  • data_seed : None
  • jit_mode_eval : False
  • use_ipex : False
  • bf16 : False
  • fp16 : True
  • 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 : False
  • 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}
  • deepspeed : None
  • label_smoothing_factor : 0.0
  • optim : adamw_torch
  • optim_args : None
  • adafactor : False
  • group_by_length : False
  • length_column_name : length
  • 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 : True
  • resume_from_checkpoint : None
  • hub_model_id : None
  • hub_strategy : every_save
  • hub_private_repo : None
  • hub_always_push : False
  • 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
  • dispatch_batches : None
  • split_batches : None
  • include_tokens_per_second : False
  • include_num_input_tokens_seen : False
  • neftune_noise_alpha : None
  • optim_target_modules : None
  • batch_eval_metrics : False
  • eval_on_start : False
  • use_liger_kernel : False
  • eval_use_gather_object : False
  • average_tokens_across_devices : False
  • prompts : None
  • batch_sampler : no_duplicates
  • multi_dataset_batch_sampler : proportional
Training Logs
Epoch Step Training Loss Validation Loss all-mqp-test_cosine_ap
0.6536 100 0.0137 0.0135 -
1.0 153 - - 0.9474
Framework Versions
  • Python: 3.11.11
  • Sentence Transformers: 3.3.1
  • Transformers: 4.47.1
  • PyTorch: 2.6.0+cu124
  • Accelerate: 1.2.1
  • Datasets: 3.2.0
  • Tokenizers: 0.21.0
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",
}
ContrastiveLoss
@inproceedings{hadsell2006dimensionality,
    author={Hadsell, R. and Chopra, S. and LeCun, Y.},
    booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)},
    title={Dimensionality Reduction by Learning an Invariant Mapping},
    year={2006},
    volume={2},
    number={},
    pages={1735-1742},
    doi={10.1109/CVPR.2006.100}
}

Runs of ayaat mpnet-base-all-mqp-binary on huggingface.co

104
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24-hour runs
15
3-day runs
33
7-day runs
21
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