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FritzStack
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mpnet_MH_embedding

Feature Extraction
sentence-transformers
Safetensors
mpnet
sentence-similarity
dense
Generated from Trainer
dataset_size:4615
loss:TripletLoss
text-embeddings-inference
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metadata 
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - dense
  - generated_from_trainer
  - dataset_size:4615
  - loss:TripletLoss
base_model: sentence-transformers/all-mpnet-base-v2
widget:
  - source_sentence: Do you ever feel like you have failed in life or let yourself down?
    sentences:
      - >-
        But I just don't feel like even getting started because I know that I
        will fail again.
      - I cant remember the last time I felt happiness.
      - That was their biggest and last mistake.
  - source_sentence: Do you feel sad or unhappy?
    sentences:
      - I have been depressed since late September so I feel you.
      - I share a lot of your traits, and considered myself a failure too.
      - He conveys that feeling of regret so well I can feel it everytime
  - source_sentence: Do you feel hopeful about your future or do things seem hopeless?
    sentences:
      - >-
        I'm pretty optimistic though since the pace of technological growth is
        accelerating so rapidly.
      - >-
        [For a clickable image, click
        here](http://futurism.com/thisweekinscience)


        [To get these images directly to your inbox, sign up
        here](http://futurism.com/subscribe)


        _


        Sources | Reddit

        --- | ---

        [Oldest and Furthest
        Galaxy](http://futurism.com/links/astronomers-discover-the-oldest-and-farthest-known-galaxy/)
        |
        [Reddit](https://www.reddit.com/r/science/comments/3jypyf/researchers_find_132_billion_yearold_galaxy_in/)

        [3D Printed Ribs
        ](http://futurism.com/links/these-3d-printed-titanium-ribs-were-successfully-implanted-in-a-person/)
        |
        [Reddit](https://www.reddit.com/r/technology/comments/3kj8pf/patient_receives_3dprinted_titanium_sternum_and/?ref=search_posts)

        [Chinese Far Side of Moon]
        (http://m.phys.org/news/2015-09-china-aims-probe-moon-side.html)  |
        [Reddit](https://www.reddit.com/r/worldnews/comments/3kcsg5/china_to_explore_dark_side_of_the_moon_china_has/)

        [Rugby Ball
        Molecule](http://www.forbes.com/sites/carmendrahl/2015/09/02/giant-rugby-ball-new-interaction-chemistry/)
        |
        [Reddit](https://www.reddit.com/r/EverythingScience/comments/3krt22/this_giant_rugby_ball_contains_a_new_chemical/)

        [Measuring the
        Universe](http://astronomynow.com/2015/09/04/using-stellar-twins-to-climb-the-cosmic-distance-ladder/)
        |
        [Reddit](https://www.reddit.com/r/science/comments/3jum8c/astronomers_have_developed_a_new_highly_accurate/)

        [3D Printed Stethoscope
        ](http://futurism.com/links/3d-printed-stethoscopes-cost-as-little-as-2-50-and-are-just-as-good/)
        |
        [Reddit](https://www.reddit.com/r/news/comments/3kgboz/doctor_3d_prints_stethoscope_to_alleviate_supply/)

        [Giant Structure in
        Universe](http://phys.org/news/2015-09-giant-ring-like-universe.html) |
        [Reddit](https://www.reddit.com/r/EverythingScience/comments/3jzjlm/surprising_giant_ringlike_structure_in_the/)

        [Recoded Cell
        Factories](http://m.phys.org/news/2015-09-recoded-cells-factories-proteins.html)
        |
        [Reddit](https://www.reddit.com/r/EverythingScience/comments/3krux3/researchers_transform_recoded_cells_into/)
pipeline_tag: sentence-similarity
library_name: sentence-transformers

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 Type: Sentence Transformer
  • Base model: sentence-transformers/all-mpnet-base-v2
  • Maximum Sequence Length: 384 tokens
  • Output Dimensionality: 768 dimensions
  • Similarity Function: Cosine Similarity

Model Sources

Full Model Architecture 

SentenceTransformer(
  (0): Transformer({'max_seq_length': 384, 'do_lower_case': False, 'architecture': '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("FritzStack/mpnet_MH_embedding")
# Run inference
sentences = [
    '',
    '',
    '',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)

Training Details

Training Dataset

Training Hyperparameters

Non-Default Hyperparameters

  • per_device_train_batch_size: 2
  • gradient_accumulation_steps: 8
  • warmup_steps: 100
  • fp16: True

All Hyperparameters

Click to expand
  • overwrite_output_dir: False
  • do_predict: False
  • eval_strategy: no
  • prediction_loss_only: True
  • per_device_train_batch_size: 2
  • per_device_eval_batch_size: 8
  • per_gpu_train_batch_size: None
  • per_gpu_eval_batch_size: None
  • gradient_accumulation_steps: 8
  • 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: 3
  • max_steps: -1
  • lr_scheduler_type: linear
  • lr_scheduler_kwargs: {}
  • warmup_ratio: 0.0
  • warmup_steps: 100
  • 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
  • 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}
  • parallelism_config: None
  • deepspeed: None
  • label_smoothing_factor: 0.0
  • optim: adamw_torch_fused
  • 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: batch_sampler
  • multi_dataset_batch_sampler: proportional
  • router_mapping: {}
  • learning_rate_mapping: {}

Framework Versions

  • Python: 3.12.12
  • Sentence Transformers: 5.1.1
  • Transformers: 4.57.1
  • PyTorch: 2.8.0+cu126
  • Accelerate: 1.10.1
  • Datasets: 4.0.0
  • Tokenizers: 0.22.1

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",
}

TripletLoss 

@misc{hermans2017defense,
    title={In Defense of the Triplet Loss for Person Re-Identification},
    author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
    year={2017},
    eprint={1703.07737},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}