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Add new SentenceTransformer model.
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metadata
base_model: microsoft/mpnet-base
datasets:
  - sentence-transformers/all-nli
language:
  - en
library_name: sentence-transformers
license: apache-2.0
metrics:
  - cosine_accuracy
  - dot_accuracy
  - manhattan_accuracy
  - euclidean_accuracy
  - max_accuracy
pipeline_tag: sentence-similarity
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:100000
  - loss:MultipleNegativesRankingLoss
widget:
  - source_sentence: People on bicycles waiting at an intersection.
    sentences:
      - More than one person on a bicycle is obeying traffic laws.
      - The people are on skateboards.
      - People waiting at a light on bikes.
  - source_sentence: A dog is in the water.
    sentences:
      - A white dog with brown spots standing in water.
      - A woman in a white outfit holds her purse while on a crowded bus.
      - A wakeboarder is traveling across the water behind a ramp.
  - source_sentence: The people are sleeping.
    sentences:
      - A man and young boy asleep in a chair.
      - A father and his son cuddle while sleeping.
      - Several people are sitting on the back of a truck outside.
  - source_sentence: A dog is swimming.
    sentences:
      - A brown god relaxes on a brick sidewalk.
      - The furry brown dog is swimming in the ocean.
      - a black dog swimming in the water with a tennis ball in his mouth
  - source_sentence: A dog is swimming.
    sentences:
      - >-
        A woman in all black throws a football indoors while man looks at his
        cellphone in the background.
      - A white dog with a stick in his mouth standing next to a black dog.
      - A dog with yellow fur swims, neck deep, in water.
model-index:
  - name: MPNet base trained on AllNLI triplets
    results:
      - task:
          type: triplet
          name: Triplet
        dataset:
          name: all nli dev
          type: all-nli-dev
        metrics:
          - type: cosine_accuracy
            value: 0.9059842041312273
            name: Cosine Accuracy
          - type: dot_accuracy
            value: 0.09386391251518833
            name: Dot Accuracy
          - type: manhattan_accuracy
            value: 0.900820170109356
            name: Manhattan Accuracy
          - type: euclidean_accuracy
            value: 0.9017314702308628
            name: Euclidean Accuracy
          - type: max_accuracy
            value: 0.9059842041312273
            name: Max Accuracy
      - task:
          type: triplet
          name: Triplet
        dataset:
          name: all nli test
          type: all-nli-test
        metrics:
          - type: cosine_accuracy
            value: 0.9185958541382963
            name: Cosine Accuracy
          - type: dot_accuracy
            value: 0.08019367529126949
            name: Dot Accuracy
          - type: manhattan_accuracy
            value: 0.9142078983204721
            name: Manhattan Accuracy
          - type: euclidean_accuracy
            value: 0.9142078983204721
            name: Euclidean Accuracy
          - type: max_accuracy
            value: 0.9185958541382963
            name: Max Accuracy

MPNet base trained on AllNLI triplets

This is a sentence-transformers model finetuned from microsoft/mpnet-base on the sentence-transformers/all-nli dataset. 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: microsoft/mpnet-base
  • Maximum Sequence Length: 512 tokens
  • Output Dimensionality: 768 tokens
  • Similarity Function: Cosine Similarity
  • Training Dataset:
  • Language: en
  • License: apache-2.0

Model Sources

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, '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})
)

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("korruz/mpnet-base-all-nli-triplet")
# Run inference
sentences = [
    'A dog is swimming.',
    'A dog with yellow fur swims, neck deep, in water.',
    'A white dog with a stick in his mouth standing next to a black dog.',
]
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

Triplet

Metric Value
cosine_accuracy 0.906
dot_accuracy 0.0939
manhattan_accuracy 0.9008
euclidean_accuracy 0.9017
max_accuracy 0.906

Triplet

Metric Value
cosine_accuracy 0.9186
dot_accuracy 0.0802
manhattan_accuracy 0.9142
euclidean_accuracy 0.9142
max_accuracy 0.9186

Training Details

Training Dataset

sentence-transformers/all-nli

  • Dataset: sentence-transformers/all-nli at d482672
  • Size: 100,000 training samples
  • Columns: anchor, positive, and negative
  • Approximate statistics based on the first 1000 samples:
    anchor positive negative
    type string string string
    details
    • min: 7 tokens
    • mean: 10.46 tokens
    • max: 46 tokens
    • min: 6 tokens
    • mean: 12.81 tokens
    • max: 40 tokens
    • min: 5 tokens
    • mean: 13.4 tokens
    • max: 50 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"
    }
    

Training Hyperparameters

Non-Default Hyperparameters

  • eval_strategy: steps
  • per_device_train_batch_size: 16
  • per_device_eval_batch_size: 16
  • learning_rate: 2e-05
  • num_train_epochs: 1
  • warmup_ratio: 0.1
  • fp16: 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: 2e-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: False
  • resume_from_checkpoint: None
  • hub_model_id: None
  • hub_strategy: every_save
  • hub_private_repo: False
  • hub_always_push: False
  • gradient_checkpointing: False
  • gradient_checkpointing_kwargs: None
  • include_inputs_for_metrics: False
  • 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
  • eval_use_gather_object: False
  • batch_sampler: no_duplicates
  • multi_dataset_batch_sampler: proportional

Training Logs

Epoch Step Training Loss all-nli-dev_max_accuracy all-nli-test_max_accuracy
0 0 - 0.6832 -
0.032 100 3.2593 0.8010 -
0.064 200 1.318 0.8152 -
0.096 300 1.2552 0.8256 -
0.128 400 1.3322 0.8141 -
0.16 500 1.4141 0.8224 -
0.192 600 1.2339 0.8149 -
0.224 700 1.2556 0.8091 -
0.256 800 1.138 0.8262 -
0.288 900 1.0928 0.8311 -
0.32 1000 1.0438 0.8341 -
0.352 1100 1.1159 0.8323 -
0.384 1200 1.1909 0.8472 -
0.416 1300 1.2542 0.8543 -
0.448 1400 1.2359 0.8574 -
0.48 1500 1.0265 0.8712 -
0.512 1600 0.8688 0.8783 -
0.544 1700 0.8819 0.8841 -
0.576 1800 0.8903 0.8931 -
0.608 1900 0.9334 0.8858 -
0.64 2000 1.0225 0.9028 -
0.672 2100 0.9252 0.9034 -
0.704 2200 0.9036 0.9033 -
0.736 2300 0.8122 0.9040 -
0.768 2400 0.8503 0.9058 -
0.8 2500 0.8448 0.9055 -
0.832 2600 0.7918 0.9039 -
0.864 2700 0.7787 0.9025 -
0.896 2800 0.8624 0.9034 -
0.928 2900 0.9513 0.9058 -
0.96 3000 0.6548 0.9072 -
0.992 3100 0.0163 0.9060 -
1.0 3125 - - 0.9186

Framework Versions

  • Python: 3.10.12
  • Sentence Transformers: 3.0.1
  • Transformers: 4.44.2
  • PyTorch: 2.4.0+cu121
  • Accelerate: 0.33.0
  • Datasets: 2.21.0
  • Tokenizers: 0.19.1

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",
}

MultipleNegativesRankingLoss

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply}, 
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}