mpnet-base-allnli

SentenceTransformer based on microsoft/mpnet-base

This is a sentence-transformers model finetuned from microsoft/mpnet-base on the multi_nli, snli and stsb datasets. 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 <!-- at revision 6996ce1e91bd2a9c7d7f61daec37463394f73f09 -->
• Maximum Sequence Length: 384 tokens
• Output Dimensionality: 768 tokens
• Similarity Function: Dot Product
• Training Datasets:
  • multi_nli
  • snli
  • stsb
• 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({'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("tomaarsen/mpnet-base-allnli")
# Run inference
sentences = [
    "Rouen is the ancient center of Normandy's thriving textile industry, and the place of Joan of Arc's martyrdom ' a national symbol of resistance to tyranny.",
    'Joan of Arc sacrificed her life at Rouen, which became an enduring symbol of opposition to tyranny.',
    'The islands are part of France now instead of just colonies.',
]
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]

<!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* -->

Evaluation

Metrics

Semantic Similarity

• Dataset: sts-dev
• Evaluated with <code>EmbeddingSimilarityEvaluator</code>

Semantic Similarity

• Dataset: sts-test
• Evaluated with <code>EmbeddingSimilarityEvaluator</code>

<!-- ## Bias, Risks and Limitations *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* -->

Training Details

Training Datasets

multi_nli

• Dataset: multi_nli at da70db2
• Size: 392,702 training samples
• Columns: <code>premise</code>, <code>hypothesis</code>, and <code>label</code>
• Approximate statistics based on the first 1000 samples:

  	premise	hypothesis	label
  type	string	string	int
  details	<ul><li>min: 4 tokens</li><li>mean: 26.95 tokens</li><li>max: 189 tokens</li></ul>	<ul><li>min: 5 tokens</li><li>mean: 14.11 tokens</li><li>max: 49 tokens</li></ul>	<ul><li>0: ~34.30%</li><li>1: ~28.20%</li><li>2: ~37.50%</li></ul>

• Samples:

  premise	hypothesis	label
  <code>Conceptually cream skimming has two basic dimensions - product and geography.</code>	<code>Product and geography are what make cream skimming work. </code>	<code>1</code>
  <code>you know during the season and i guess at at your level uh you lose them to the next level if if they decide to recall the the parent team the Braves decide to call to recall a guy from triple A then a double A guy goes up to replace him and a single A guy goes up to replace him</code>	<code>You lose the things to the following level if the people recall.</code>	<code>0</code>
  <code>One of our number will carry out your instructions minutely.</code>	<code>A member of my team will execute your orders with immense precision.</code>	<code>0</code>

• Loss: <code>SoftmaxLoss</code>

snli

• Dataset: snli at cdb5c3d
• Size: 549,367 training samples
• Columns: <code>snli_premise</code>, <code>hypothesis</code>, and <code>label</code>
• Approximate statistics based on the first 1000 samples:

  	snli_premise	hypothesis	label
  type	string	string	int
  details	<ul><li>min: 6 tokens</li><li>mean: 17.38 tokens</li><li>max: 52 tokens</li></ul>	<ul><li>min: 4 tokens</li><li>mean: 10.7 tokens</li><li>max: 31 tokens</li></ul>	<ul><li>0: ~33.40%</li><li>1: ~33.30%</li><li>2: ~33.30%</li></ul>

• Samples:

  snli_premise	hypothesis	label
  <code>A person on a horse jumps over a broken down airplane.</code>	<code>A person is training his horse for a competition.</code>	<code>1</code>
  <code>A person on a horse jumps over a broken down airplane.</code>	<code>A person is at a diner, ordering an omelette.</code>	<code>2</code>
  <code>A person on a horse jumps over a broken down airplane.</code>	<code>A person is outdoors, on a horse.</code>	<code>0</code>

• Loss: <code>SoftmaxLoss</code>

stsb

• Dataset: stsb at 8913289
• Size: 5,749 training samples
• Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2	label
  type	string	string	float
  details	<ul><li>min: 6 tokens</li><li>mean: 10.0 tokens</li><li>max: 28 tokens</li></ul>	<ul><li>min: 5 tokens</li><li>mean: 9.95 tokens</li><li>max: 25 tokens</li></ul>	<ul><li>min: 0.0</li><li>mean: 0.54</li><li>max: 1.0</li></ul>

• Samples:

  sentence1	sentence2	label
  <code>A plane is taking off.</code>	<code>An air plane is taking off.</code>	<code>1.0</code>
  <code>A man is playing a large flute.</code>	<code>A man is playing a flute.</code>	<code>0.76</code>
  <code>A man is spreading shreded cheese on a pizza.</code>	<code>A man is spreading shredded cheese on an uncooked pizza.</code>	<code>0.76</code>

• Loss: <code>CosineSimilarityLoss</code> with these parameters:

  {
      "loss_fct": "torch.nn.modules.loss.MSELoss"
  }
  

Evaluation Datasets

multi_nli

• Dataset: multi_nli at da70db2
• Size: 100 evaluation samples
• Columns: <code>premise</code>, <code>hypothesis</code>, and <code>label</code>
• Approximate statistics based on the first 1000 samples:

  	premise	hypothesis	label
  type	string	string	int
  details	<ul><li>min: 5 tokens</li><li>mean: 27.67 tokens</li><li>max: 138 tokens</li></ul>	<ul><li>min: 6 tokens</li><li>mean: 13.48 tokens</li><li>max: 27 tokens</li></ul>	<ul><li>0: ~35.00%</li><li>1: ~31.00%</li><li>2: ~34.00%</li></ul>

• Samples:

  premise	hypothesis	label
  <code>The new rights are nice enough</code>	<code>Everyone really likes the newest benefits </code>	<code>1</code>
  <code>This site includes a list of all award winners and a searchable database of Government Executive articles.</code>	<code>The Government Executive articles housed on the website are not able to be searched.</code>	<code>2</code>
  <code>uh i don't know i i have mixed emotions about him uh sometimes i like him but at the same times i love to see somebody beat him</code>	<code>I like him for the most part, but would still enjoy seeing someone beat him.</code>	<code>0</code>

• Loss: <code>SoftmaxLoss</code>

snli

• Dataset: snli at cdb5c3d
• Size: 9,842 evaluation samples
• Columns: <code>snli_premise</code>, <code>hypothesis</code>, and <code>label</code>
• Approximate statistics based on the first 1000 samples:

  	snli_premise	hypothesis	label
  type	string	string	int
  details	<ul><li>min: 6 tokens</li><li>mean: 18.44 tokens</li><li>max: 57 tokens</li></ul>	<ul><li>min: 5 tokens</li><li>mean: 10.57 tokens</li><li>max: 25 tokens</li></ul>	<ul><li>0: ~33.10%</li><li>1: ~33.30%</li><li>2: ~33.60%</li></ul>

• Samples:

  snli_premise	hypothesis	label
  <code>Two women are embracing while holding to go packages.</code>	<code>The sisters are hugging goodbye while holding to go packages after just eating lunch.</code>	<code>1</code>
  <code>Two women are embracing while holding to go packages.</code>	<code>Two woman are holding packages.</code>	<code>0</code>
  <code>Two women are embracing while holding to go packages.</code>	<code>The men are fighting outside a deli.</code>	<code>2</code>

• Loss: <code>SoftmaxLoss</code>

stsb

• Dataset: stsb at 8913289
• Size: 1,500 evaluation samples
• Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2	label
  type	string	string	float
  details	<ul><li>min: 5 tokens</li><li>mean: 15.1 tokens</li><li>max: 45 tokens</li></ul>	<ul><li>min: 6 tokens</li><li>mean: 15.11 tokens</li><li>max: 53 tokens</li></ul>	<ul><li>min: 0.0</li><li>mean: 0.47</li><li>max: 1.0</li></ul>

• Samples:

  sentence1	sentence2	label
  <code>A man with a hard hat is dancing.</code>	<code>A man wearing a hard hat is dancing.</code>	<code>1.0</code>
  <code>A young child is riding a horse.</code>	<code>A child is riding a horse.</code>	<code>0.95</code>
  <code>A man is feeding a mouse to a snake.</code>	<code>The man is feeding a mouse to the snake.</code>	<code>1.0</code>

• Loss: <code>CosineSimilarityLoss</code> with these parameters:

  {
      "loss_fct": "torch.nn.modules.loss.MSELoss"
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 64
• per_device_eval_batch_size: 64
• learning_rate: 2e-05
• num_train_epochs: 1
• warmup_ratio: 0.1
• seed: 33
• bf16: True
• load_best_model_at_end: True
• push_to_hub: True
• hub_model_id: tomaarsen/mpnet-base-allnli
• hub_private_repo: True
• multi_dataset_batch_sampler: round_robin

All Hyperparameters

<details><summary>Click to expand</summary>

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: steps
• prediction_loss_only: True
• per_device_train_batch_size: 64
• per_device_eval_batch_size: 64
• per_gpu_train_batch_size: None
• per_gpu_eval_batch_size: None
• gradient_accumulation_steps: 1
• eval_accumulation_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: 33
• data_seed: None
• jit_mode_eval: False
• use_ipex: 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}
• 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: tomaarsen/mpnet-base-allnli
• hub_strategy: every_save
• hub_private_repo: True
• 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
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: round_robin

</details>

Training Logs

• The bold row denotes the saved checkpoint.

Environmental Impact

Carbon emissions were measured using CodeCarbon.

• Energy Consumed: 0.018 kWh
• Carbon Emitted: 0.007 kg of CO2
• Hours Used: 0.068 hours

Training Hardware

• On Cloud: No
• GPU Model: 1 x NVIDIA GeForce RTX 3090
• CPU Model: 13th Gen Intel(R) Core(TM) i7-13700K
• RAM Size: 31.78 GB

Framework Versions

• Python: 3.11.6
• Sentence Transformers: 3.1.0.dev0
• Transformers: 4.41.2
• PyTorch: 2.3.0+cu121
• Accelerate: 0.30.1
• Datasets: 2.19.1
• Tokenizers: 0.19.1

Citation

BibTeX

Sentence Transformers and SoftmaxLoss

@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",
}

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