all-mpnet-base-v2-augmentation-indomain-bm25-sts

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 <!-- at revision 9a3225965996d404b775526de6dbfe85d3368642 -->
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 768 tokens
• Similarity Function: Cosine Similarity

<!-- - **Training Dataset:** Unknown --> <!-- - **Language:** Unknown --> <!-- - **License:** Unknown -->

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': 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})
  (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("armaniii/all-mpnet-base-v2-augmentation-indomain-bm25-sts")
# Run inference
sentences = [
    'Fanatics of the pro – life argument are sometimes so focused on the fetus that they put no value to the mother ’s life and do not even consider the viability of the fetus .',
    'Life is life , whether it s outside the womb or not .',
    'Legalization of marijuana is phasing out black markets and taking money away from drug cartels, organized crime, and street gangs.',
]
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-test
• Evaluated with <code>EmbeddingSimilarityEvaluator</code>

Semantic Similarity

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

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Training Details

Training Dataset

Unnamed Dataset

• Size: 17,093 training samples
• Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2	score
  type	string	string	float
  details	<ul><li>min: 7 tokens</li><li>mean: 33.23 tokens</li><li>max: 97 tokens</li></ul>	<ul><li>min: 4 tokens</li><li>mean: 30.75 tokens</li><li>max: 96 tokens</li></ul>	<ul><li>min: 0.09</li><li>mean: 0.55</li><li>max: 0.95</li></ul>

• Samples:

  sentence1	sentence2	score
  <code>It is true that a Colorado study found a post-legalization increase in youths being treated for marijuana exposure .</code>	<code>In Colorado , recent figures correlate with the years since marijuana legalization to show a dramatic decrease in overall highway fatalities – and a two-fold increase in the frequency of marijuana-positive drivers in fatal auto crashes .</code>	<code>0.4642857142857143</code>
  <code>The idea of a school uniform is that students wear the uniform at school , but do not wear the uniform , say , at a disco or other events outside school .</code>	<code>If it means that the schoolrooms will be more orderly , more disciplined , and that our young people will learn to evaluate themselves by what they are on the inside instead of what they 're wearing on the outside , then our public schools should be able to require their students to wear school uniforms . "</code>	<code>0.5714285714285714</code>
  <code>The resulting embryonic stem cells could then theoretically be grown into adult cells to replace the ailing person 's mutated cells .</code>	<code>However , there is a more serious , less cartoonish objection to turning procreation into manufacturing .</code>	<code>0.4464285714285714</code>

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

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

Evaluation Dataset

Unnamed Dataset

• Size: 340 evaluation samples
• Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2	score
  type	string	string	float
  details	<ul><li>min: 8 tokens</li><li>mean: 33.76 tokens</li><li>max: 105 tokens</li></ul>	<ul><li>min: 6 tokens</li><li>mean: 31.86 tokens</li><li>max: 102 tokens</li></ul>	<ul><li>min: 0.09</li><li>mean: 0.5</li><li>max: 0.89</li></ul>

• Samples:

  sentence1	sentence2	score
  <code>[ quoting himself from Furman v. Georgia , 408 U.S. 238 , 257 ( 1972 ) ] As such it is a penalty that ' subjects the individual to a fate forbidden by the principle of civilized treatment guaranteed by the [ Clause ] . '</code>	<code>It provides a deterrent for prisoners already serving a life sentence .</code>	<code>0.3214285714285714</code>
  <code>Of those savings , $ 25.7 billion would accrue to state and local governments , while $ 15.6 billion would accrue to the federal government .</code>	<code>Jaime Smith , deputy communications director for the governor ’s office , said , “ The legalization initiative was not driven by a desire for a revenue , but it has provided a small assist for our state budget . ”</code>	<code>0.5357142857142857</code>
  <code>If the uterus is designed to sustain an unborn child ’s life , do n’t unborn children have a right to receive nutrition and shelter through the one organ designed to provide them with that ordinary care ?</code>	<code>We as parents are supposed to protect our children at all costs whether they are in the womb or not .</code>	<code>0.7678571428571428</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: 16
• per_device_eval_batch_size: 16
• warmup_ratio: 0.1
• bf16: True

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: 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: 3
• 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: 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: 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: batch_sampler
• multi_dataset_batch_sampler: proportional

</details>

Training Logs

Framework Versions

• Python: 3.9.2
• Sentence Transformers: 3.0.1
• Transformers: 4.43.1
• PyTorch: 2.3.1+cu121
• Accelerate: 0.34.2
• Datasets: 2.14.7
• 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",
}

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