Deehan1866/finetuned-sentence-transformers-multi-qa-mpnet-base-dot-v1

SentenceTransformer based on sentence-transformers/multi-qa-mpnet-base-dot-v1

This is a sentence-transformers model finetuned from sentence-transformers/multi-qa-mpnet-base-dot-v1 on the PiC/phrase_similarity 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: sentence-transformers/multi-qa-mpnet-base-dot-v1 <!-- at revision 3af7c6da5b3e1bea796ef6c97fe237538cbe6e7f -->
• —Maximum Sequence Length: 512 tokens
• —Output Dimensionality: 768 tokens
• —Similarity Function: Dot Product
• —Training Dataset:
• —PiC/phrase_similarity
• —Language: en <!-- - 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': True, 'pooling_mode_mean_tokens': False, '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("Deehan1866/finetuned-sentence-transformers-multi-qa-mpnet-base-dot-v1")
# Run inference
sentences = [
    "She wants to write about Keima but suffers a major case of writer's block.",
    "She wants to write about Keima but suffers a huge occurrence of writer's block.",
    'specific medical status of movement and the general condition of movement both are conditions under which contradictions can move.',
]
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]

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Evaluation

Metrics

Binary Classification

• —Dataset: quora-duplicates-dev
• —Evaluated with <code>BinaryClassificationEvaluator</code>

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Training Details

Training Dataset

PiC/phrase_similarity

• —Dataset: PiC/phrase_similarity at fc67ce7
• —Size: 7,004 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 | int | | details | <ul><li>min: 12 tokens</li><li>mean: 26.35 tokens</li><li>max: 57 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 26.89 tokens</li><li>max: 58 tokens</li></ul> | <ul><li>0: ~48.80%</li><li>1: ~51.20%</li></ul> |
• —Samples: | sentence1 | sentence2 | label | |:------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------|:---------------| | <code>newly formed camp is released from the membrane and diffuses across the intracellular space where it serves to activate pka.</code> | <code>recently made encampment is released from the membrane and diffuses across the intracellular space where it serves to activate pka.</code> | <code>0</code> | | <code>According to one data, in 1910, on others – in 1915, the mansion became Natalya Dmitriyevna Shchuchkina's property.</code> | <code>According to a particular statistic, in 1910, on others – in 1915, the mansion became Natalya Dmitriyevna Shchuchkina's property.</code> | <code>1</code> | | <code>Note that Fact 1 does not assume any particular structure on the set formula65.</code> | <code>Note that Fact 1 does not assume any specific edifice on the set formula65.</code> | <code>0</code> |
• —Loss: <code>SoftmaxLoss</code>

Evaluation Dataset

PiC/phrase_similarity

• —Dataset: PiC/phrase_similarity at fc67ce7
• —Size: 1,000 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 | int | | details | <ul><li>min: 9 tokens</li><li>mean: 26.21 tokens</li><li>max: 61 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 26.8 tokens</li><li>max: 61 tokens</li></ul> | <ul><li>0: ~50.00%</li><li>1: ~50.00%</li></ul> |
• —Samples: | sentence1 | sentence2 | label | |:----------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------|:---------------| | <code>after theo's apparent death, she decides to leave first colony and ends up traveling with the apostles.</code> | <code>after theo's apparent death, she decides to leave original settlement and ends up traveling with the apostles.</code> | <code>0</code> | | <code>The guard assigned to Vivian leaves her to prevent the robbery, allowing her to connect to the bank's network.</code> | <code>The guard assigned to Vivian leaves her to prevent the robbery, allowing her to connect to the bank's locations.</code> | <code>0</code> | | <code>Two days later Louis XVI banished Necker by a "lettre de cachet" for his very public exchange of pamphlets.</code> | <code>Two days later Louis XVI banished Necker by a "lettre de cachet" for his very free forum of pamphlets.</code> | <code>0</code> |
• —Loss: <code>SoftmaxLoss</code>

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: 5
• —warmup_ratio: 0.1
• —load_best_model_at_end: 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
• —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: 5
• —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: 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: {'minnumparams': 0, 'xla': False, 'xlafsdpv2': False, 'xlafsdpgrad_ckpt': False}
• —fsdp_transformer_layer_cls_to_wrap: None
• —accelerator_config: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': 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
• —batch_sampler: batch_sampler
• —multi_dataset_batch_sampler: proportional

</details>

Training Logs

• —The bold row denotes the saved checkpoint.

Framework Versions

• —Python: 3.10.10
• —Sentence Transformers: 3.0.1
• —Transformers: 4.42.3
• —PyTorch: 2.2.1+cu121
• —Accelerate: 0.32.1
• —Datasets: 2.20.0
• —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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