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
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 768 tokens
• Similarity Function: Dot Product
• Training Dataset:
  • PiC/phrase_similarity
• Language: en

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]

Evaluation

Metrics

Binary Classification

• Dataset: quora-duplicates-dev
• Evaluated with BinaryClassificationEvaluator

Metric	Value
cosine_accuracy	0.681
cosine_accuracy_threshold	0.8657
cosine_f1	0.7373
cosine_f1_threshold	0.5984
cosine_precision	0.6161
cosine_recall	0.918
cosine_ap	0.7183
dot_accuracy	0.678
dot_accuracy_threshold	35.8649
dot_f1	0.7362
dot_f1_threshold	26.9072
dot_precision	0.6145
dot_recall	0.918
dot_ap	0.6677
manhattan_accuracy	0.682
manhattan_accuracy_threshold	75.963
manhattan_f1	0.7362
manhattan_f1_threshold	128.1774
manhattan_precision	0.6182
manhattan_recall	0.91
manhattan_ap	0.7193
euclidean_accuracy	0.682
euclidean_accuracy_threshold	3.4474
euclidean_f1	0.7362
euclidean_f1_threshold	6.0247
euclidean_precision	0.6145
euclidean_recall	0.918
euclidean_ap	0.7195
max_accuracy	0.682
max_accuracy_threshold	75.963
max_f1	0.7373
max_f1_threshold	128.1774
max_precision	0.6182
max_recall	0.918
max_ap	0.7195

Training Details

Training Dataset

PiC/phrase_similarity

• Dataset: PiC/phrase_similarity at fc67ce7
• Size: 7,004 training samples
• Columns: sentence1, sentence2, and label
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2	label
  type	string	string	int
  details	min: 12 tokens mean: 26.35 tokens max: 57 tokens	min: 12 tokens mean: 26.89 tokens max: 58 tokens	0: ~48.80% 1: ~51.20%

• Samples:

  sentence1	sentence2	label
  newly formed camp is released from the membrane and diffuses across the intracellular space where it serves to activate pka.	recently made encampment is released from the membrane and diffuses across the intracellular space where it serves to activate pka.	0
  According to one data, in 1910, on others – in 1915, the mansion became Natalya Dmitriyevna Shchuchkina's property.	According to a particular statistic, in 1910, on others – in 1915, the mansion became Natalya Dmitriyevna Shchuchkina's property.	1
  Note that Fact 1 does not assume any particular structure on the set formula_65.	Note that Fact 1 does not assume any specific edifice on the set formula_65.	0

• Loss: SoftmaxLoss

Evaluation Dataset

PiC/phrase_similarity

• Dataset: PiC/phrase_similarity at fc67ce7
• Size: 1,000 evaluation samples
• Columns: sentence1, sentence2, and label
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2	label
  type	string	string	int
  details	min: 9 tokens mean: 26.21 tokens max: 61 tokens	min: 10 tokens mean: 26.8 tokens max: 61 tokens	0: ~50.00% 1: ~50.00%

• Samples:

  sentence1	sentence2	label
  after theo's apparent death, she decides to leave first colony and ends up traveling with the apostles.	after theo's apparent death, she decides to leave original settlement and ends up traveling with the apostles.	0
  The guard assigned to Vivian leaves her to prevent the robbery, allowing her to connect to the bank's network.	The guard assigned to Vivian leaves her to prevent the robbery, allowing her to connect to the bank's locations.	0
  Two days later Louis XVI banished Necker by a "lettre de cachet" for his very public exchange of pamphlets.	Two days later Louis XVI banished Necker by a "lettre de cachet" for his very free forum of pamphlets.	0

• Loss: SoftmaxLoss

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

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
• 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: {'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
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	loss	quora-duplicates-dev_max_ap
0	0	-	-	0.6564
0.2283	100	-	0.6941	0.6565
0.4566	200	-	0.6899	0.6713
0.6849	300	-	0.6467	0.7247
0.9132	400	-	0.5957	0.7231
1.1416	500	0.6571	0.6093	0.7044
1.3699	600	-	0.5578	0.7195
1.5982	700	-	0.5626	0.7372
1.8265	800	-	0.5790	0.7413
2.0548	900	-	0.5648	0.7405
2.2831	1000	0.519	0.5820	0.7467
2.5114	1100	-	0.5976	0.7455
2.7397	1200	-	0.6026	0.7335
2.9680	1300	-	0.6231	0.7422
3.1963	1400	-	0.6514	0.7376
3.4247	1500	0.3903	0.6695	0.7379
3.6530	1600	-	0.6610	0.7339
3.8813	1700	-	0.6811	0.7318
4.1096	1800	-	0.7205	0.7274
4.3379	1900	-	0.7333	0.7332
4.5662	2000	0.3036	0.7353	0.7323
4.7945	2100	-	0.7293	0.7322
5.0	2190	-	-	0.7195

• 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",
}