SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
This is a sentence-transformers model finetuned from sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 on the sentence-transformers/quora-duplicates dataset. It maps sentences & paragraphs to a 384-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/paraphrase-multilingual-MiniLM-L12-v2
- Maximum Sequence Length: 128 tokens
- Output Dimensionality: 384 tokens
- Similarity Function: Cosine Similarity
- Training Dataset:
- 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': 128, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, '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("DashReza7/paraphrase-multilingual-MiniLM-L12-v2_QuoraDuplicateDetection_FINETUNED")
# Run inference
sentences = [
'Why do complementary angles have to be adjacent?',
'Can two adjacent angles be complementary?',
'How can I get rid of my bad habits?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
Evaluation
Metrics
Binary Classification
- Evaluated with
BinaryClassificationEvaluator
Metric | Value |
---|---|
cosine_accuracy | 0.8684 |
cosine_accuracy_threshold | 0.7981 |
cosine_f1 | 0.8292 |
cosine_f1_threshold | 0.7599 |
cosine_precision | 0.7747 |
cosine_recall | 0.8921 |
cosine_ap | 0.8822 |
dot_accuracy | 0.836 |
dot_accuracy_threshold | 17.1121 |
dot_f1 | 0.7914 |
dot_f1_threshold | 16.0833 |
dot_precision | 0.7294 |
dot_recall | 0.865 |
dot_ap | 0.8439 |
manhattan_accuracy | 0.8568 |
manhattan_accuracy_threshold | 46.9431 |
manhattan_f1 | 0.8144 |
manhattan_f1_threshold | 50.5148 |
manhattan_precision | 0.7656 |
manhattan_recall | 0.8698 |
manhattan_ap | 0.8636 |
euclidean_accuracy | 0.8569 |
euclidean_accuracy_threshold | 3.0017 |
euclidean_f1 | 0.8143 |
euclidean_f1_threshold | 3.2429 |
euclidean_precision | 0.7652 |
euclidean_recall | 0.8701 |
euclidean_ap | 0.8638 |
max_accuracy | 0.8684 |
max_accuracy_threshold | 46.9431 |
max_f1 | 0.8292 |
max_f1_threshold | 50.5148 |
max_precision | 0.7747 |
max_recall | 0.8921 |
max_ap | 0.8822 |
Training Details
Training Dataset
sentence-transformers/quora-duplicates
- Dataset: sentence-transformers/quora-duplicates at 451a485
- Size: 323,432 training samples
- Columns:
sentence1
,sentence2
, andlabel
- Approximate statistics based on the first 1000 samples:
sentence1 sentence2 label type string string int details - min: 6 tokens
- mean: 16.39 tokens
- max: 80 tokens
- min: 4 tokens
- mean: 16.2 tokens
- max: 71 tokens
- 0: ~62.10%
- 1: ~37.90%
- Samples:
sentence1 sentence2 label Which are the best compilers for C language (for Windows 10)?
Which is the best open source C/C++ compiler for Windows?
0
How much does YouTube pay per 1000 views in India?
How much does youtube pay per 1000 views?
0
What parts do I need to build my own PC?
I want to build a new computer. What parts do I need?
1
- Loss:
OnlineContrastiveLoss
Evaluation Dataset
sentence-transformers/quora-duplicates
- Dataset: sentence-transformers/quora-duplicates at 451a485
- Size: 80,858 evaluation samples
- Columns:
sentence1
,sentence2
, andlabel
- Approximate statistics based on the first 1000 samples:
sentence1 sentence2 label type string string int details - min: 6 tokens
- mean: 16.48 tokens
- max: 79 tokens
- min: 6 tokens
- mean: 16.76 tokens
- max: 101 tokens
- 0: ~63.90%
- 1: ~36.10%
- Samples:
sentence1 sentence2 label How many stories got busted on Quora while being anonymous?
Can what I say on Quora anonymously be used against me legally?
0
What are innovative mechanical component designs?
What is the Innovation design?
0
What is the best way to learn phrasal verbs?
Why should I learn phrasal verbs?
1
- Loss:
OnlineContrastiveLoss
Training Hyperparameters
Non-Default Hyperparameters
eval_strategy
: stepsper_device_train_batch_size
: 256per_device_eval_batch_size
: 256learning_rate
: 2e-05num_train_epochs
: 1warmup_ratio
: 0.1fp16
: True
All Hyperparameters
Click to expand
overwrite_output_dir
: Falsedo_predict
: Falseeval_strategy
: stepsprediction_loss_only
: Trueper_device_train_batch_size
: 256per_device_eval_batch_size
: 256per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 1eval_accumulation_steps
: Nonelearning_rate
: 2e-05weight_decay
: 0.0adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1.0num_train_epochs
: 1max_steps
: -1lr_scheduler_type
: linearlr_scheduler_kwargs
: {}warmup_ratio
: 0.1warmup_steps
: 0log_level
: passivelog_level_replica
: warninglog_on_each_node
: Truelogging_nan_inf_filter
: Truesave_safetensors
: Truesave_on_each_node
: Falsesave_only_model
: Falserestore_callback_states_from_checkpoint
: Falseno_cuda
: Falseuse_cpu
: Falseuse_mps_device
: Falseseed
: 42data_seed
: Nonejit_mode_eval
: Falseuse_ipex
: Falsebf16
: Falsefp16
: Truefp16_opt_level
: O1half_precision_backend
: autobf16_full_eval
: Falsefp16_full_eval
: Falsetf32
: Nonelocal_rank
: 0ddp_backend
: Nonetpu_num_cores
: Nonetpu_metrics_debug
: Falsedebug
: []dataloader_drop_last
: Falsedataloader_num_workers
: 0dataloader_prefetch_factor
: Nonepast_index
: -1disable_tqdm
: Falseremove_unused_columns
: Truelabel_names
: Noneload_best_model_at_end
: Falseignore_data_skip
: Falsefsdp
: []fsdp_min_num_params
: 0fsdp_config
: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}fsdp_transformer_layer_cls_to_wrap
: Noneaccelerator_config
: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}deepspeed
: Nonelabel_smoothing_factor
: 0.0optim
: adamw_torchoptim_args
: Noneadafactor
: Falsegroup_by_length
: Falselength_column_name
: lengthddp_find_unused_parameters
: Noneddp_bucket_cap_mb
: Noneddp_broadcast_buffers
: Falsedataloader_pin_memory
: Truedataloader_persistent_workers
: Falseskip_memory_metrics
: Trueuse_legacy_prediction_loop
: Falsepush_to_hub
: Falseresume_from_checkpoint
: Nonehub_model_id
: Nonehub_strategy
: every_savehub_private_repo
: Falsehub_always_push
: Falsegradient_checkpointing
: Falsegradient_checkpointing_kwargs
: Noneinclude_inputs_for_metrics
: Falseeval_do_concat_batches
: Truefp16_backend
: autopush_to_hub_model_id
: Nonepush_to_hub_organization
: Nonemp_parameters
:auto_find_batch_size
: Falsefull_determinism
: Falsetorchdynamo
: Noneray_scope
: lastddp_timeout
: 1800torch_compile
: Falsetorch_compile_backend
: Nonetorch_compile_mode
: Nonedispatch_batches
: Nonesplit_batches
: Noneinclude_tokens_per_second
: Falseinclude_num_input_tokens_seen
: Falseneftune_noise_alpha
: Noneoptim_target_modules
: Nonebatch_eval_metrics
: Falseeval_on_start
: Falsebatch_sampler
: batch_samplermulti_dataset_batch_sampler
: proportional
Training Logs
Epoch | Step | Training Loss | loss | max_ap |
---|---|---|---|---|
0.0791 | 100 | - | 8.0607 | 0.8164 |
0.1582 | 200 | - | 7.3012 | 0.8445 |
0.2373 | 300 | - | 6.9626 | 0.8582 |
0.3165 | 400 | - | 6.7901 | 0.8639 |
0.3956 | 500 | 7.5229 | 6.6498 | 0.8694 |
0.4747 | 600 | - | 6.5315 | 0.8736 |
0.5538 | 700 | - | 6.4686 | 0.8766 |
0.6329 | 800 | - | 6.4027 | 0.8787 |
0.7120 | 900 | - | 6.3108 | 0.8797 |
0.7911 | 1000 | 6.4636 | 6.2862 | 0.8812 |
0.8703 | 1100 | - | 6.2449 | 0.8818 |
0.9494 | 1200 | - | 6.2344 | 0.8822 |
Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.42.4
- PyTorch: 2.3.1+cu121
- Accelerate: 0.32.1
- 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",
}
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Dataset used to train DashReza7/paraphrase-multilingual-MiniLM-L12-v2_QuoraDuplicateDetection_FINETUNED
Evaluation results
- Cosine Accuracy on Unknownself-reported0.868
- Cosine Accuracy Threshold on Unknownself-reported0.798
- Cosine F1 on Unknownself-reported0.829
- Cosine F1 Threshold on Unknownself-reported0.760
- Cosine Precision on Unknownself-reported0.775
- Cosine Recall on Unknownself-reported0.892
- Cosine Ap on Unknownself-reported0.882
- Dot Accuracy on Unknownself-reported0.836
- Dot Accuracy Threshold on Unknownself-reported17.112
- Dot F1 on Unknownself-reported0.791