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",
}
- Downloads last month
- 71
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.
Model tree for DashReza7/paraphrase-multilingual-MiniLM-L12-v2_QuoraDuplicateDetection_FINETUNED
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