mouped / duplicate-detection

SentenceTransformer based on intfloat/multilingual-e5-small

This is a sentence-transformers model finetuned from intfloat/multilingual-e5-small . It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for retrieval.

Model Details

Model Description

• Model Type: Sentence Transformer
• Base model: intfloat/multilingual-e5-small
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 384 dimensions
• Similarity Function: Cosine Similarity
• Supported Modality: Text

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'BertModel'})
  (1): Pooling({'embedding_dimension': 384, 'pooling_mode': 'mean', '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("sentence_transformers_model_id")
# Run inference
sentences = [
    'query: What free online course can I take to learn how to be an expert in drawing?',
    'query: What are some best online courses to learn Drawing?',
    'query: Permisi, tagihan WiFi bulan ini belum saya bayar, please advise',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[ 1.0000,  0.7386,  0.0006],
#         [ 0.7386,  1.0000, -0.0875],
#         [ 0.0006, -0.0875,  1.0000]])

Evaluation

Metrics

Binary Classification

• Dataset: ticket-duplicate-eval
• Evaluated with BinaryClassificationEvaluator

Training Details

Training Dataset

Unnamed Dataset

• Size: 67,265 training samples
• Columns: sentence_0 , sentence_1 , and label
• Approximate statistics based on the first 100 samples:

  	sentence_0	sentence_1	label
  type	string	string	float
  modality	text	text
  details	min: 11 tokens mean: 18.45 tokens max: 29 tokens	min: 9 tokens mean: 19.12 tokens max: 45 tokens	min: 0.0 mean: 0.49 max: 1.0

• Samples:

  sentence_0	sentence_1	label
  query: Akses saya ke aplikasi butuh reset password dari kemarin, bisa dibantu ya?	query: My 2fa verification code never arrives since last night, can someone assist?	0.0
  query: Mohon segera ditindaklanjuti: internet kantor mati total, bukan lambat, kindly assist	query: Selamat pagi, tagihan internet bulan ini lebih mahal dari biasanya, please advise	0.0
  query: The company website won't load since yesterday, can someone assist?	query: Need help — Our internal portal keeps throwing a 500 error since this afternoon.	1.0

• Loss: ContrastiveLoss with these parameters:

  {
      "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
      "margin": 0.5,
      "size_average": true
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• per_device_train_batch_size : 32
• num_train_epochs : 2
• per_device_eval_batch_size : 32
• multi_dataset_batch_sampler : round_robin

All Hyperparameters

Click to expand

• per_device_train_batch_size : 32
• num_train_epochs : 2
• max_steps : -1
• learning_rate : 5e-05
• lr_scheduler_type : linear
• lr_scheduler_kwargs : None
• warmup_steps : 0
• optim : adamw_torch_fused
• optim_args : None
• weight_decay : 0.0
• adam_beta1 : 0.9
• adam_beta2 : 0.999
• adam_epsilon : 1e-08
• optim_target_modules : None
• gradient_accumulation_steps : 1
• average_tokens_across_devices : True
• max_grad_norm : 1
• label_smoothing_factor : 0.0
• bf16 : False
• fp16 : False
• bf16_full_eval : False
• fp16_full_eval : False
• tf32 : None
• gradient_checkpointing : False
• gradient_checkpointing_kwargs : None
• torch_compile : False
• torch_compile_backend : None
• torch_compile_mode : None
• use_liger_kernel : False
• liger_kernel_config : None
• use_cache : False
• neftune_noise_alpha : None
• torch_empty_cache_steps : None
• auto_find_batch_size : False
• log_on_each_node : True
• logging_nan_inf_filter : True
• include_num_input_tokens_seen : no
• log_level : passive
• log_level_replica : warning
• disable_tqdm : False
• project : huggingface
• trackio_space_id : None
• trackio_bucket_id : None
• trackio_static_space_id : None
• per_device_eval_batch_size : 32
• prediction_loss_only : True
• eval_on_start : False
• eval_do_concat_batches : True
• eval_use_gather_object : False
• eval_accumulation_steps : None
• include_for_metrics : []
• batch_eval_metrics : False
• save_only_model : False
• save_on_each_node : False
• enable_jit_checkpoint : False
• push_to_hub : False
• hub_private_repo : None
• hub_model_id : None
• hub_strategy : every_save
• hub_always_push : False
• hub_revision : None
• load_best_model_at_end : False
• ignore_data_skip : False
• restore_callback_states_from_checkpoint : False
• full_determinism : False
• seed : 42
• data_seed : None
• use_cpu : False
• accelerator_config : {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
• parallelism_config : None
• dataloader_drop_last : False
• dataloader_num_workers : 0
• dataloader_pin_memory : True
• dataloader_persistent_workers : False
• dataloader_prefetch_factor : None
• remove_unused_columns : True
• label_names : None
• train_sampling_strategy : random
• length_column_name : length
• ddp_find_unused_parameters : None
• ddp_bucket_cap_mb : None
• ddp_broadcast_buffers : False
• ddp_static_graph : None
• ddp_backend : None
• ddp_timeout : 1800
• fsdp : None
• fsdp_config : None
• deepspeed : None
• debug : []
• skip_memory_metrics : True
• do_predict : False
• resume_from_checkpoint : None
• warmup_ratio : None
• local_rank : -1
• prompts : None
• batch_sampler : batch_sampler
• multi_dataset_batch_sampler : round_robin
• router_mapping : {}
• learning_rate_mapping : {}

Training Logs

Training Time

• Training : 43.8 minutes

Framework Versions

• Python: 3.12.13
• Sentence Transformers: 5.6.0
• Transformers: 5.13.1
• PyTorch: 2.11.0+cu128
• Accelerate: 1.14.0
• Datasets: 4.0.0
• Tokenizers: 0.22.2

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",
}

ContrastiveLoss

@inproceedings{hadsell2006dimensionality,
    author={Hadsell, R. and Chopra, S. and LeCun, Y.},
    booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)},
    title={Dimensionality Reduction by Learning an Invariant Mapping},
    year={2006},
    volume={2},
    number={},
    pages={1735-1742},
    doi={10.1109/CVPR.2006.100}
}