manestay/bge-base-en-v1.5-mnrl-cl-multi

SentenceTransformer based on BAAI/bge-base-en-v1.5

This is a sentence-transformers model finetuned from BAAI/bge-base-en-v1.5 on the mnrl and cl datasets. 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: BAAI/bge-base-en-v1.5
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 768 dimensions
• Similarity Function: Cosine Similarity
• Training Datasets:
  • mnrl
  • cl
• 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': True}) with Transformer model: BertModel 
  (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})
  (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("manestay/bge-base-en-v1.5-mnrl-cl-multi")
# Run inference
sentences = [
    "For what reasons can't the Olympics be held in India?",
    'When will Olympics be held in India?',
    'When will India qualify for the FIFA World Cup?',
]
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
• Evaluated with BinaryClassificationEvaluator

Metric	Value
cosine_accuracy	0.833
cosine_accuracy_threshold	0.8065
cosine_f1	0.7631
cosine_f1_threshold	0.7453
cosine_precision	0.6706
cosine_recall	0.8851
cosine_ap	0.8121
cosine_mcc	0.6414

Paraphrase Mining

• Dataset: quora-duplicates-dev
• Evaluated with ParaphraseMiningEvaluator with these parameters:

  {'add_transitive_closure': <function ParaphraseMiningEvaluator.add_transitive_closure at 0x7f26a89802c0>, 'max_pairs': 500000, 'top_k': 100}
  

Metric	Value
average_precision	0.6287
f1	0.6032
precision	0.5627
recall	0.65
threshold	0.7945

Information Retrieval

• Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.9732
cosine_accuracy@3	0.9944
cosine_accuracy@5	0.9958
cosine_accuracy@10	0.9994
cosine_precision@1	0.9732
cosine_precision@3	0.432
cosine_precision@5	0.2765
cosine_precision@10	0.1461
cosine_recall@1	0.8392
cosine_recall@3	0.9655
cosine_recall@5	0.9826
cosine_recall@10	0.9955
cosine_ndcg@10	0.9852
cosine_mrr@10	0.9839
cosine_map@100	0.9794

Training Details

Training Datasets

mnrl

• Dataset: mnrl at 451a485
• Size: 100,000 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 6 tokens mean: 13.85 tokens max: 42 tokens	min: 6 tokens mean: 13.65 tokens max: 44 tokens	min: 4 tokens mean: 14.76 tokens max: 64 tokens

• Samples:

  anchor	positive	negative
  Why in India do we not have one on one political debate as in USA?	Why cant we have a public debate between politicians in India like the one in US?	Can people on Quora stop India Pakistan debate? We are sick and tired seeing this everyday in bulk?
  What is OnePlus One?	How is oneplus one?	Why is OnePlus One so good?
  Does our mind control our emotions?	How do smart and successful people control their emotions?	How can I control my positive emotions for the people whom I love but they don't care about me?

• Loss: MultipleNegativesRankingLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  

cl

• Dataset: cl at 451a485
• Size: 100,000 training samples
• Columns: sentence1, sentence2, and label
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2	label
  type	string	string	int
  details	min: 6 tokens mean: 15.3 tokens max: 57 tokens	min: 6 tokens mean: 15.66 tokens max: 56 tokens	0: ~62.00% 1: ~38.00%

• Samples:

  sentence1	sentence2	label
  What is the step by step guide to invest in share market in india?	What is the step by step guide to invest in share market?	0
  What is the story of Kohinoor (Koh-i-Noor) Diamond?	What would happen if the Indian government stole the Kohinoor (Koh-i-Noor) diamond back?	0
  How can I increase the speed of my internet connection while using a VPN?	How can Internet speed be increased by hacking through DNS?	0

• Loss: ContrastiveLoss with these parameters:

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

Evaluation Datasets

mnrl

• Dataset: mnrl at 451a485
• Size: 1,000 evaluation samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 7 tokens mean: 13.84 tokens max: 43 tokens	min: 6 tokens mean: 13.8 tokens max: 38 tokens	min: 6 tokens mean: 14.71 tokens max: 56 tokens

• Samples:

  anchor	positive	negative
  Which programming language is best for developing low-end games?	What coding language should I learn first for making games?	I am entering the world of video game programming and want to know what language I should learn? Because there are so many languages ​​I do not know which one to start with. Can you recommend a language that's easy to learn and can be used with many platforms?
  Was it appropriate for Meryl Streep to use her Golden Globes speech to attack Donald Trump?	Should Meryl Streep be using her position to attack the president?	Why did Kelly Ann Conway say that Meryl Streep incited peoples worst feelings?
  Where can I found excellent commercial fridges in Sydney?	Where can I found impressive range of commercial fridges in Sydney?	What is the best grocery delivery service in Sydney?

• Loss: MultipleNegativesRankingLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  

cl

• Dataset: cl at 451a485
• 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: 5 tokens mean: 15.59 tokens max: 59 tokens	min: 6 tokens mean: 15.65 tokens max: 76 tokens	0: ~63.40% 1: ~36.60%

• Samples:

  sentence1	sentence2	label
  What should I ask my friend to get from UK to India?	What is the process of getting a surgical residency in UK after completing MBBS from India?	0
  How can I learn hacking for free?	How can I learn to hack seriously?	1
  Which is the best website to learn programming language C++?	Which is the best website to learn C++ Programming language for free?	0

• Loss: ContrastiveLoss with these parameters:

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

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 400
• per_device_eval_batch_size: 400
• num_train_epochs: 100
• warmup_ratio: 0.1
• bf16: True
• load_best_model_at_end: True
• batch_sampler: no_duplicates

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: steps
• prediction_loss_only: True
• per_device_train_batch_size: 400
• per_device_eval_batch_size: 400
• per_gpu_train_batch_size: None
• per_gpu_eval_batch_size: None
• gradient_accumulation_steps: 1
• eval_accumulation_steps: None
• torch_empty_cache_steps: None
• learning_rate: 5e-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: 100
• 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: True
• 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: None
• hub_always_push: False
• gradient_checkpointing: False
• gradient_checkpointing_kwargs: None
• include_inputs_for_metrics: False
• include_for_metrics: []
• 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
• 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
• use_liger_kernel: False
• eval_use_gather_object: False
• average_tokens_across_devices: False
• prompts: None
• batch_sampler: no_duplicates
• multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	mnrl loss	cl loss	quora-duplicates_cosine_ap	quora-duplicates-dev_average_precision	cosine_ndcg@10
0	0	-	-	-	0.7461	0.5988	0.9831
0.2	100	0.2804	-	-	-	-	-
0.4	200	0.2006	-	-	-	-	-
0.5	250	-	0.1153	0.0157	0.7661	0.6165	0.9839
0.6	300	0.1704	-	-	-	-	-
0.8	400	0.1459	-	-	-	-	-
1.0	500	0.1296	0.0835	0.0146	0.7860	0.6238	0.9843
1.2	600	0.1344	-	-	-	-	-
1.4	700	0.1181	-	-	-	-	-
1.5	750	-	0.0737	0.0139	0.7983	0.6263	0.9847
1.6	800	0.1176	-	-	-	-	-
1.8	900	0.119	-	-	-	-	-
2.0	1000	0.1127	0.0682	0.0133	0.8121	0.6287	0.9852

• The bold row denotes the saved checkpoint.

Framework Versions

• Python: 3.12.9
• Sentence Transformers: 4.1.0
• Transformers: 4.52.4
• PyTorch: 2.7.0+cu126
• Accelerate: 1.7.0
• Datasets: 3.6.0
• Tokenizers: 0.21.1

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",
}

MultipleNegativesRankingLoss

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

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}
}