embedding_fine_tuning_with_prompts_bge_large_en_v1.5

bge-large-en-v1.5

This is a sentence-transformers model finetuned from BAAI/bge-large-en-v1.5 on the natural-questions dataset. It maps sentences & paragraphs to a 1024-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-large-en-v1.5 <!-- at revision d4aa6901d3a41ba39fb536a557fa166f842b0e09 -->
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 1024 dimensions
• Similarity Function: Cosine Similarity
• Training Dataset:
  • natural-questions
• Language: en
• License: mit

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, 'architecture': 'BertModel'})
  (1): Pooling({'word_embedding_dimension': 1024, '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("DannyAI/embedding_fine_tuning_with_prompts_bge_large_en_v1.5")
# Run inference
queries = [
    "what was agenda 21 of earth summit of rio de janeiro",
]
documents = [
    'Agenda 21 Agenda 21 is a non-binding, action plan of the United Nations with regard to sustainable development.[1] It is a product of the Earth Summit (UN Conference on Environment and Development) held in Rio de Janeiro, Brazil, in 1992. It is an action agenda for the UN, other multilateral organizations, and individual governments around the world that can be executed at local, national, and global levels.',
    'Jab Harry Met Sejal Jab Harry Met Sejal (English: When Harry Met Sejal) is a 2017 Indian romantic comedy film written and directed by Imtiaz Ali. It features Shah Rukh Khan and Anushka Sharma in the lead roles,[1] their third collaboration after Rab Ne Bana Di Jodi (2008) and Jab Tak Hai Jaan (2012). Pre-production of the film begun in April 2015 and principal photography commenced in August 2016 in Prague, Amsterdam, Vienna, Lisbon and Budapest.',
    'Pencil Most manufacturers, and almost all in Europe, designate their pencils with the letters H (commonly interpreted as "hardness") to B (commonly "blackness"), as well as F (usually taken to mean "fineness", although F pencils are no more fine or more easily sharpened than any other grade. also known as "firm" in Japan[68]). The standard writing pencil is graded HB.[69] This designation might have been first used in the early 20th century by Brookman, an English pencil maker. It used B for black and H for hard; a pencil\'s grade was described by a sequence or successive Hs or Bs such as BB and BBB for successively softer leads, and HH and HHH for successively harder ones.[70] The Koh-i-Noor Hardtmuth pencil manufacturers claim to have first used the HB designations, with H standing for Hardtmuth, B for the company\'s location of Budějovice, and F for Franz Hardtmuth, who was responsible for technological improvements in pencil manufacture.[71][72]',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 1024] [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[0.9017, 0.2307, 0.2148]])

<!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* -->

Evaluation

Metrics

Information Retrieval

• Dataset: NanoQuoraRetrieval
• Evaluated with <code>InformationRetrievalEvaluator</code> with these parameters:

  {
      "query_prompt": "query: ",
      "corpus_prompt": "document: "
  }
  

Information Retrieval

• Dataset: NanoQuoraRetrieval
• Evaluated with <code>InformationRetrievalEvaluator</code> with these parameters:

  {
      "query_prompt": "query: ",
      "corpus_prompt": "document: "
  }
  

<!-- ## Bias, Risks and Limitations *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* -->

Training Details

Training Dataset

natural-questions

• Dataset: natural-questions at f9e894e
• Size: 64,147 training samples
• Columns: <code>query</code> and <code>answer</code>
• Approximate statistics based on the first 1000 samples:

  	query	answer
  type	string	string
  details	<ul><li>min: 10 tokens</li><li>mean: 11.81 tokens</li><li>max: 26 tokens</li></ul>	<ul><li>min: 21 tokens</li><li>mean: 137.28 tokens</li><li>max: 512 tokens</li></ul>

• Samples:

  query	answer
  <code>the internal revenue code is part of federal statutory law. true false</code>	<code>Internal Revenue Code The Internal Revenue Code (IRC), formally the Internal Revenue Code of 1986, is the domestic portion of federal statutory tax law in the United States, published in various volumes of the United States Statutes at Large, and separately as Title 26 of the United States Code (USC).[1] It is organized topically, into subtitles and sections, covering income tax (see Income tax in the United States), payroll taxes, estate taxes, gift taxes, and excise taxes; as well as procedure and administration. Its implementing agency is the Internal Revenue Service.</code>
  <code>where is the pyramid temple at borobudur located</code>	<code>Borobudur Approximately 40 kilometres (25 mi) northwest of Yogyakarta and 86 kilometres (53 mi) west of Surakarta, Borobudur is located in an elevated area between two twin volcanoes, Sundoro-Sumbing and Merbabu-Merapi, and two rivers, the Progo and the Elo. According to local myth, the area known as Kedu Plain is a Javanese "sacred" place and has been dubbed "the garden of Java" due to its high agricultural fertility.[19] During the restoration in the early 20th century, it was discovered that three Buddhist temples in the region, Borobudur, Pawon and Mendut, are positioned along a straight line.[20] A ritual relationship between the three temples must have existed, although the exact ritual process is unknown.[14]</code>
  <code>what does uncle stand for in the show man from uncle</code>	<code>The Man from U.N.C.L.E. Originally, co-creator Sam Rolfe wanted to leave the meaning of U.N.C.L.E. ambiguous so it could refer to either "Uncle Sam" or the United Nations.[2]:14 Concerns by Metro-Goldwyn-Mayer's (MGM) legal department about using "U.N." for commercial purposes resulted in the producers' clarification that U.N.C.L.E. was an acronym for the United Network Command for Law and Enforcement.[3] Each episode had an "acknowledgement" to the U.N.C.L.E. in the end titles.</code>

• Loss: <code>CachedMultipleNegativesRankingLoss</code> with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim",
      "mini_batch_size": 16,
      "gather_across_devices": false
  }
  

Evaluation Dataset

natural-questions

• Dataset: natural-questions at f9e894e
• Size: 16,037 evaluation samples
• Columns: <code>query</code> and <code>answer</code>
• Approximate statistics based on the first 1000 samples:

  	query	answer
  type	string	string
  details	<ul><li>min: 10 tokens</li><li>mean: 11.67 tokens</li><li>max: 22 tokens</li></ul>	<ul><li>min: 12 tokens</li><li>mean: 134.64 tokens</li><li>max: 512 tokens</li></ul>

• Samples:

  query	answer
  <code>when did last harry potter movie come out</code>	<code>Harry Potter (film series) Harry Potter is a British-American film series based on the Harry Potter novels by author J. K. Rowling. The series is distributed by Warner Bros. and consists of eight fantasy films, beginning with Harry Potter and the Philosopher's Stone (2001) and culminating with Harry Potter and the Deathly Hallows – Part 2 (2011).[2][3] A spin-off prequel series will consist of five films, starting with Fantastic Beasts and Where to Find Them (2016). The Fantastic Beasts films mark the beginning of a shared media franchise known as J. K. Rowling's Wizarding World.[4]</code>
  <code>where did the saying debbie downer come from</code>	<code>Debbie Downer The character's name, Debbie Downer, is a slang phrase which refers to someone who frequently adds bad news and negative feelings to a gathering, thus bringing down the mood of everyone around them. Dratch's character would usually appear at social gatherings and interrupt the conversation to voice negative opinions and pronouncements. She is especially concerned about the rate of feline AIDS, a subject that she would bring up on more than one occasion, saying it was the number one killer of domestic cats.</code>
  <code>the financial crisis of 2008 was caused by</code>	<code>Financial crisis of 2007–2008 It began in 2007 with a crisis in the subprime mortgage market in the United States, and developed into a full-blown international banking crisis with the collapse of the investment bank Lehman Brothers on September 15, 2008.[5] Excessive risk-taking by banks such as Lehman Brothers helped to magnify the financial impact globally.[6] Massive bail-outs of financial institutions and other palliative monetary and fiscal policies were employed to prevent a possible collapse of the world financial system. The crisis was nonetheless followed by a global economic downturn, the Great Recession. The European debt crisis, a crisis in the banking system of the European countries using the euro, followed later.</code>

• Loss: <code>CachedMultipleNegativesRankingLoss</code> with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim",
      "mini_batch_size": 16,
      "gather_across_devices": false
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 5
• per_device_eval_batch_size: 5
• learning_rate: 2e-05
• max_steps: 100
• warmup_ratio: 0.1
• seed: 30
• bf16: True
• load_best_model_at_end: True
• prompts: {'query': 'query: ', 'answer': 'document: '}
• batch_sampler: no_duplicates

All Hyperparameters

<details><summary>Click to expand</summary>

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: steps
• prediction_loss_only: True
• per_device_train_batch_size: 5
• per_device_eval_batch_size: 5
• 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: 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: 3.0
• max_steps: 100
• 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: 30
• 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}
• parallelism_config: None
• deepspeed: None
• label_smoothing_factor: 0.0
• optim: adamw_torch_fused
• 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
• hub_revision: None
• 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
• liger_kernel_config: None
• eval_use_gather_object: False
• average_tokens_across_devices: False
• prompts: {'query': 'query: ', 'answer': 'document: '}
• batch_sampler: no_duplicates
• multi_dataset_batch_sampler: proportional
• router_mapping: {}
• learning_rate_mapping: {}

</details>

Training Logs

• The bold row denotes the saved checkpoint.

Framework Versions

• Python: 3.12.11
• Sentence Transformers: 5.1.0
• Transformers: 4.56.1
• PyTorch: 2.8.0+cu126
• Accelerate: 1.10.1
• Datasets: 4.0.0
• Tokenizers: 0.22.0

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",
}

CachedMultipleNegativesRankingLoss

@misc{gao2021scaling,
    title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup},
    author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
    year={2021},
    eprint={2101.06983},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

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