RishuD7/bge-base-en-v1.5-41-keys-phase-2-v1

BGE base En v1.5 version 1

This is a sentence-transformers model finetuned from BAAI/bge-base-en-v1.5. 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 tokens
• Similarity Function: Cosine Similarity
• Language: en
• License: apache-2.0

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("RishuD7/bge-base-en-v1.5-41-keys-phase-2-v1")
# Run inference
sentences = [
    '13.2 We accept liability to the extent arising from our negligence, breach of contract or nbn™ Activities: (a) for any personal injury or death to you or your Personnel resulting from the supply of the Services; (b) for any damage to your real or tangible property resulting from the supply of the Services, but we limit our liability to our choice of repairing or replacing the property or paying the cost of repairing or replacing it; or (c) unless clause 13.1 applies, for any other cost or expense you reasonably incur that is a direct result of and flows naturally from, our breach of contract, negligence or nbn™ Activities (but TELSTRA CORPORATION LIMITED (ABN 33 051 775 556) | PAGE 6 OF 25 DocuSign Envelope ID: 3EE2487C-8AA0-42DB-8C95-FD658789EC41 CONFIDENTIAL excluding loss of profits, revenue, business opportunities, likely savings and data), and our liability under this clause is limited for all claims in aggregate to the total amount payable to us under this Agreement during the first year of this Agreement.\n                      Intellectual Property Rights means all current and future registered rights in respect of copyright,\n                      designs, circuit layouts, trademarks, trade secrets, domain names, database rights, know-how and\n                      confidential information and any other intellectual property rights as defined by Article 2 of the World\n                      Intellectual Property Organisation Convention of July 1967, excluding patents.\n                      nbn™ means nbn co limited (ABN 86 136 533 741), as that company exists from time to time.\n                      nbn™ Activities means nbn™ Equipment and nbn™’s negligent or wilful acts or omissions in\n                      connection with the Services.\n                      nbn™ Equipment means any equipment that is owned, operated or controlled by nbn™.\n                      nbn™ Service means a Service that is supplied by or using nbn™ or nbn™ Equipment.\n.\n                      Our Customer Terms means the Standard Form of Agreement formulated by Telstra for the purposes\n                      of Part 23 of the Act, as amended by us from time to time in accordance with the Act.\n.\n                      Personnel means a person’s officers, employees, agents, contractors and sub-contractors and in our\n                      case includes our Related Bodies Corporate.\n.\n                      Planned Maintenance has the meaning in clause 10.1.\n.\n                      Related Bodies Corporate has the meaning given under the Corporations Act 2001 (Cth).\n.\n                      Service means a service under this Agreement set out or referred to in a Service Schedule or an\n                      agreed statement of work, and includes any individual service or component which constitutes the\n                      service.\n.\n                      Service Order Form means an agreed:\n                      (a)          application or order form for a new Service or to vary, reconfigure, renew, reconfigure or\n                                   cancel an existing Service; or\n                      (b)          statement of work between the parties for services under a Service Schedule or otherwise.\n.\n          TELSTRA CORPORATION LIMITED (ABN 33 051 775 556) |                                                            PAGE 10 OF 25\nDocuSign Envelope ID: 3EE2487C-8AA0-42DB-8C95-FD658789EC41\n                                                                                                                       CONFIDENTIAL\n                      Service Schedules means the Schedules attached or added to these Agreement Terms for a\n                      Service.\n',
    'Absolute Maximum Amount of Liability',
    'Late Payment Charges',
]
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

Information Retrieval

• Dataset: dim_768
• Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.0067
cosine_accuracy@3	0.02
cosine_accuracy@5	0.0278
cosine_accuracy@10	0.0622
cosine_precision@1	0.0067
cosine_precision@3	0.0067
cosine_precision@5	0.0056
cosine_precision@10	0.0062
cosine_recall@1	0.0067
cosine_recall@3	0.02
cosine_recall@5	0.0278
cosine_recall@10	0.0622
cosine_ndcg@10	0.0289
cosine_mrr@10	0.019
cosine_map@100	0.0321

Information Retrieval

• Dataset: dim_512
• Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.0089
cosine_accuracy@3	0.0222
cosine_accuracy@5	0.0267
cosine_accuracy@10	0.0656
cosine_precision@1	0.0089
cosine_precision@3	0.0074
cosine_precision@5	0.0053
cosine_precision@10	0.0066
cosine_recall@1	0.0089
cosine_recall@3	0.0222
cosine_recall@5	0.0267
cosine_recall@10	0.0656
cosine_ndcg@10	0.0308
cosine_mrr@10	0.0206
cosine_map@100	0.0336

Information Retrieval

• Dataset: dim_256
• Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.0056
cosine_accuracy@3	0.0167
cosine_accuracy@5	0.0289
cosine_accuracy@10	0.0589
cosine_precision@1	0.0056
cosine_precision@3	0.0056
cosine_precision@5	0.0058
cosine_precision@10	0.0059
cosine_recall@1	0.0056
cosine_recall@3	0.0167
cosine_recall@5	0.0289
cosine_recall@10	0.0589
cosine_ndcg@10	0.0263
cosine_mrr@10	0.0167
cosine_map@100	0.0306

Information Retrieval

• Dataset: dim_128
• Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.0056
cosine_accuracy@3	0.0189
cosine_accuracy@5	0.0322
cosine_accuracy@10	0.0611
cosine_precision@1	0.0056
cosine_precision@3	0.0063
cosine_precision@5	0.0064
cosine_precision@10	0.0061
cosine_recall@1	0.0056
cosine_recall@3	0.0189
cosine_recall@5	0.0322
cosine_recall@10	0.0611
cosine_ndcg@10	0.0281
cosine_mrr@10	0.0183
cosine_map@100	0.0324

Information Retrieval

• Dataset: dim_64
• Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.0089
cosine_accuracy@3	0.02
cosine_accuracy@5	0.0378
cosine_accuracy@10	0.0667
cosine_precision@1	0.0089
cosine_precision@3	0.0067
cosine_precision@5	0.0076
cosine_precision@10	0.0067
cosine_recall@1	0.0089
cosine_recall@3	0.02
cosine_recall@5	0.0378
cosine_recall@10	0.0667
cosine_ndcg@10	0.0319
cosine_mrr@10	0.0215
cosine_map@100	0.0355

Training Details

Training Dataset

Unnamed Dataset

• Size: 4,894 training samples
• Columns: positive and anchor
• Approximate statistics based on the first 1000 samples:

  	positive	anchor
  type	string	string
  details	min: 123 tokens mean: 353.07 tokens max: 512 tokens	min: 3 tokens mean: 5.37 tokens max: 8 tokens

• Samples:

  positive	anchor
  In no event shall CBRE, Client, or their respective affiliates incur liability under this agreement or otherwise relating to the Services beyond the insurance proceeds available with respect to the particular matter under the Insurance Policies required to be carried by CBRE AND Client under Article 6 above including, if applicable, proceeds of self-insurance. Each party shall and shall cause its affiliates to look solely to such insurance proceeds (and any such proceeds paid through self-insurance) to satisfy its claims against the released parties and agrees that it shall have no right of recovery beyond such proceeds; provided, however, that if insurance proceeds under such policies are not paid because a party has failed to maintain such policies, comply with policy requirements or, in the case of self-insurance, unreasonably denied a claim, such party shall be liable for the amounts that otherwise would have been payable under such policies had such party maintained such policies, complied with the policy requirement or not unreasonably denied such claim, as the case may be.	Absolute Maximum Amount of Liability
  4. Rent. 4.01 From and after the Commencement Date, Tenant shall pay Landlord, without any setoff or deduction, unless expressly set forth in this Lease, all Base Rent and Additional Rent due for the Term (collectively referred to as "Rent"). "Additional Rent" means all sums (exclusive of Base Rent) that Tenant is required to pay Landlord under this Lease. Tenant shall pay and be liable for all rental, sales and use taxes (but excluding income taxes), if any, imposed upon or measured by Rent. Base Rent and recurring monthly charges of Additional Rent shall be due and payable in advance on the first day of each calendar month without notice or demand, provided that the installment of Base Rent attributable to the first (1st) full calendar month of the Term following the Abatement Period shall be due concurrently with the execution of this Lease by Tenant. All other items of Rent shall be due and payable on or before thirty (30) days after billing by Landlord. Rent shall be made payable to the entity, and sent to the address, that Landlord designates and shall be made by good and sufficient check or by other means acceptable to Landlord. Landlord may return to Tenant, at any time within fifteen (15) days after receiving same, any payment of Rent (a) made following any Default (irrespective of whether Landlord has commenced the exercise of any remedy), or (b) that is less than the amount due. Each such returned payment (whether made by returning Tenant's actual check, or by issuing a refund in the event Tenant's check was deposited) shall be conclusively presumed not to have been received or approved by Landlord. If Tenant does not pay any Rent when due hereunder, Tenant shall pay Landlord an administration fee in the amount of five percent (5%) of the past due amount. In addition, past due Rent shall accrue interest at a rate equal to the lesser of (i) twelve percent (12%) per annum or (ii) the maximum legal rate, and Tenant shall pay Landlord a fee for any checks returned by Tenant's bank for any reason. Notwithstanding the foregoing, no such late charge or of interest shall be imposed with respect to the first (1st) late payment in any calendar year, but not with respect to more than three (3) such late payments during the initial Term of this Lease.	Late Payment Charges
  Term This Agreement shall come into force and shall last unlimited from such date. Either Party may however terminate this Agreement at any time by giving upon thirty (30) days' written notice to the other Party. The Receiving Party's obligations contained in this Agreement to keep confidential and restrict use of the Disclosing Party's Confidential Information shall sur- vive for a period of five (5) years from the date of its termination for any reason whatsoever. lX. Contractual penalty For the purposes of this Non-Disclosure Agreement, " Confidential Information" includes all technical and/or commercial and/or financial information in the field designated in section 1., which a contracting Party (hereinafter referred to as the "EQ€i1gPedy") makes, or has made, accessible to the other contracting Party (hereinafter referred to as the ".&eiyi!g Partv") in oral, written, tangible or other form (e.9. disk, data carrier) directly or indirectly, in- cluding but not limited to, drawings, models, components, and other material. Confidential In- formation is to be identified as such. Orally communicated or visually, information having been designated as confidential at the time of disclosure will be confirmed as such in writing by the Disclosing Party within 30 (thirty) days from such disclosure being understood thatlhe ./A information will be considered Confidential Information during that period of 30 (thirty) days. /L t'-4 PF 0233 (September 2016) page 1 of 5 ä =. PFEIFFER F . F . VACUUM	Termination for Convenience

• Loss: MatryoshkaLoss with these parameters:

  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          768,
          512,
          256,
          128,
          64
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: epoch
• per_device_train_batch_size: 32
• per_device_eval_batch_size: 16
• gradient_accumulation_steps: 16
• learning_rate: 2e-05
• num_train_epochs: 30
• lr_scheduler_type: cosine
• warmup_ratio: 0.1
• tf32: False
• load_best_model_at_end: True
• optim: adamw_torch_fused
• batch_sampler: no_duplicates

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: epoch
• prediction_loss_only: True
• per_device_train_batch_size: 32
• per_device_eval_batch_size: 16
• per_gpu_train_batch_size: None
• per_gpu_eval_batch_size: None
• gradient_accumulation_steps: 16
• eval_accumulation_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: 30
• max_steps: -1
• lr_scheduler_type: cosine
• 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: False
• fp16: False
• fp16_opt_level: O1
• half_precision_backend: auto
• bf16_full_eval: False
• fp16_full_eval: False
• tf32: False
• 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_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: False
• hub_always_push: False
• gradient_checkpointing: False
• gradient_checkpointing_kwargs: None
• include_inputs_for_metrics: False
• 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
• dispatch_batches: None
• split_batches: None
• include_tokens_per_second: False
• include_num_input_tokens_seen: False
• neftune_noise_alpha: None
• optim_target_modules: None
• batch_eval_metrics: False
• batch_sampler: no_duplicates
• multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	dim_128_cosine_map@100	dim_256_cosine_map@100	dim_512_cosine_map@100	dim_64_cosine_map@100	dim_768_cosine_map@100
1.0458	10	11.285	-	-	-	-	-
2.0915	20	2.1467	-	-	-	-	-
3.1373	30	0.2715	-	-	-	-	-
4.1830	40	0.0	-	-	-	-	-
5.0196	48	-	0.0250	0.0238	0.0258	0.0234	0.0253
1.1830	50	2.0636	-	-	-	-	-
2.2288	60	5.6313	-	-	-	-	-
3.2745	70	0.3035	-	-	-	-	-
4.3203	80	0.0347	-	-	-	-	-
5.3660	90	0.0	-	-	-	-	-
5.9935	96	-	0.0293	0.0297	0.0304	0.0323	0.0297
2.3660	100	2.3496	-	-	-	-	-
3.4118	110	2.3024	-	-	-	-	-
4.4575	120	0.0451	-	-	-	-	-
5.5033	130	0.0021	-	-	-	-	-
6.5490	140	0.0	-	-	-	-	-
6.9673	144	-	0.0318	0.0308	0.0308	0.031	0.031
3.5490	150	2.6928	-	-	-	-	-
4.5948	160	1.0232	-	-	-	-	-
5.6405	170	0.0082	-	-	-	-	-
6.6863	180	0.0	-	-	-	-	-
7.7320	190	0.0	-	-	-	-	-
8.0458	193	-	0.0331	0.0319	0.0333	0.0315	0.0335
4.7320	200	2.635	-	-	-	-	-
5.7778	210	0.3362	-	-	-	-	-
6.8235	220	0.0005	-	-	-	-	-
7.8693	230	0.0	-	-	-	-	-
8.9150	240	0.0	-	-	-	-	-
9.0196	241	-	0.0311	0.0307	0.0322	0.0348	0.0324
5.9150	250	2.7229	-	-	-	-	-
6.9608	260	0.0297	-	-	-	-	-
8.0065	270	0.0003	0.0324	0.0306	0.0336	0.0355	0.0321

• The bold row denotes the saved checkpoint.

Framework Versions

• Python: 3.10.12
• Sentence Transformers: 3.1.1
• Transformers: 4.41.2
• PyTorch: 2.1.2+cu121
• Accelerate: 0.34.2
• Datasets: 2.19.1
• 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",
}

MatryoshkaLoss

@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning},
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

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}
}