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

	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}
}

RishuD7
/

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

BGE base En v1.5 version 1

Model Details

Model Description

Model Sources

Full Model Architecture

Usage

Direct Usage (Sentence Transformers)

Evaluation

Metrics

Information Retrieval

Information Retrieval

Information Retrieval

Information Retrieval

Information Retrieval

Training Details

Training Dataset

Unnamed Dataset

Training Hyperparameters

Non-Default Hyperparameters

All Hyperparameters

Training Logs

Framework Versions

Citation

BibTeX

Sentence Transformers

MatryoshkaLoss

MultipleNegativesRankingLoss

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

Evaluation results