metadata

tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:178
  - loss:MultipleNegativesRankingLoss
base_model: BAAI/bge-base-en-v1.5
widget:
  - source_sentence: >-
      Where can investors find more information about NVIDIA's financial
      information and company updates?
    sentences:
      - ' The potential risks include restrictions on sales of products containing certain components made by Micron, restrictions on receiving supply of components, parts, or services from Taiwan, increased scrutiny from shareholders, regulators, and others regarding corporate sustainability practices, and failure to meet evolving shareholder, regulator, or other industry stakeholder expectations, which could result in additional costs, reputational harm, and loss of customers and suppliers.'
      - ' Investors and others can find more information about NVIDIA''s financial information and company updates on the company''s investor relations website, through press releases, SEC filings, public conference calls and webcasts, as well as on the company''s social media channels, including Twitter, the NVIDIA Corporate Blog, Facebook, LinkedIn, Instagram, and YouTube.'
      - ' The text mentions the following forms and agreements: Officers'' Certificate, Form of Note (with various years), Form of Indemnity Agreement, Amended and Restated 2007 Equity Incentive Plan, Non-Employee Director Deferred Restricted Stock Unit Grant Notice and Deferred Restricted Stock Unit Agreement, Non-Employee Director Restricted Stock Unit Grant Notice and Restricted Stock Unit Agreement, Global Performance-Based Restricted Stock Unit Grant Notice and Performance-Based Restricted Stock Unit Agreement, Global Restricted Stock Unit Grant Notice and Global Restricted Stock Unit Agreement, and various Schedules and Exhibits (such as 2.1, 3.1, 4.1, 4.2, 10.1, 10.2, 10.26, and 10.27).'
  - source_sentence: >-
      What are the potential consequences if regulators in China conclude that
      NVIDIA has failed to fulfill its commitments or has violated applicable
      law in China?
    sentences:
      - ' The company''s share repurchase program aims to offset dilution from shares issued to employees.'
      - ' Ms. Shoquist served as Senior Vice President and General Manager of the Electro-Optics business at Coherent, Inc., and previously worked at Quantum Corp. as President of the Personal Computer Hard Disk Drive Division, and at Hewlett-Packard.'
      - ' If regulators in China conclude that NVIDIA has failed to fulfill its commitments or has violated applicable law in China, the company could be subject to various penalties or restrictions on its ability to conduct its business, which could have a material and adverse impact on its business, operating results, and financial condition.'
  - source_sentence: >-
      What percentage of the company's revenue was attributed to sales to
      customers outside of the United States in fiscal year 2024?
    sentences:
      - ' NVIDIA reports its business results in two segments: the Compute & Networking segment and the Graphics segment.'
      - ' The company expects to use its existing cash, cash equivalents, and marketable securities, as well as the cash generated by its operations, to fund its capital investments of approximately $3.5 billion to $4.0 billion related to property and equipment during fiscal year 2025.'
      - ' 56% of the company''s total revenue in fiscal year 2024 was attributed to sales to customers outside of the United States.'
  - source_sentence: >-
      What is the net income per share of NVIDIA Corporation for the year ended
      January 29, 2023?
    sentences:
      - ' 6% of the company''s workforce in the United States is composed of Black or African American employees.'
      - ' The net income per share of NVIDIA Corporation for the year ended January 29, 2023 is $12.05 for basic and $11.93 for diluted.'
      - ' The company may face potential risks and challenges such as increased expenses, substantial expenditures and time spent to fully resume operations, disruption to product development or operations due to employees being called-up for active military duty, and potential impact on future product development, operations, and revenue. Additionally, the company may also experience interruptions or delays in services from third-party providers, which could impair its ability to provide its products and services and harm its business.'
  - source_sentence: >-
      What percentage of the company's accounts receivable balance as of January
      28, 2024, was accounted for by two customers?
    sentences:
      - ' The change in equipment and assembly and test equipment resulted in a benefit of $135 million in operating income and $114 million in net income, or $0.05 per both basic and diluted share, for the fiscal year ended January 28, 2024.'
      - ' The estimates of deferred tax assets and liabilities may change based on added certainty or finality to an anticipated outcome, changes in accounting standards or tax laws in the U.S. or foreign jurisdictions where the company operates, or changes in other facts or circumstances.'
      - ' 24% and 11%, which is a total of 35%.'
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
  - cosine_accuracy@1
  - cosine_accuracy@3
  - cosine_accuracy@5
  - cosine_accuracy@10
  - cosine_precision@1
  - cosine_precision@3
  - cosine_precision@5
  - cosine_precision@10
  - cosine_recall@1
  - cosine_recall@3
  - cosine_recall@5
  - cosine_recall@10
  - cosine_ndcg@10
  - cosine_mrr@10
  - cosine_map@100
  - dot_accuracy@1
  - dot_accuracy@3
  - dot_accuracy@5
  - dot_accuracy@10
  - dot_precision@1
  - dot_precision@3
  - dot_precision@5
  - dot_precision@10
  - dot_recall@1
  - dot_recall@3
  - dot_recall@5
  - dot_recall@10
  - dot_ndcg@10
  - dot_mrr@10
  - dot_map@100
model-index:
  - name: SentenceTransformer based on BAAI/bge-base-en-v1.5
    results:
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: bge base en
          type: bge-base-en
        metrics:
          - type: cosine_accuracy@1
            value: 0.9269662921348315
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.9831460674157303
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.9943820224719101
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 1
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.9269662921348315
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.3277153558052434
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.198876404494382
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09999999999999998
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.9269662921348315
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.9831460674157303
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.9943820224719101
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 1
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.9682702490705566
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.9575842696629214
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.9575842696629213
            name: Cosine Map@100
          - type: dot_accuracy@1
            value: 0.9269662921348315
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.9831460674157303
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.9943820224719101
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 1
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.9269662921348315
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.3277153558052434
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.198876404494382
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.09999999999999998
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.9269662921348315
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.9831460674157303
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.9943820224719101
            name: Dot Recall@5
          - type: dot_recall@10
            value: 1
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.9682702490705566
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.9575842696629214
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.9575842696629213
            name: Dot Map@100

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 train dataset. 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
Training Dataset:
- train

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("rezarahim/bge-finetuned-detail")
# Run inference
sentences = [
    "What percentage of the company's accounts receivable balance as of January 28, 2024, was accounted for by two customers?",
    ' 24% and 11%, which is a total of 35%.',
    ' The change in equipment and assembly and test equipment resulted in a benefit of $135 million in operating income and $114 million in net income, or $0.05 per both basic and diluted share, for the fiscal year ended January 28, 2024.',
]
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: bge-base-en
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.927
cosine_accuracy@3	0.9831
cosine_accuracy@5	0.9944
cosine_accuracy@10	1.0
cosine_precision@1	0.927
cosine_precision@3	0.3277
cosine_precision@5	0.1989
cosine_precision@10	0.1
cosine_recall@1	0.927
cosine_recall@3	0.9831
cosine_recall@5	0.9944
cosine_recall@10	1.0
cosine_ndcg@10	0.9683
cosine_mrr@10	0.9576
cosine_map@100	0.9576
dot_accuracy@1	0.927
dot_accuracy@3	0.9831
dot_accuracy@5	0.9944
dot_accuracy@10	1.0
dot_precision@1	0.927
dot_precision@3	0.3277
dot_precision@5	0.1989
dot_precision@10	0.1
dot_recall@1	0.927
dot_recall@3	0.9831
dot_recall@5	0.9944
dot_recall@10	1.0
dot_ndcg@10	0.9683
dot_mrr@10	0.9576
dot_map@100	0.9576

Training Details

Training Dataset

train

Dataset: train
Size: 178 training samples
Columns: anchor and positive
Approximate statistics based on the first 178 samples:
anchor positive
type string string
details
min: 10 tokens
mean: 23.63 tokens
max: 46 tokens

min: 4 tokens
mean: 66.67 tokens
max: 313 tokens

	anchor	positive
type	string	string
details	min: 10 tokens mean: 23.63 tokens max: 46 tokens	min: 4 tokens mean: 66.67 tokens max: 313 tokens

Samples:

anchor	positive
`What is the publication date of the NVIDIA Corporation Annual Report 2024?`	`The publication date of the NVIDIA Corporation Annual Report 2024 is February 21st, 2024.`
`What is the filing date of the 10-K report for NVIDIA Corporation in 2004?`	`The filing dates of the 10-K reports for NVIDIA Corporation in 2004 are May 20th, March 29th, and April 25th.`
`What is the purpose of the section of the filing that requires the registrant to indicate whether it has submitted electronically every Interactive Data File required to be submitted pursuant to Rule 405 of Regulation S-T?`	`The purpose of this section is to require the registrant to disclose whether it has submitted all required Interactive Data Files electronically, as mandated by Rule 405 of Regulation S-T, during the preceding 12 months or for the shorter period that the registrant was required to submit such files.`

Loss: MultipleNegativesRankingLoss with these parameters:

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

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: epoch
per_device_train_batch_size: 4
per_device_eval_batch_size: 16
gradient_accumulation_steps: 16
learning_rate: 2e-05
num_train_epochs: 25
lr_scheduler_type: cosine
warmup_ratio: 0.1
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: 4
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
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: 25
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: 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_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
eval_on_start: False
use_liger_kernel: False
eval_use_gather_object: False
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	bge-base-en_cosine_map@100
0	0	-	0.8574
0.7111	2	-	0.8591
1.7778	5	-	0.8757
2.8444	8	-	0.9012
3.5556	10	0.2885	-
3.9111	11	-	0.9134
4.9778	14	-	0.9277
5.6889	16	-	0.9391
6.7556	19	-	0.9463
7.1111	20	0.0644	-
7.8222	22	-	0.9506
8.8889	25	-	0.9515
9.9556	28	-	0.9555
10.6667	30	0.0333	0.9560
11.7333	33	-	0.9551
12.8	36	-	0.9569
13.8667	39	-	0.9579
14.2222	40	0.0157	-
14.9333	42	-	0.9576
16.0	45	-	0.9576
16.7111	47	-	0.9576
17.7778	50	0.0124	0.9576

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.1.1
Transformers: 4.45.2
PyTorch: 2.5.1+cu121
Accelerate: 1.2.1
Datasets: 3.1.0
Tokenizers: 0.20.3

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}
}