README.md

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: What is the purpose of the company's share repurchase program?
    sentences:
      - ' The potential risks and challenges include the possibility of not receiving a license, imposition of burdensome conditions, disadvantage against competitors, complicated and time-consuming management of licenses, harm to competitive position, and potential denial of licenses to significant customers.'
      - ' The purpose of the company''s share repurchase program is to offset dilution from shares issued to employees.'
      - ' The filing dates mentioned in the document are March 11, 2019, September 14, 2020, March 18, 2022, and March 8, 2023.'
  - source_sentence: >-
      How do NVIDIA allocate the total transaction price to each distinct
      performance obligation in a multiple performance obligations arrangement?
    sentences:
      - ' The factors that may affect a company''s effective tax rate include changes in business or statutory rates, mix of earnings in countries with different statutory tax rates, available tax incentives, credits and deductions, expiration of statutes of limitations, changes in accounting principles, adjustments to income taxes upon finalization of tax returns, increases in expenses not deductible for tax purposes, estimates of deferred tax assets and liabilities, changing interpretation of existing laws or regulations, impact of accounting for business combinations, and changes in the domestic or international organization of the business and structure.'
      - ' The graph compares the cumulative total shareholder return for NVIDIA''s common stock, the S&P 500 Index, and the Nasdaq 100 Index for the five years ended January 28, 2024.'
      - ' NVIDIA allocate the total transaction price to each distinct performance obligation on a relative standalone selling price basis.'
  - source_sentence: >-
      What is the amount of long-term deferred tax liabilities as of January 28,
      2024?
    sentences:
      - ' A 10% strengthening of the US dollar would result in a decrease of $116 million in accumulated other comprehensive income (loss) as of January 28, 2024, and $112 million as of January 29, 2023.'
      - ' Revenue from software licenses is recognized up front when the software is made available to the customer.'
      - ' $462 million.'
  - source_sentence: >-
      What is the amount of penalties related to unrecognized tax benefits that
      the company has accrued as of January 28, 2024?
    sentences:
      - ' Macroeconomic factors, including inflation, increased interest rates, capital market volatility, global supply chain constraints, and global economic and geopolitical developments, may have direct and indirect impacts on the company''s results of operations, particularly demand for its products, supply chain and manufacturing costs, employee wages, costs for capital equipment, and the value of its investments.'
      - ' $140 million.'
      - ' The key components of NVIDIA''s data center platform include GPUs, DPUs, CPUs, and a large body of software, including the CUDA parallel programming model, CUDA-X acceleration libraries, APIs, SDKs, and domain-specific application frameworks.'
  - source_sentence: What are the potential risks to the company's operating results?
    sentences:
      - ' The change resulted in an increase in operating income of $135 million and net income of $114 million after tax, or $0.05 per both basic and diluted share.'
      - ' Adverse rulings could occur, including monetary damages or fines, an injunction stopping the company from manufacturing or selling certain products, engaging in certain business practices, or requiring other remedies, such as compulsory licensing of patents.'
      - ' The company''s operating results may be impacted by challenges in integrating acquisition target systems, difficulties with system integration with key suppliers and customers, training and change management needs, loss of or inability to sell to a significant number of customers, and changes in purchasing patterns or decisions by channel partners.'
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: 0.4606741573033708
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.601123595505618
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.6685393258426966
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.7415730337078652
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.4606741573033708
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.20037453183520593
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.1337078651685393
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.0741573033707865
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.4606741573033708
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.601123595505618
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.6685393258426966
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.7415730337078652
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.5949908809486526
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.5487560192616373
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.5588326293039799
            name: Cosine Map@100
          - type: dot_accuracy@1
            value: 0.4606741573033708
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.601123595505618
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.6685393258426966
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.7415730337078652
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.4606741573033708
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.20037453183520593
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.1337078651685393
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.0741573033707865
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.4606741573033708
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.601123595505618
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.6685393258426966
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.7415730337078652
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.5949908809486526
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.5487560192616373
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.5588326293039799
            name: Dot Map@100
          - type: cosine_accuracy@1
            value: 0.7303370786516854
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8314606741573034
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8876404494382022
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9269662921348315
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.7303370786516854
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2771535580524344
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.1775280898876404
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09269662921348312
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.7303370786516854
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8314606741573034
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8876404494382022
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9269662921348315
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8245911858212284
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7921950240770463
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.797491792209672
            name: Cosine Map@100
          - type: dot_accuracy@1
            value: 0.7303370786516854
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.8314606741573034
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.8876404494382022
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.9269662921348315
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.7303370786516854
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.2771535580524344
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.1775280898876404
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.09269662921348312
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.7303370786516854
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.8314606741573034
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.8876404494382022
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.9269662921348315
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.8245911858212284
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.7921950240770463
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.797491792209672
            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")
# Run inference
sentences = [
    "What are the potential risks to the company's operating results?",
    " The company's operating results may be impacted by challenges in integrating acquisition target systems, difficulties with system integration with key suppliers and customers, training and change management needs, loss of or inability to sell to a significant number of customers, and changes in purchasing patterns or decisions by channel partners.",
    ' The change resulted in an increase in operating income of $135 million and net income of $114 million after tax, or $0.05 per both basic and diluted share.',
]
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	0.4607
cosine_accuracy@3	0.6011
cosine_accuracy@5	0.6685
cosine_accuracy@10	0.7416
cosine_precision@1	0.4607
cosine_precision@3	0.2004
cosine_precision@5	0.1337
cosine_precision@10	0.0742
cosine_recall@1	0.4607
cosine_recall@3	0.6011
cosine_recall@5	0.6685
cosine_recall@10	0.7416
cosine_ndcg@10	0.595
cosine_mrr@10	0.5488
cosine_map@100	0.5588
dot_accuracy@1	0.4607
dot_accuracy@3	0.6011
dot_accuracy@5	0.6685
dot_accuracy@10	0.7416
dot_precision@1	0.4607
dot_precision@3	0.2004
dot_precision@5	0.1337
dot_precision@10	0.0742
dot_recall@1	0.4607
dot_recall@3	0.6011
dot_recall@5	0.6685
dot_recall@10	0.7416
dot_ndcg@10	0.595
dot_mrr@10	0.5488
dot_map@100	0.5588

Information Retrieval

• Dataset: bge-base-en
• Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.7303
cosine_accuracy@3	0.8315
cosine_accuracy@5	0.8876
cosine_accuracy@10	0.927
cosine_precision@1	0.7303
cosine_precision@3	0.2772
cosine_precision@5	0.1775
cosine_precision@10	0.0927
cosine_recall@1	0.7303
cosine_recall@3	0.8315
cosine_recall@5	0.8876
cosine_recall@10	0.927
cosine_ndcg@10	0.8246
cosine_mrr@10	0.7922
cosine_map@100	0.7975
dot_accuracy@1	0.7303
dot_accuracy@3	0.8315
dot_accuracy@5	0.8876
dot_accuracy@10	0.927
dot_precision@1	0.7303
dot_precision@3	0.2772
dot_precision@5	0.1775
dot_precision@10	0.0927
dot_recall@1	0.7303
dot_recall@3	0.8315
dot_recall@5	0.8876
dot_recall@10	0.927
dot_ndcg@10	0.8246
dot_mrr@10	0.7922
dot_map@100	0.7975

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: 22.24 tokens max: 43 tokens	min: 3 tokens mean: 37.76 tokens max: 118 tokens

• Samples:

  anchor	positive
  What is the publication date of the NVIDIA Corporation Annual Report 2024?	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 during the preceding 12 months?	The purpose of this section is to comply with Rule 405 of Regulation S-T, which requires the registrant to submit electronic files for certain financial information.

• 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	-	0.5588
0.7111	2	-	0.5609
1.7778	5	-	0.5889
2.8444	8	-	0.6191
3.5556	10	0.749	-
3.9111	11	-	0.6704
4.9778	14	-	0.7009
5.6889	16	-	0.7158
6.7556	19	-	0.7454
7.1111	20	0.363	-
7.8222	22	-	0.7633
8.8889	25	-	0.7685
9.9556	28	-	0.7816
10.6667	30	0.2181	0.7857
11.7333	33	-	0.7866
12.8	36	-	0.7939
13.8667	39	-	0.7953
14.2222	40	0.1552	-
14.9333	42	-	0.7962
16.0	45	-	0.7975
16.7111	47	-	0.7975
17.7778	50	0.1315	0.7975

• 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: 1.1.1
• 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}
}