Add new SentenceTransformer model.

423452e verified about 2 years ago

28.6 kB

language:
  - en
license: apache-2.0
library_name: sentence-transformers
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:6300
  - loss:MatryoshkaLoss
  - loss:MultipleNegativesRankingLoss
base_model: BAAI/bge-base-en-v1.5
datasets: []
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@10
widget:
  - source_sentence: >-
      The Gross Merchandise Sales (GMS) decreased by 1.2% in 2023 compared to
      2022.
    sentences:
      - What specific matters did the CFPB investigate concerning Equifax?
      - >-
        What was the percentage decline in GMS for the year ended December 31,
        2023 compared to 2022?
      - >-
        What percentage of eBay's 2023 net revenues were attributed to
        international markets?
  - source_sentence: >-
      Asset management and administration fees vary with changes in the balances
      of client assets due to market fluctuations and client activity.
    sentences:
      - >-
        Why was there a net outflow of cash in financing activities in fiscal
        2022?
      - >-
        How do asset management and administration fees vary at The Charles
        Schwab Corporation?
      - What are some key goals of the corporation related to climate change?
  - source_sentence: >-
      Operating profit margin was 19.3 percent in 2023, compared with 13.3
      percent in 2022.
    sentences:
      - What was the operating profit margin for 2023?
      - How do the studios compete in the entertainment industry?
      - >-
        What types of audio products does Garmin's Fusion and JL Audio brands
        offer?
  - source_sentence: >-
      Subsequent to 2023, on February 12, 2024, AbbVie borrowed $5.0 billion
      under the term loan credit agreement.
    sentences:
      - >-
        What percentage of U.S. dialysis patient service revenues in 2023 came
        from Medicare and Medicare Advantage plans?
      - >-
        What is Peloton Interactive, Inc. known for in the interactive fitness
        industry?
      - >-
        What was the purpose stated by AbbVie for borrowing $5.0 billion under
        the term loan credit agreement on February 12, 2024?
  - source_sentence: >-
      Chipotle retains an independent third-party compensation consultant each
      year to conduct a pay equity analysis of its U.S. and Canadian workforce,
      including factors of pay such as grade level, tenure in role, and external
      market conditions like geographic location, to ensure consistency and
      equitable treatment among employees.
    sentences:
      - How does Chipotle ensure pay equity among its employees?
      - >-
        How can one locate information on legal proceedings within the
        Consolidated Financial Statements?
      - >-
        What criteria did the independent audit use to assess the effectiveness
        of internal control over financial reporting at the company?
pipeline_tag: sentence-similarity
model-index:
  - name: BGE base Financial Matryoshka
    results:
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 768
          type: dim_768
        metrics:
          - type: cosine_accuracy@1
            value: 0.6985714285714286
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8342857142857143
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8628571428571429
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6985714285714286
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.27809523809523806
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17257142857142854
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08999999999999998
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6985714285714286
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8342857142857143
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8628571428571429
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8029099239677612
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.771475056689342
            name: Cosine Mrr@10
          - type: cosine_map@10
            value: 0.7714750566893424
            name: Cosine Map@10
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 512
          type: dim_512
        metrics:
          - type: cosine_accuracy@1
            value: 0.6842857142857143
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8271428571428572
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8628571428571429
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8928571428571429
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6842857142857143
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2757142857142857
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17257142857142854
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08928571428571427
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6842857142857143
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8271428571428572
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8628571428571429
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8928571428571429
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7942762197573711
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7620697278911563
            name: Cosine Mrr@10
          - type: cosine_map@10
            value: 0.7620697278911566
            name: Cosine Map@10
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 256
          type: dim_256
        metrics:
          - type: cosine_accuracy@1
            value: 0.6871428571428572
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8157142857142857
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8614285714285714
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8928571428571429
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6871428571428572
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.27190476190476187
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17228571428571426
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08928571428571427
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6871428571428572
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8157142857142857
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8614285714285714
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8928571428571429
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7935865448697424
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7613917233560088
            name: Cosine Mrr@10
          - type: cosine_map@10
            value: 0.7613917233560091
            name: Cosine Map@10
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 128
          type: dim_128
        metrics:
          - type: cosine_accuracy@1
            value: 0.6757142857142857
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8171428571428572
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8514285714285714
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8814285714285715
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6757142857142857
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2723809523809524
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17028571428571426
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08814285714285712
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6757142857142857
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8171428571428572
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8514285714285714
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8814285714285715
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7842926561068588
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7525731292517003
            name: Cosine Mrr@10
          - type: cosine_map@10
            value: 0.7525731292517006
            name: Cosine Map@10
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 64
          type: dim_64
        metrics:
          - type: cosine_accuracy@1
            value: 0.64
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.79
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8271428571428572
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.87
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.64
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2633333333333333
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.1654285714285714
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.087
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.64
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.79
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8271428571428572
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.87
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7594704472459967
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7236507936507934
            name: Cosine Mrr@10
          - type: cosine_map@10
            value: 0.7236507936507937
            name: Cosine Map@10

BGE base Financial Matryoshka

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("Sailesh9999/bge-base-financial-matryoshka")
# Run inference
sentences = [
    'Chipotle retains an independent third-party compensation consultant each year to conduct a pay equity analysis of its U.S. and Canadian workforce, including factors of pay such as grade level, tenure in role, and external market conditions like geographic location, to ensure consistency and equitable treatment among employees.',
    'How does Chipotle ensure pay equity among its employees?',
    'How can one locate information on legal proceedings within the Consolidated Financial Statements?',
]
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	0.6986
cosine_accuracy@3	0.8343
cosine_accuracy@5	0.8629
cosine_accuracy@10	0.9
cosine_precision@1	0.6986
cosine_precision@3	0.2781
cosine_precision@5	0.1726
cosine_precision@10	0.09
cosine_recall@1	0.6986
cosine_recall@3	0.8343
cosine_recall@5	0.8629
cosine_recall@10	0.9
cosine_ndcg@10	0.8029
cosine_mrr@10	0.7715
cosine_map@10	0.7715

Information Retrieval

Dataset: dim_512
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6843
cosine_accuracy@3	0.8271
cosine_accuracy@5	0.8629
cosine_accuracy@10	0.8929
cosine_precision@1	0.6843
cosine_precision@3	0.2757
cosine_precision@5	0.1726
cosine_precision@10	0.0893
cosine_recall@1	0.6843
cosine_recall@3	0.8271
cosine_recall@5	0.8629
cosine_recall@10	0.8929
cosine_ndcg@10	0.7943
cosine_mrr@10	0.7621
cosine_map@10	0.7621

Information Retrieval

Dataset: dim_256
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6871
cosine_accuracy@3	0.8157
cosine_accuracy@5	0.8614
cosine_accuracy@10	0.8929
cosine_precision@1	0.6871
cosine_precision@3	0.2719
cosine_precision@5	0.1723
cosine_precision@10	0.0893
cosine_recall@1	0.6871
cosine_recall@3	0.8157
cosine_recall@5	0.8614
cosine_recall@10	0.8929
cosine_ndcg@10	0.7936
cosine_mrr@10	0.7614
cosine_map@10	0.7614

Information Retrieval

Dataset: dim_128
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6757
cosine_accuracy@3	0.8171
cosine_accuracy@5	0.8514
cosine_accuracy@10	0.8814
cosine_precision@1	0.6757
cosine_precision@3	0.2724
cosine_precision@5	0.1703
cosine_precision@10	0.0881
cosine_recall@1	0.6757
cosine_recall@3	0.8171
cosine_recall@5	0.8514
cosine_recall@10	0.8814
cosine_ndcg@10	0.7843
cosine_mrr@10	0.7526
cosine_map@10	0.7526

Information Retrieval

Dataset: dim_64
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.64
cosine_accuracy@3	0.79
cosine_accuracy@5	0.8271
cosine_accuracy@10	0.87
cosine_precision@1	0.64
cosine_precision@3	0.2633
cosine_precision@5	0.1654
cosine_precision@10	0.087
cosine_recall@1	0.64
cosine_recall@3	0.79
cosine_recall@5	0.8271
cosine_recall@10	0.87
cosine_ndcg@10	0.7595
cosine_mrr@10	0.7237
cosine_map@10	0.7237

Training Details

Training Dataset

Unnamed Dataset

Size: 6,300 training samples
Columns: positive and anchor
Approximate statistics based on the first 1000 samples:
positive anchor
type string string
details
min: 7 tokens
mean: 46.55 tokens
max: 439 tokens

min: 9 tokens
mean: 20.43 tokens
max: 46 tokens

	positive	anchor
type	string	string
details	min: 7 tokens mean: 46.55 tokens max: 439 tokens	min: 9 tokens mean: 20.43 tokens max: 46 tokens

Samples:

positive	anchor
`Americas`	$
`Item 1 Business typically includes detailed information about the organization's operations, the nature of the business, and its strategic direction.`	`What is the title of the section that potentially discusses the operations or nature of a business in a document?`
`Operating expenses as a percentage of total revenues decreased to 15.3% in 2023 compared to 15.9% in 2022.`	`What was the operating expenses as a percentage of total revenues in 2023?`

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: 4
lr_scheduler_type: cosine
warmup_ratio: 0.1
bf16: True
tf32: True
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: 4
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: True
fp16: False
fp16_opt_level: O1
half_precision_backend: auto
bf16_full_eval: False
fp16_full_eval: False
tf32: True
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@10	dim_256_cosine_map@10	dim_512_cosine_map@10	dim_64_cosine_map@10	dim_768_cosine_map@10
0.8122	10	1.5638	-	-	-	-	-
0.9746	12	-	0.7308	0.7547	0.7547	0.7004	0.7624
1.6244	20	0.6662	-	-	-	-	-
1.9492	24	-	0.7468	0.7586	0.7624	0.7195	0.7655
2.4365	30	0.4634	-	-	-	-	-
2.9239	36	-	0.7525	0.7620	0.7614	0.7237	0.7717
3.2487	40	0.387	-	-	-	-	-
3.8985	48	-	0.7526	0.7614	0.7621	0.7237	0.7715

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.9.18
Sentence Transformers: 3.0.1
Transformers: 4.41.2
PyTorch: 2.1.2+cu121
Accelerate: 0.29.3
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}
}