metadata

base_model: BAAI/bge-base-en-v1.5
language:
  - en
library_name: sentence-transformers
license: apache-2.0
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
pipeline_tag: sentence-similarity
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:6300
  - loss:MatryoshkaLoss
  - loss:MultipleNegativesRankingLoss
widget:
  - source_sentence: Total company-operated stores | 711 | | 655
    sentences:
      - >-
        What type of financial documents are included in Part IV, Item 15(a)(1)
        of the Annual Report on Form 10-K?
      - >-
        What is the total number of company-operated stores as of January 28,
        2024?
      - >-
        When does the 364-day facility entered into in August 2023 expire, and
        what is its total amount?
  - source_sentence: >-
      GM empowers employees to 'Speak Up for Safety' through the Employee Safety
      Concern Process which makes it easier for employees to report potential
      safety issues or suggest improvements without fear of retaliation and
      ensures their safety every day.
    sentences:
      - >-
        What item number is associated with financial statements and
        supplementary data in documents?
      - How does GM promote safety and well-being among its employees?
      - >-
        What are the main features included in the Skills for Jobs initiative
        launched by Microsoft?
  - source_sentence: >-
      Under the 2020 Plan, the exercise price of options granted is generally at
      least equal to the fair market value of the Company’s Class A common stock
      on the date of grant.
    sentences:
      - >-
        How is the exercise price for incentive stock options determined under
        Palantir Technologies Inc.’s 2020 Equity Incentive Plan?
      - >-
        What were the dividend amounts declared by AT&T for its preferred and
        common shares in December 2022 and December 2023?
      - What does Item 8 in a document usually represent?
  - source_sentence: >-
      On December 22, 2022, the parties entered into a settlement agreement to
      resolve the lawsuit, which provides for a payment of $725 million by us.
      The settlement was approved by the court on October 10, 2023, and the
      payment was made in November 2023.
    sentences:
      - >-
        What is the purpose of GM's collaboration efforts at their Global
        Technical Center in Warren, Michigan?
      - >-
        How does the acquisition method affect the financial statements after a
        business acquisition?
      - >-
        What was the outcome of the 2019 consumer class action regarding the
        company's user data practices?
  - source_sentence: >-
      Item 8, titled 'Financial Statements and Supplementary Data,' is followed
      by an index to these sections.
    sentences:
      - What section follows Item 8 in the document?
      - >-
        What is the total assets and shareholders' equity of Chubb Limited as of
        December 31, 2023?
      - How does AT&T emphasize diversity in its hiring practices?
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: 0.7385714285714285
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8642857142857143
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8942857142857142
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9342857142857143
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.7385714285714285
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.28809523809523807
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17885714285714285
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09342857142857142
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.7385714285714285
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8642857142857143
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8942857142857142
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9342857142857143
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8387370920568787
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.8078395691609976
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.8102903092098301
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 512
          type: dim_512
        metrics:
          - type: cosine_accuracy@1
            value: 0.7414285714285714
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8557142857142858
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8942857142857142
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9328571428571428
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.7414285714285714
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2852380952380953
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17885714285714285
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09328571428571426
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.7414285714285714
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8557142857142858
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8942857142857142
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9328571428571428
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8380676321786823
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.8075895691609978
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.8101143502932845
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 256
          type: dim_256
        metrics:
          - type: cosine_accuracy@1
            value: 0.7357142857142858
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.85
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8814285714285715
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.92
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.7357142857142858
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2833333333333333
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17628571428571424
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09199999999999998
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.7357142857142858
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.85
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8814285714285715
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.92
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8286016704428653
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7992942176870748
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.8028214002001232
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 128
          type: dim_128
        metrics:
          - type: cosine_accuracy@1
            value: 0.7142857142857143
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.84
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.87
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9128571428571428
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.7142857142857143
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.28
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.174
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09128571428571428
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.7142857142857143
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.84
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.87
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9128571428571428
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8153680997284491
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7840521541950115
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7875962124214356
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 64
          type: dim_64
        metrics:
          - type: cosine_accuracy@1
            value: 0.6771428571428572
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8085714285714286
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8371428571428572
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8857142857142857
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6771428571428572
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.26952380952380955
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.1674285714285714
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08857142857142855
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6771428571428572
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8085714285714286
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8371428571428572
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8857142857142857
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7840147713456539
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7513815192743762
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.755682487136274
            name: Cosine Map@100

BGE base Financial Matryoshka

This is a sentence-transformers model finetuned from BAAI/bge-base-en-v1.5 on the json 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:
- json
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("tessimago/bge-base-financial-matryoshka")
# Run inference
sentences = [
    "Item 8, titled 'Financial Statements and Supplementary Data,' is followed by an index to these sections.",
    'What section follows Item 8 in the document?',
    "What is the total assets and shareholders' equity of Chubb Limited as of December 31, 2023?",
]
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	0.7386
cosine_accuracy@3	0.8643
cosine_accuracy@5	0.8943
cosine_accuracy@10	0.9343
cosine_precision@1	0.7386
cosine_precision@3	0.2881
cosine_precision@5	0.1789
cosine_precision@10	0.0934
cosine_recall@1	0.7386
cosine_recall@3	0.8643
cosine_recall@5	0.8943
cosine_recall@10	0.9343
cosine_ndcg@10	0.8387
cosine_mrr@10	0.8078
cosine_map@100	0.8103

Information Retrieval

Dataset: dim_512
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.7414
cosine_accuracy@3	0.8557
cosine_accuracy@5	0.8943
cosine_accuracy@10	0.9329
cosine_precision@1	0.7414
cosine_precision@3	0.2852
cosine_precision@5	0.1789
cosine_precision@10	0.0933
cosine_recall@1	0.7414
cosine_recall@3	0.8557
cosine_recall@5	0.8943
cosine_recall@10	0.9329
cosine_ndcg@10	0.8381
cosine_mrr@10	0.8076
cosine_map@100	0.8101

Information Retrieval

Dataset: dim_256
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.7357
cosine_accuracy@3	0.85
cosine_accuracy@5	0.8814
cosine_accuracy@10	0.92
cosine_precision@1	0.7357
cosine_precision@3	0.2833
cosine_precision@5	0.1763
cosine_precision@10	0.092
cosine_recall@1	0.7357
cosine_recall@3	0.85
cosine_recall@5	0.8814
cosine_recall@10	0.92
cosine_ndcg@10	0.8286
cosine_mrr@10	0.7993
cosine_map@100	0.8028

Information Retrieval

Dataset: dim_128
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.7143
cosine_accuracy@3	0.84
cosine_accuracy@5	0.87
cosine_accuracy@10	0.9129
cosine_precision@1	0.7143
cosine_precision@3	0.28
cosine_precision@5	0.174
cosine_precision@10	0.0913
cosine_recall@1	0.7143
cosine_recall@3	0.84
cosine_recall@5	0.87
cosine_recall@10	0.9129
cosine_ndcg@10	0.8154
cosine_mrr@10	0.7841
cosine_map@100	0.7876

Information Retrieval

Dataset: dim_64
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6771
cosine_accuracy@3	0.8086
cosine_accuracy@5	0.8371
cosine_accuracy@10	0.8857
cosine_precision@1	0.6771
cosine_precision@3	0.2695
cosine_precision@5	0.1674
cosine_precision@10	0.0886
cosine_recall@1	0.6771
cosine_recall@3	0.8086
cosine_recall@5	0.8371
cosine_recall@10	0.8857
cosine_ndcg@10	0.784
cosine_mrr@10	0.7514
cosine_map@100	0.7557

Training Details

Training Dataset

json

Dataset: json
Size: 6,300 training samples
Columns: positive and anchor
Approximate statistics based on the first 1000 samples:
positive anchor
type string string
details
min: 6 tokens
mean: 46.25 tokens
max: 512 tokens

min: 7 tokens
mean: 20.69 tokens
max: 42 tokens

	positive	anchor
type	string	string
details	min: 6 tokens mean: 46.25 tokens max: 512 tokens	min: 7 tokens mean: 20.69 tokens max: 42 tokens

Samples:

positive	anchor
`As of January 28, 2024, we held cash and cash equivalents of $2.2 billion.`	`What was the total cash and cash equivalents held by the company as of January 28, 2024?`
`Net cash used in financing activities amounted to $1,600 million in fiscal year 2023.`	`What was the total net cash used in financing activities in fiscal year 2023?`
`Item 8, titled 'Financial Statements and Supplementary Data,' is followed by an index to these sections.`	`What section follows Item 8 in the document?`

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@100	dim_256_cosine_map@100	dim_512_cosine_map@100	dim_64_cosine_map@100	dim_768_cosine_map@100
0.8122	10	1.5849	-	-	-	-	-
0.9746	12	-	0.7610	0.7799	0.7878	0.7254	0.7922
1.6244	20	0.6368	-	-	-	-	-
1.9492	24	-	0.7823	0.7974	0.8047	0.7515	0.8046
2.4365	30	0.4976	-	-	-	-	-
2.9239	36	-	0.7876	0.803	0.8096	0.754	0.8081
3.2487	40	0.3845	-	-	-	-	-
3.8985	48	-	0.7876	0.8028	0.8101	0.7557	0.8103

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.10.14
Sentence Transformers: 3.1.0
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}
}