metadata

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@100
widget:
  - source_sentence: >-
      Item 3—Legal Proceedings See discussion of Legal Proceedings in Note 10 to
      the consolidated financial statements included in Item 8 of this Report.
    sentences:
      - >-
        How much did the company's finance lease obligations total as of
        December 31, 2023?
      - What do Note 10 and Item 8 of the report encompass?
      - >-
        What was the basic earnings per common share attributable to Comcast
        Corporation shareholders in 2023?
  - source_sentence: >-
      Our quarterly Insurance segment earnings and operating cash flows are
      impacted by the Medicare Part D benefit Grant program, the changing
      membership composition, and the multistage plan period starting annually
      on January 1. These plan designs generally result in us sharing a greater
      portion of the responsibility for total prescription drug costs in the
      early stages and less in the latter stages.
    sentences:
      - >-
        What are the two main categories into which Ford Motor Company
        classifies its costs and expenses, excluding those related to Ford
        Credit?
      - >-
        How does the benefit design of Medicare Part D impact the quarterly
        insurance segment earnings and operating cash flows?
      - >-
        What basis is used to record HTM investment securities in Schwab's
        financial statements?
  - source_sentence: >-
      Operating Profit in the Wizards of the Coast and Digital Gaming segment
      decreased 2% to $538.3 million.
    sentences:
      - >-
        How much did the Wizards of the Coast and Digital Gaming segment's
        operating profit change in 2022?
      - >-
        What factors are considered in evaluating the lifetime losses for most
        loans and receivables?
      - >-
        How did the loss on certain U.S. affiliates impact the Company's
        effective tax rate in the fiscal fourth quarter of 2021?
  - source_sentence: >-
      In 2023, the net earnings of Johnson & Johnson were $35,153 million. The
      company also registered cash dividends paid amounting to $11,770 million
      for the year, priced at $4.70 per share.
    sentences:
      - What was the postpaid churn rate for AT&T Inc. in 2023?
      - >-
        What was the GAAP net revenue for the fiscal year ended October 31,
        2023?
      - What were the total net earnings of Johnson & Johnson in the year 2023?
  - source_sentence: >-
      During fiscal 2022, GameStop Corp increased its valuation allowances by
      approximately $70.2 million in various jurisdictions.
    sentences:
      - >-
        How much did GameStop Corp's valuation allowances increase during fiscal
        2022?
      - How does Gilead ensure an inclusive and diverse workforce?
      - >-
        What factors are considered in determining the estimated future warranty
        costs for connected fitness and Precor branded fitness products?
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: 0.7185714285714285
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.83
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8714285714285714
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.91
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.7185714285714285
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.27666666666666667
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17428571428571427
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.091
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.7185714285714285
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.83
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8714285714285714
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.91
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8137967516958747
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7830442176870747
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7866777593387027
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 512
          type: dim_512
        metrics:
          - type: cosine_accuracy@1
            value: 0.7114285714285714
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8314285714285714
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8728571428571429
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9142857142857143
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.7114285714285714
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.27714285714285714
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17457142857142854
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09142857142857141
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.7114285714285714
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8314285714285714
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8728571428571429
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9142857142857143
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8123538841130576
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7798667800453513
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7831580648041446
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 256
          type: dim_256
        metrics:
          - type: cosine_accuracy@1
            value: 0.7
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8285714285714286
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8614285714285714
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9042857142857142
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.7
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2761904761904762
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17228571428571426
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09042857142857143
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.7
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8285714285714286
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8614285714285714
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9042857142857142
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8043112987059042
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7721706349206346
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7759026470022171
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 128
          type: dim_128
        metrics:
          - type: cosine_accuracy@1
            value: 0.6857142857142857
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8071428571428572
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8571428571428571
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8971428571428571
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6857142857142857
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.26904761904761904
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.1714285714285714
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.0897142857142857
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6857142857142857
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8071428571428572
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8571428571428571
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8971428571428571
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.79087795854059
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7568854875283447
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7608935817550728
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 64
          type: dim_64
        metrics:
          - type: cosine_accuracy@1
            value: 0.66
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.7757142857142857
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8128571428571428
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8671428571428571
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.66
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.25857142857142856
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.16257142857142853
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.0867142857142857
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.66
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.7757142857142857
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8128571428571428
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8671428571428571
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7616045249840884
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7281247165532877
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7330922421864847
            name: Cosine Map@100

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("cristuf/bge-base-financial-matryoshka")
# Run inference
sentences = [
    'During fiscal 2022, GameStop Corp increased its valuation allowances by approximately $70.2 million in various jurisdictions.',
    "How much did GameStop Corp's valuation allowances increase during fiscal 2022?",
    'How does Gilead ensure an inclusive and diverse workforce?',
]
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	0.7186
cosine_accuracy@3	0.83
cosine_accuracy@5	0.8714
cosine_accuracy@10	0.91
cosine_precision@1	0.7186
cosine_precision@3	0.2767
cosine_precision@5	0.1743
cosine_precision@10	0.091
cosine_recall@1	0.7186
cosine_recall@3	0.83
cosine_recall@5	0.8714
cosine_recall@10	0.91
cosine_ndcg@10	0.8138
cosine_mrr@10	0.783
cosine_map@100	0.7867

Information Retrieval

Dataset: dim_512
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.7114
cosine_accuracy@3	0.8314
cosine_accuracy@5	0.8729
cosine_accuracy@10	0.9143
cosine_precision@1	0.7114
cosine_precision@3	0.2771
cosine_precision@5	0.1746
cosine_precision@10	0.0914
cosine_recall@1	0.7114
cosine_recall@3	0.8314
cosine_recall@5	0.8729
cosine_recall@10	0.9143
cosine_ndcg@10	0.8124
cosine_mrr@10	0.7799
cosine_map@100	0.7832

Information Retrieval

Dataset: dim_256
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.7
cosine_accuracy@3	0.8286
cosine_accuracy@5	0.8614
cosine_accuracy@10	0.9043
cosine_precision@1	0.7
cosine_precision@3	0.2762
cosine_precision@5	0.1723
cosine_precision@10	0.0904
cosine_recall@1	0.7
cosine_recall@3	0.8286
cosine_recall@5	0.8614
cosine_recall@10	0.9043
cosine_ndcg@10	0.8043
cosine_mrr@10	0.7722
cosine_map@100	0.7759

Information Retrieval

Dataset: dim_128
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6857
cosine_accuracy@3	0.8071
cosine_accuracy@5	0.8571
cosine_accuracy@10	0.8971
cosine_precision@1	0.6857
cosine_precision@3	0.269
cosine_precision@5	0.1714
cosine_precision@10	0.0897
cosine_recall@1	0.6857
cosine_recall@3	0.8071
cosine_recall@5	0.8571
cosine_recall@10	0.8971
cosine_ndcg@10	0.7909
cosine_mrr@10	0.7569
cosine_map@100	0.7609

Information Retrieval

Dataset: dim_64
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.66
cosine_accuracy@3	0.7757
cosine_accuracy@5	0.8129
cosine_accuracy@10	0.8671
cosine_precision@1	0.66
cosine_precision@3	0.2586
cosine_precision@5	0.1626
cosine_precision@10	0.0867
cosine_recall@1	0.66
cosine_recall@3	0.7757
cosine_recall@5	0.8129
cosine_recall@10	0.8671
cosine_ndcg@10	0.7616
cosine_mrr@10	0.7281
cosine_map@100	0.7331

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: 8 tokens
mean: 46.36 tokens
max: 439 tokens

min: 9 tokens
mean: 20.41 tokens
max: 51 tokens

	positive	anchor
type	string	string
details	min: 8 tokens mean: 46.36 tokens max: 439 tokens	min: 9 tokens mean: 20.41 tokens max: 51 tokens

Samples:

positive	anchor
`Japan's revenue for the year 2023 reached 2,367.0 million.`	`What was the revenue attributed to Japan in the year 2023?`
Our four reportable segments are: •the Data Center segment, which primarily includes server CPUs, GPUs, APUs, DPUs, FPGAs, SmartNICs, AI accelerators and Adaptive SoC products for data centers; •the Client segment, which primarily includes CPUs, APUs, and chipsets for desktop, notebook and handheld personal computers; •the Gaming segment, which primarily includes discrete GPUs, semi-custom SoC products and development services; and •the Embedded segment, which primarily includes embedded CPUs, GPUs, APUs, FPGAs, SOMs, and Adaptive SoC products.	`What are the different segments that AMD reports financially?`
`For detailed information about the company's legal proceedings, see Note 4 to the consolidated financial statements, included under the caption 'Contingencies' in the Annual Report on Form 10-K.`	`Where can detailed information about the company's legal proceedings be found in its financial statements?`

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	1.5267	-	-	-	-	-
0.9746	12	-	0.7446	0.7639	0.7765	0.7039	0.7725
1.6244	20	0.6742	-	-	-	-	-
1.9492	24	-	0.7606	0.7795	0.7828	0.7297	0.7839
2.4365	30	0.4469	-	-	-	-	-
2.9239	36	-	0.7643	0.7758	0.7834	0.7332	0.7845
3.2487	40	0.3712	-	-	-	-	-
3.8985	48	-	0.7609	0.7759	0.7832	0.7331	0.7867

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.11.8
Sentence Transformers: 3.0.1
Transformers: 4.41.2
PyTorch: 2.3.1+cu121
Accelerate: 0.30.1
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}
}