Add new SentenceTransformer model.

8be2049 verified about 2 years ago

29.4 kB

base_model: BAAI/bge-base-en-v1.5
datasets: []
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: >-
      As of December 31, 2023, Hilton franchised 6,679 hotels and resorts, with
      914,974 rooms.
    sentences:
      - What does Google's new model 'Gemini' aim to achieve?
      - >-
        What is the total number of rooms in Hilton's franchised hotels as of
        December 31, 2023?
      - >-
        How much is the Company agreed to pay under the opioid settlement to
        resolve all lawsuits and future claims by government entities
        nationwide?
  - source_sentence: >-
      Under the Biologics Price Competition and Innovation Act, innovator
      biologics are granted a regulatory exclusivity period of 12 years.
    sentences:
      - >-
        What are the primary goals of the asset allocation strategy for USRIP's
        plan, and what standards must investment managers follow?
      - >-
        How long is the regulatory exclusivity period for innovator biologics
        under the Biologics Price Competition and Innovation Act?
      - >-
        By what percentage did the office loans increase in exposure during
        2023?
  - source_sentence: >-
      Amounts recorded in a business combination may change during the
      measurement period, which is a period not to exceed one year from the date
      of acquisition, as additional information about conditions that existed at
      the acquisition date becomes available.
    sentences:
      - >-
        What is considered during the measurement period in a business
        combination?
      - >-
        What was the primary reason for the increase in other costs of $15.3
        million reported?
      - >-
        How is the stock-based compensation expense determined for service-based
        and performance or market condition awards at Hewlett Packard
        Enterprise?
  - source_sentence: >-
      The Be Human pillar of our Impact Agenda sets out our focus areas with
      respect to human capital, including: •Inclusion, Diversity, Equity, and
      Action (“IDEA”); •Employee empowerment; and •Fair labor practices and the
      well-being of the people who make our products.
    sentences:
      - >-
        How did Hilton Worldwide Holdings Inc.'s accumulated deficit change from
        December 31, 2022, to December 31, 2023?
      - >-
        What primarily caused the decrease in the Company's effective income tax
        rate in 2023?
      - >-
        What is the objective of the Be Human pillar in the company's Impact
        Agenda?
  - source_sentence: >-
      Our revenue consists of service fees, net of incentives and refunds,
      charged to our customers. For stays, service fees, which are charged to
      customers as a percentage of the value of the booking, excluding taxes,
      vary based on factors specific to the booking, such as booking value, the
      duration of the booking, geography, and Host type.
    sentences:
      - >-
        What are some factors that affect the percentage of service fees charged
        to customers?
      - >-
        What is the PCAOB ID number for PricewaterhouseCoopers LLP concerning
        the firm's financial statements?
      - What were the net revenues for Global Banking & Markets in 2023?
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: 0.6957142857142857
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8485714285714285
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6957142857142857
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.26666666666666666
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.16971428571428568
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08999999999999998
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6957142857142857
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8485714285714285
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7935293220413043
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.759959183673469
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7639893123837201
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 512
          type: dim_512
        metrics:
          - type: cosine_accuracy@1
            value: 0.7057142857142857
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8014285714285714
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8528571428571429
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9028571428571428
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.7057142857142857
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2671428571428571
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17057142857142854
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09028571428571427
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.7057142857142857
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8014285714285714
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8528571428571429
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9028571428571428
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7983926017556883
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7656269841269838
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7693363291720529
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 256
          type: dim_256
        metrics:
          - type: cosine_accuracy@1
            value: 0.6914285714285714
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.79
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8471428571428572
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8914285714285715
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6914285714285714
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2633333333333333
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.16942857142857143
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08914285714285713
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6914285714285714
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.79
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8471428571428572
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8914285714285715
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7878064776962901
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7549427437641724
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7595543581664418
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 128
          type: dim_128
        metrics:
          - type: cosine_accuracy@1
            value: 0.6885714285714286
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.7928571428571428
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8385714285714285
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8914285714285715
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6885714285714286
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2642857142857143
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.1677142857142857
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08914285714285713
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6885714285714286
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.7928571428571428
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8385714285714285
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8914285714285715
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7855455284623294
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.752206916099773
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7560619398777708
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 64
          type: dim_64
        metrics:
          - type: cosine_accuracy@1
            value: 0.64
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.7642857142857142
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8114285714285714
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8671428571428571
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.64
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.25476190476190474
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.16228571428571426
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.0867142857142857
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.64
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.7642857142857142
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8114285714285714
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8671428571428571
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7491977147487785
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.711975623582766
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7167882776968978
            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("bhlim/bge-base-financial-matryoshka")
# Run inference
sentences = [
    'Our revenue consists of service fees, net of incentives and refunds, charged to our customers. For stays, service fees, which are charged to customers as a percentage of the value of the booking, excluding taxes, vary based on factors specific to the booking, such as booking value, the duration of the booking, geography, and Host type.',
    'What are some factors that affect the percentage of service fees charged to customers?',
    "What is the PCAOB ID number for PricewaterhouseCoopers LLP concerning the firm's 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	0.6957
cosine_accuracy@3	0.8
cosine_accuracy@5	0.8486
cosine_accuracy@10	0.9
cosine_precision@1	0.6957
cosine_precision@3	0.2667
cosine_precision@5	0.1697
cosine_precision@10	0.09
cosine_recall@1	0.6957
cosine_recall@3	0.8
cosine_recall@5	0.8486
cosine_recall@10	0.9
cosine_ndcg@10	0.7935
cosine_mrr@10	0.76
cosine_map@100	0.764

Information Retrieval

Dataset: dim_512
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.7057
cosine_accuracy@3	0.8014
cosine_accuracy@5	0.8529
cosine_accuracy@10	0.9029
cosine_precision@1	0.7057
cosine_precision@3	0.2671
cosine_precision@5	0.1706
cosine_precision@10	0.0903
cosine_recall@1	0.7057
cosine_recall@3	0.8014
cosine_recall@5	0.8529
cosine_recall@10	0.9029
cosine_ndcg@10	0.7984
cosine_mrr@10	0.7656
cosine_map@100	0.7693

Information Retrieval

Dataset: dim_256
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6914
cosine_accuracy@3	0.79
cosine_accuracy@5	0.8471
cosine_accuracy@10	0.8914
cosine_precision@1	0.6914
cosine_precision@3	0.2633
cosine_precision@5	0.1694
cosine_precision@10	0.0891
cosine_recall@1	0.6914
cosine_recall@3	0.79
cosine_recall@5	0.8471
cosine_recall@10	0.8914
cosine_ndcg@10	0.7878
cosine_mrr@10	0.7549
cosine_map@100	0.7596

Information Retrieval

Dataset: dim_128
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6886
cosine_accuracy@3	0.7929
cosine_accuracy@5	0.8386
cosine_accuracy@10	0.8914
cosine_precision@1	0.6886
cosine_precision@3	0.2643
cosine_precision@5	0.1677
cosine_precision@10	0.0891
cosine_recall@1	0.6886
cosine_recall@3	0.7929
cosine_recall@5	0.8386
cosine_recall@10	0.8914
cosine_ndcg@10	0.7855
cosine_mrr@10	0.7522
cosine_map@100	0.7561

Information Retrieval

Dataset: dim_64
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.64
cosine_accuracy@3	0.7643
cosine_accuracy@5	0.8114
cosine_accuracy@10	0.8671
cosine_precision@1	0.64
cosine_precision@3	0.2548
cosine_precision@5	0.1623
cosine_precision@10	0.0867
cosine_recall@1	0.64
cosine_recall@3	0.7643
cosine_recall@5	0.8114
cosine_recall@10	0.8671
cosine_ndcg@10	0.7492
cosine_mrr@10	0.712
cosine_map@100	0.7168

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: 46.18 tokens
max: 512 tokens

min: 7 tokens
mean: 20.64 tokens
max: 42 tokens

	positive	anchor
type	string	string
details	min: 8 tokens mean: 46.18 tokens max: 512 tokens	min: 7 tokens mean: 20.64 tokens max: 42 tokens

Samples:

positive	anchor
`Within the contiguous U.S., FedEx Freight offers FedEx Freight Priority, when speed is critical to meet a customer’s supply chain needs.`	`How does FedEx Freight accommodate rapid delivery needs?`
`For purposes of our goodwill impairment evaluation, the reporting units are Family Dollar, Dollar Tree and Dollar Tree Canada.`	`What reporting units are used for the goodwill impairment evaluation?`
`In 2024, AT&T Inc. expects a long-term rate of return of 7.75% on pension plan assets, reflecting an increase of 0.25%. This adjustment in expected returns is based on economic forecasts and changes in the asset mix.`	`What will AT&T Inc.'s expected long-term rate of return be on pension plan assets in 2024?`

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	1.5825	-	-	-	-	-
0.9746	12	-	0.7349	0.7502	0.7566	0.6910	0.7566
1.6244	20	0.6595	-	-	-	-	-
1.9492	24	-	0.7508	0.7583	0.7648	0.7142	0.7615
2.4365	30	0.4717	-	-	-	-	-
2.9239	36	-	0.7562	0.7616	0.7692	0.7178	0.7622
3.2487	40	0.4059	-	-	-	-	-
3.8985	48	-	0.7561	0.7596	0.7693	0.7168	0.7640

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.0.1
Transformers: 4.41.2
PyTorch: 2.3.1+cu121
Accelerate: 0.32.1
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}
}