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_3")
# 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 | 0.6871 |
cosine_accuracy@3 | 0.8214 |
cosine_accuracy@5 | 0.8586 |
cosine_accuracy@10 | 0.9 |
cosine_precision@1 | 0.6871 |
cosine_precision@3 | 0.2738 |
cosine_precision@5 | 0.1717 |
cosine_precision@10 | 0.09 |
cosine_recall@1 | 0.6871 |
cosine_recall@3 | 0.8214 |
cosine_recall@5 | 0.8586 |
cosine_recall@10 | 0.9 |
cosine_ndcg@10 | 0.7967 |
cosine_mrr@10 | 0.7634 |
cosine_map@10 | 0.7634 |
Information Retrieval
- Dataset:
dim_512
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 0.6857 |
cosine_accuracy@3 | 0.82 |
cosine_accuracy@5 | 0.8557 |
cosine_accuracy@10 | 0.9014 |
cosine_precision@1 | 0.6857 |
cosine_precision@3 | 0.2733 |
cosine_precision@5 | 0.1711 |
cosine_precision@10 | 0.0901 |
cosine_recall@1 | 0.6857 |
cosine_recall@3 | 0.82 |
cosine_recall@5 | 0.8557 |
cosine_recall@10 | 0.9014 |
cosine_ndcg@10 | 0.7952 |
cosine_mrr@10 | 0.761 |
cosine_map@10 | 0.761 |
Information Retrieval
- Dataset:
dim_256
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 0.6814 |
cosine_accuracy@3 | 0.8171 |
cosine_accuracy@5 | 0.8571 |
cosine_accuracy@10 | 0.8886 |
cosine_precision@1 | 0.6814 |
cosine_precision@3 | 0.2724 |
cosine_precision@5 | 0.1714 |
cosine_precision@10 | 0.0889 |
cosine_recall@1 | 0.6814 |
cosine_recall@3 | 0.8171 |
cosine_recall@5 | 0.8571 |
cosine_recall@10 | 0.8886 |
cosine_ndcg@10 | 0.7891 |
cosine_mrr@10 | 0.7567 |
cosine_map@10 | 0.7567 |
Information Retrieval
- Dataset:
dim_128
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 0.6571 |
cosine_accuracy@3 | 0.8071 |
cosine_accuracy@5 | 0.8457 |
cosine_accuracy@10 | 0.8743 |
cosine_precision@1 | 0.6571 |
cosine_precision@3 | 0.269 |
cosine_precision@5 | 0.1691 |
cosine_precision@10 | 0.0874 |
cosine_recall@1 | 0.6571 |
cosine_recall@3 | 0.8071 |
cosine_recall@5 | 0.8457 |
cosine_recall@10 | 0.8743 |
cosine_ndcg@10 | 0.7724 |
cosine_mrr@10 | 0.7391 |
cosine_map@10 | 0.7391 |
Information Retrieval
- Dataset:
dim_64
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 0.6157 |
cosine_accuracy@3 | 0.7686 |
cosine_accuracy@5 | 0.8171 |
cosine_accuracy@10 | 0.8557 |
cosine_precision@1 | 0.6157 |
cosine_precision@3 | 0.2562 |
cosine_precision@5 | 0.1634 |
cosine_precision@10 | 0.0856 |
cosine_recall@1 | 0.6157 |
cosine_recall@3 | 0.7686 |
cosine_recall@5 | 0.8171 |
cosine_recall@10 | 0.8557 |
cosine_ndcg@10 | 0.7405 |
cosine_mrr@10 | 0.7032 |
cosine_map@10 | 0.7032 |
Training Details
Training Dataset
Unnamed Dataset
- Size: 6,300 training samples
- Columns:
positive
andanchor
- 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
- 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
: epochper_device_train_batch_size
: 32per_device_eval_batch_size
: 16gradient_accumulation_steps
: 16learning_rate
: 1e-05num_train_epochs
: 4lr_scheduler_type
: cosinewarmup_ratio
: 0.1bf16
: Truetf32
: Trueload_best_model_at_end
: Trueoptim
: adamw_torch_fusedbatch_sampler
: no_duplicates
All Hyperparameters
Click to expand
overwrite_output_dir
: Falsedo_predict
: Falseeval_strategy
: epochprediction_loss_only
: Trueper_device_train_batch_size
: 32per_device_eval_batch_size
: 16per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 16eval_accumulation_steps
: Nonelearning_rate
: 1e-05weight_decay
: 0.0adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1.0num_train_epochs
: 4max_steps
: -1lr_scheduler_type
: cosinelr_scheduler_kwargs
: {}warmup_ratio
: 0.1warmup_steps
: 0log_level
: passivelog_level_replica
: warninglog_on_each_node
: Truelogging_nan_inf_filter
: Truesave_safetensors
: Truesave_on_each_node
: Falsesave_only_model
: Falserestore_callback_states_from_checkpoint
: Falseno_cuda
: Falseuse_cpu
: Falseuse_mps_device
: Falseseed
: 42data_seed
: Nonejit_mode_eval
: Falseuse_ipex
: Falsebf16
: Truefp16
: Falsefp16_opt_level
: O1half_precision_backend
: autobf16_full_eval
: Falsefp16_full_eval
: Falsetf32
: Truelocal_rank
: 0ddp_backend
: Nonetpu_num_cores
: Nonetpu_metrics_debug
: Falsedebug
: []dataloader_drop_last
: Falsedataloader_num_workers
: 0dataloader_prefetch_factor
: Nonepast_index
: -1disable_tqdm
: Falseremove_unused_columns
: Truelabel_names
: Noneload_best_model_at_end
: Trueignore_data_skip
: Falsefsdp
: []fsdp_min_num_params
: 0fsdp_config
: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}fsdp_transformer_layer_cls_to_wrap
: Noneaccelerator_config
: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}deepspeed
: Nonelabel_smoothing_factor
: 0.0optim
: adamw_torch_fusedoptim_args
: Noneadafactor
: Falsegroup_by_length
: Falselength_column_name
: lengthddp_find_unused_parameters
: Noneddp_bucket_cap_mb
: Noneddp_broadcast_buffers
: Falsedataloader_pin_memory
: Truedataloader_persistent_workers
: Falseskip_memory_metrics
: Trueuse_legacy_prediction_loop
: Falsepush_to_hub
: Falseresume_from_checkpoint
: Nonehub_model_id
: Nonehub_strategy
: every_savehub_private_repo
: Falsehub_always_push
: Falsegradient_checkpointing
: Falsegradient_checkpointing_kwargs
: Noneinclude_inputs_for_metrics
: Falseeval_do_concat_batches
: Truefp16_backend
: autopush_to_hub_model_id
: Nonepush_to_hub_organization
: Nonemp_parameters
:auto_find_batch_size
: Falsefull_determinism
: Falsetorchdynamo
: Noneray_scope
: lastddp_timeout
: 1800torch_compile
: Falsetorch_compile_backend
: Nonetorch_compile_mode
: Nonedispatch_batches
: Nonesplit_batches
: Noneinclude_tokens_per_second
: Falseinclude_num_input_tokens_seen
: Falseneftune_noise_alpha
: Noneoptim_target_modules
: Nonebatch_eval_metrics
: Falsebatch_sampler
: no_duplicatesmulti_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 | 1.7427 | - | - | - | - | - |
0.9746 | 12 | - | 0.7118 | 0.7377 | 0.7411 | 0.6774 | 0.7440 |
1.6244 | 20 | 0.9354 | - | - | - | - | - |
1.9492 | 24 | - | 0.7353 | 0.7544 | 0.7562 | 0.7008 | 0.7632 |
2.4365 | 30 | 0.674 | - | - | - | - | - |
2.9239 | 36 | - | 0.7382 | 0.7569 | 0.7612 | 0.7018 | 0.7625 |
3.2487 | 40 | 0.5862 | - | - | - | - | - |
3.8985 | 48 | - | 0.7391 | 0.7567 | 0.761 | 0.7032 | 0.7634 |
- 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}
}
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Model tree for Sailesh9999/bge-base-financial-matryoshka_3
Base model
BAAI/bge-base-en-v1.5
Finetuned
this model
Evaluation results
- Cosine Accuracy@1 on dim 768self-reported0.687
- Cosine Accuracy@3 on dim 768self-reported0.821
- Cosine Accuracy@5 on dim 768self-reported0.859
- Cosine Accuracy@10 on dim 768self-reported0.900
- Cosine Precision@1 on dim 768self-reported0.687
- Cosine Precision@3 on dim 768self-reported0.274
- Cosine Precision@5 on dim 768self-reported0.172
- Cosine Precision@10 on dim 768self-reported0.090
- Cosine Recall@1 on dim 768self-reported0.687
- Cosine Recall@3 on dim 768self-reported0.821