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("dustyatx/bge-base-financial-matryoshka")
# Run inference
sentences = [
'Details about legal proceedings are included in Part II, Item 8, "Financial Statements and Supplementary Data" of the Annual Report on Form 10-K, under the caption "Legal Proceedings".',
'Where can details about legal proceedings be located in an Annual Report on Form 10-K?',
'How many stores did AutoZone operate in the United States as of August 26, 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 | 0.7071 |
cosine_accuracy@3 | 0.8414 |
cosine_accuracy@5 | 0.88 |
cosine_accuracy@10 | 0.9314 |
cosine_precision@1 | 0.7071 |
cosine_precision@3 | 0.2805 |
cosine_precision@5 | 0.176 |
cosine_precision@10 | 0.0931 |
cosine_recall@1 | 0.7071 |
cosine_recall@3 | 0.8414 |
cosine_recall@5 | 0.88 |
cosine_recall@10 | 0.9314 |
cosine_ndcg@10 | 0.8207 |
cosine_mrr@10 | 0.7853 |
cosine_map@100 | 0.7882 |
Information Retrieval
- Dataset:
dim_512
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 0.6957 |
cosine_accuracy@3 | 0.8386 |
cosine_accuracy@5 | 0.8757 |
cosine_accuracy@10 | 0.93 |
cosine_precision@1 | 0.6957 |
cosine_precision@3 | 0.2795 |
cosine_precision@5 | 0.1751 |
cosine_precision@10 | 0.093 |
cosine_recall@1 | 0.6957 |
cosine_recall@3 | 0.8386 |
cosine_recall@5 | 0.8757 |
cosine_recall@10 | 0.93 |
cosine_ndcg@10 | 0.8149 |
cosine_mrr@10 | 0.7781 |
cosine_map@100 | 0.781 |
Information Retrieval
- Dataset:
dim_256
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 0.6886 |
cosine_accuracy@3 | 0.83 |
cosine_accuracy@5 | 0.8743 |
cosine_accuracy@10 | 0.9143 |
cosine_precision@1 | 0.6886 |
cosine_precision@3 | 0.2767 |
cosine_precision@5 | 0.1749 |
cosine_precision@10 | 0.0914 |
cosine_recall@1 | 0.6886 |
cosine_recall@3 | 0.83 |
cosine_recall@5 | 0.8743 |
cosine_recall@10 | 0.9143 |
cosine_ndcg@10 | 0.8061 |
cosine_mrr@10 | 0.7711 |
cosine_map@100 | 0.7752 |
Information Retrieval
- Dataset:
dim_128
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 0.6771 |
cosine_accuracy@3 | 0.8214 |
cosine_accuracy@5 | 0.8614 |
cosine_accuracy@10 | 0.9143 |
cosine_precision@1 | 0.6771 |
cosine_precision@3 | 0.2738 |
cosine_precision@5 | 0.1723 |
cosine_precision@10 | 0.0914 |
cosine_recall@1 | 0.6771 |
cosine_recall@3 | 0.8214 |
cosine_recall@5 | 0.8614 |
cosine_recall@10 | 0.9143 |
cosine_ndcg@10 | 0.7979 |
cosine_mrr@10 | 0.7606 |
cosine_map@100 | 0.764 |
Information Retrieval
- Dataset:
dim_64
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 0.6557 |
cosine_accuracy@3 | 0.7871 |
cosine_accuracy@5 | 0.8271 |
cosine_accuracy@10 | 0.8714 |
cosine_precision@1 | 0.6557 |
cosine_precision@3 | 0.2624 |
cosine_precision@5 | 0.1654 |
cosine_precision@10 | 0.0871 |
cosine_recall@1 | 0.6557 |
cosine_recall@3 | 0.7871 |
cosine_recall@5 | 0.8271 |
cosine_recall@10 | 0.8714 |
cosine_ndcg@10 | 0.7664 |
cosine_mrr@10 | 0.7327 |
cosine_map@100 | 0.7376 |
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: 8 tokens
- mean: 45.94 tokens
- max: 512 tokens
- min: 7 tokens
- mean: 20.7 tokens
- max: 42 tokens
- Samples:
positive anchor The company must continuously strengthen its capabilities in marketing and innovation to compete in a digital environment and maintain brand loyalty and marketallability. In addition, it is increasing its investments in e-commerce to support retail and meal delivery services, offering more package sizes that are fit-for-purpose for online sales and shifting more consumer and trade promotions to digital.
What strategies is the company employing to enhance its competitiveness in a digital environment?
Fedflowing expanded or relocated its hub and linehaul network, FedEx Ground also introduced new safety technologies, set new driver standards, and made operational enhancements for safer handling of heavy items.
What specific changes has FedEx Ground made for vehicle and driver safety?
The debt financing, which is being provided by a syndicate of Chinese financial institutions, contains certain covenants and a maximum borrowing limit of ¥29.7 billion RMB (approximately $4.2 billion).
What is the maximum borrowing limit of the debt financing provided by the syndicate of Chinese financial institutions for Universal Beijing Resort?
- 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
: 2e-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
: Nonetorch_empty_cache_steps
: Nonelearning_rate
: 2e-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
: Falseeval_on_start
: Falseeval_use_gather_object
: Falsebatch_sampler
: no_duplicatesmulti_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 | 1.5212 | - | - | - | - | - |
0.9746 | 12 | - | 0.7439 | 0.7556 | 0.7670 | 0.7142 | 0.7717 |
1.6244 | 20 | 0.6418 | - | - | - | - | - |
1.9492 | 24 | - | 0.7592 | 0.7743 | 0.7787 | 0.7331 | 0.7839 |
2.4365 | 30 | 0.4411 | - | - | - | - | - |
2.9239 | 36 | - | 0.7623 | 0.7757 | 0.7816 | 0.7365 | 0.7902 |
3.2487 | 40 | 0.3917 | - | - | - | - | - |
3.8985 | 48 | - | 0.764 | 0.7752 | 0.781 | 0.7376 | 0.7882 |
- The bold row denotes the saved checkpoint.
Framework Versions
- Python: 3.11.9
- Sentence Transformers: 3.0.1
- Transformers: 4.44.2
- PyTorch: 2.4.0+cu121
- Accelerate: 0.33.0
- Datasets: 2.21.0
- 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 dustyatx/bge-base-financial-matryoshka
Base model
BAAI/bge-base-en-v1.5Evaluation results
- Cosine Accuracy@1 on dim 768self-reported0.707
- Cosine Accuracy@3 on dim 768self-reported0.841
- Cosine Accuracy@5 on dim 768self-reported0.880
- Cosine Accuracy@10 on dim 768self-reported0.931
- Cosine Precision@1 on dim 768self-reported0.707
- Cosine Precision@3 on dim 768self-reported0.280
- Cosine Precision@5 on dim 768self-reported0.176
- Cosine Precision@10 on dim 768self-reported0.093
- Cosine Recall@1 on dim 768self-reported0.707
- Cosine Recall@3 on dim 768self-reported0.841