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("MugheesAwan11/bge-base-financial-matryoshka")
# Run inference
sentences = [
'The net earnings margin in 2023 was 6.0%.',
'What was the net earnings margin in 2023?',
'What caused the slight decline in Workforce Solutions revenue in 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 | 0.7257 |
cosine_accuracy@3 | 0.8514 |
cosine_accuracy@5 | 0.8829 |
cosine_accuracy@10 | 0.9143 |
cosine_precision@1 | 0.7257 |
cosine_precision@3 | 0.2838 |
cosine_precision@5 | 0.1766 |
cosine_precision@10 | 0.0914 |
cosine_recall@1 | 0.7257 |
cosine_recall@3 | 0.8514 |
cosine_recall@5 | 0.8829 |
cosine_recall@10 | 0.9143 |
cosine_ndcg@10 | 0.8233 |
cosine_mrr@10 | 0.7938 |
cosine_map@100 | 0.7966 |
Information Retrieval
- Dataset:
dim_512
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 0.7257 |
cosine_accuracy@3 | 0.8543 |
cosine_accuracy@5 | 0.8757 |
cosine_accuracy@10 | 0.91 |
cosine_precision@1 | 0.7257 |
cosine_precision@3 | 0.2848 |
cosine_precision@5 | 0.1751 |
cosine_precision@10 | 0.091 |
cosine_recall@1 | 0.7257 |
cosine_recall@3 | 0.8543 |
cosine_recall@5 | 0.8757 |
cosine_recall@10 | 0.91 |
cosine_ndcg@10 | 0.8215 |
cosine_mrr@10 | 0.7928 |
cosine_map@100 | 0.7959 |
Information Retrieval
- Dataset:
dim_256
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 0.71 |
cosine_accuracy@3 | 0.85 |
cosine_accuracy@5 | 0.8671 |
cosine_accuracy@10 | 0.9086 |
cosine_precision@1 | 0.71 |
cosine_precision@3 | 0.2833 |
cosine_precision@5 | 0.1734 |
cosine_precision@10 | 0.0909 |
cosine_recall@1 | 0.71 |
cosine_recall@3 | 0.85 |
cosine_recall@5 | 0.8671 |
cosine_recall@10 | 0.9086 |
cosine_ndcg@10 | 0.8139 |
cosine_mrr@10 | 0.7833 |
cosine_map@100 | 0.7863 |
Information Retrieval
- Dataset:
dim_128
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 0.6814 |
cosine_accuracy@3 | 0.8157 |
cosine_accuracy@5 | 0.8586 |
cosine_accuracy@10 | 0.8943 |
cosine_precision@1 | 0.6814 |
cosine_precision@3 | 0.2719 |
cosine_precision@5 | 0.1717 |
cosine_precision@10 | 0.0894 |
cosine_recall@1 | 0.6814 |
cosine_recall@3 | 0.8157 |
cosine_recall@5 | 0.8586 |
cosine_recall@10 | 0.8943 |
cosine_ndcg@10 | 0.7915 |
cosine_mrr@10 | 0.7582 |
cosine_map@100 | 0.7616 |
Information Retrieval
- Dataset:
dim_64
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 0.66 |
cosine_accuracy@3 | 0.78 |
cosine_accuracy@5 | 0.8071 |
cosine_accuracy@10 | 0.87 |
cosine_precision@1 | 0.66 |
cosine_precision@3 | 0.26 |
cosine_precision@5 | 0.1614 |
cosine_precision@10 | 0.087 |
cosine_recall@1 | 0.66 |
cosine_recall@3 | 0.78 |
cosine_recall@5 | 0.8071 |
cosine_recall@10 | 0.87 |
cosine_ndcg@10 | 0.7637 |
cosine_mrr@10 | 0.7301 |
cosine_map@100 | 0.7343 |
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: 6 tokens
- mean: 46.61 tokens
- max: 289 tokens
- min: 8 tokens
- mean: 20.58 tokens
- max: 45 tokens
- Samples:
positive anchor Insurance Medical Membership at December 31, 2020 for Florida includes Individual Medicare Advantage (851.3 thousand), Group Medicare Advantage (9.1 thousand), Medicare stand-alone PDP (131.9 thousand), Medicare Supplement (17.5 thousand), State-based contracts and Other (656.6 thousand), Fully-insured commercial Group (73.8 thousand), ASO (24.5 thousand), totaling 1,764.7 thousand members.
How is Florida's total insurance medical membership detailed in the data for December 31, 2023?
For the year ended December 31, 2023, the total provision for income taxes was $836 million, which includes both current and deferred tax amounts.
What was the total provision for income taxes at the end of 2023?
Pursuant to the IRA, under Sections 48, 48E and 25D of the Internal Revenue Code (“IRC”), standalone energy storage technology is eligible for a tax credit between 6% and 50% of qualified expenditures, regardless of the source of energy, which may be claimed by our customers for storage systems they purchase or by us for arrangements where we own the systems.
Under what sections of the Internal Revenue Code can standalone energy storage technology receive a tax credit?
- 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
: 2lr_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
: 2e-05weight_decay
: 0.0adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1.0num_train_epochs
: 2max_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@100 | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_64_cosine_map@100 | dim_768_cosine_map@100 |
---|---|---|---|---|---|---|---|
0.8122 | 10 | 1.4587 | - | - | - | - | - |
0.9746 | 12 | - | 0.7544 | 0.7722 | 0.7809 | 0.7118 | 0.7804 |
1.6244 | 20 | 0.6938 | - | - | - | - | - |
1.9492 | 24 | - | 0.7586 | 0.779 | 0.7876 | 0.7197 | 0.785 |
0.8122 | 10 | 0.5238 | - | - | - | - | - |
0.9746 | 12 | - | 0.7602 | 0.7815 | 0.7928 | 0.7285 | 0.7942 |
1.6244 | 20 | 0.4172 | - | - | - | - | - |
1.9492 | 24 | - | 0.7616 | 0.7863 | 0.7959 | 0.7343 | 0.7966 |
- The bold row denotes the saved checkpoint.
Framework Versions
- Python: 3.10.14
- Sentence Transformers: 3.0.0
- Transformers: 4.41.2
- PyTorch: 2.1.2+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}
}
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Model tree for MugheesAwan11/bge-base-financial-matryoshka
Base model
BAAI/bge-base-en-v1.5
Finetuned
this model
Evaluation results
- Cosine Accuracy@1 on dim 768self-reported0.726
- Cosine Accuracy@3 on dim 768self-reported0.851
- Cosine Accuracy@5 on dim 768self-reported0.883
- Cosine Accuracy@10 on dim 768self-reported0.914
- Cosine Precision@1 on dim 768self-reported0.726
- Cosine Precision@3 on dim 768self-reported0.284
- Cosine Precision@5 on dim 768self-reported0.177
- Cosine Precision@10 on dim 768self-reported0.091
- Cosine Recall@1 on dim 768self-reported0.726
- Cosine Recall@3 on dim 768self-reported0.851