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("gK29382231121/bge-base-financial-matryoshka")
# Run inference
sentences = [
"How is Costco's fiscal year structured?",
'How many weeks did the fiscal years 2023 and 2022 include?',
'What is the process for using reinsurers not on the authorized list?',
]
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 | 0.6814 |
cosine_accuracy@3 | 0.8129 |
cosine_accuracy@5 | 0.85 |
cosine_accuracy@10 | 0.9029 |
cosine_precision@1 | 0.6814 |
cosine_precision@3 | 0.271 |
cosine_precision@5 | 0.17 |
cosine_precision@10 | 0.0903 |
cosine_recall@1 | 0.6814 |
cosine_recall@3 | 0.8129 |
cosine_recall@5 | 0.85 |
cosine_recall@10 | 0.9029 |
cosine_ndcg@10 | 0.7917 |
cosine_mrr@10 | 0.7563 |
cosine_map@100 | 0.761 |
Information Retrieval
- Dataset:
dim_512
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 0.6843 |
cosine_accuracy@3 | 0.8114 |
cosine_accuracy@5 | 0.8529 |
cosine_accuracy@10 | 0.8986 |
cosine_precision@1 | 0.6843 |
cosine_precision@3 | 0.2705 |
cosine_precision@5 | 0.1706 |
cosine_precision@10 | 0.0899 |
cosine_recall@1 | 0.6843 |
cosine_recall@3 | 0.8114 |
cosine_recall@5 | 0.8529 |
cosine_recall@10 | 0.8986 |
cosine_ndcg@10 | 0.7909 |
cosine_mrr@10 | 0.7565 |
cosine_map@100 | 0.7616 |
Information Retrieval
- Dataset:
dim_256
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 0.6786 |
cosine_accuracy@3 | 0.8086 |
cosine_accuracy@5 | 0.8429 |
cosine_accuracy@10 | 0.8943 |
cosine_precision@1 | 0.6786 |
cosine_precision@3 | 0.2695 |
cosine_precision@5 | 0.1686 |
cosine_precision@10 | 0.0894 |
cosine_recall@1 | 0.6786 |
cosine_recall@3 | 0.8086 |
cosine_recall@5 | 0.8429 |
cosine_recall@10 | 0.8943 |
cosine_ndcg@10 | 0.7866 |
cosine_mrr@10 | 0.7523 |
cosine_map@100 | 0.7572 |
Information Retrieval
- Dataset:
dim_128
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 0.6714 |
cosine_accuracy@3 | 0.7857 |
cosine_accuracy@5 | 0.8257 |
cosine_accuracy@10 | 0.8814 |
cosine_precision@1 | 0.6714 |
cosine_precision@3 | 0.2619 |
cosine_precision@5 | 0.1651 |
cosine_precision@10 | 0.0881 |
cosine_recall@1 | 0.6714 |
cosine_recall@3 | 0.7857 |
cosine_recall@5 | 0.8257 |
cosine_recall@10 | 0.8814 |
cosine_ndcg@10 | 0.7743 |
cosine_mrr@10 | 0.7405 |
cosine_map@100 | 0.7457 |
Information Retrieval
- Dataset:
dim_64
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 0.6371 |
cosine_accuracy@3 | 0.7686 |
cosine_accuracy@5 | 0.8071 |
cosine_accuracy@10 | 0.8614 |
cosine_precision@1 | 0.6371 |
cosine_precision@3 | 0.2562 |
cosine_precision@5 | 0.1614 |
cosine_precision@10 | 0.0861 |
cosine_recall@1 | 0.6371 |
cosine_recall@3 | 0.7686 |
cosine_recall@5 | 0.8071 |
cosine_recall@10 | 0.8614 |
cosine_ndcg@10 | 0.7501 |
cosine_mrr@10 | 0.7146 |
cosine_map@100 | 0.7199 |
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: 45.34 tokens
- max: 439 tokens
- min: 2 tokens
- mean: 20.47 tokens
- max: 51 tokens
- Samples:
positive anchor The HP GreenValley edge-to-cloud platform is used for software-defined disaggregated storage services that include HPE GreenLake for Block Storage and HPE GreenLake for File Storage, and it provides unified cloud-based management to simplify how customers manage storage.
What are the focus areas for the HP GreenLake platform?
Net income
$ Deferred tax assets and deferred tax liabilities included in the Consolidated Balance Sheets as follows: As of October 31, 2023: Deferred tax assets were $3,155 million and Deferred tax liabilities were $44 million. As of October 31, 2022: Deferred tax assets were $2,167 million and Deferred tax liabilities were $121 million. The total net deferred tax assets were $3,111 million in 2023 and $2,046 million in 2022.
What was the change in HP's net deferred tax assets from 2022 to 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
: 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
: 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
: 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 | 1.5361 | - | - | - | - | - |
0.9746 | 12 | - | 0.7280 | 0.7414 | 0.7494 | 0.6896 | 0.7470 |
1.6244 | 20 | 0.6833 | - | - | - | - | - |
1.9492 | 24 | - | 0.7426 | 0.7487 | 0.7573 | 0.7138 | 0.7592 |
2.4365 | 30 | 0.4674 | - | - | - | - | - |
2.9239 | 36 | - | 0.7452 | 0.7558 | 0.7624 | 0.7190 | 0.7623 |
3.2487 | 40 | 0.4038 | - | - | - | - | - |
3.8985 | 48 | - | 0.7457 | 0.7572 | 0.7616 | 0.7199 | 0.7610 |
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 gK29382231121/bge-base-financial-matryoshka
Base model
BAAI/bge-base-en-v1.5
Finetuned
this model
Evaluation results
- Cosine Accuracy@1 on dim 768self-reported0.681
- Cosine Accuracy@3 on dim 768self-reported0.813
- Cosine Accuracy@5 on dim 768self-reported0.850
- Cosine Accuracy@10 on dim 768self-reported0.903
- Cosine Precision@1 on dim 768self-reported0.681
- Cosine Precision@3 on dim 768self-reported0.271
- Cosine Precision@5 on dim 768self-reported0.170
- Cosine Precision@10 on dim 768self-reported0.090
- Cosine Recall@1 on dim 768self-reported0.681
- Cosine Recall@3 on dim 768self-reported0.813