BGE base Financial Matryoshka
This is a sentence-transformers model finetuned from BAAI/bge-base-en-v1.5 on the json dataset. 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 dimensions
- Similarity Function: Cosine Similarity
- Training Dataset:
- json
- 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("SMARTICT/bge-base-financial-matryoshka")
# Run inference
sentences = [
'As of December 31, 2023, approximately $1.80 billion is available to be repatriated from Mainland China to the U.S.',
'What is the total amount of unrestricted cash available for repatriation from Mainland China to the U.S. as of the end of 2023?',
"What is the focus of the company's research and development efforts?",
]
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
- Datasets:
dim_768
,dim_512
,dim_256
,dim_128
anddim_64
- Evaluated with
InformationRetrievalEvaluator
Metric | dim_768 | dim_512 | dim_256 | dim_128 | dim_64 |
---|---|---|---|---|---|
cosine_accuracy@1 | 0.6771 | 0.6714 | 0.6714 | 0.6614 | 0.6357 |
cosine_accuracy@3 | 0.8143 | 0.8157 | 0.8157 | 0.8 | 0.7743 |
cosine_accuracy@5 | 0.8643 | 0.8657 | 0.8586 | 0.8414 | 0.8186 |
cosine_accuracy@10 | 0.9143 | 0.92 | 0.9157 | 0.8886 | 0.8643 |
cosine_precision@1 | 0.6771 | 0.6714 | 0.6714 | 0.6614 | 0.6357 |
cosine_precision@3 | 0.2714 | 0.2719 | 0.2719 | 0.2667 | 0.2581 |
cosine_precision@5 | 0.1729 | 0.1731 | 0.1717 | 0.1683 | 0.1637 |
cosine_precision@10 | 0.0914 | 0.092 | 0.0916 | 0.0889 | 0.0864 |
cosine_recall@1 | 0.6771 | 0.6714 | 0.6714 | 0.6614 | 0.6357 |
cosine_recall@3 | 0.8143 | 0.8157 | 0.8157 | 0.8 | 0.7743 |
cosine_recall@5 | 0.8643 | 0.8657 | 0.8586 | 0.8414 | 0.8186 |
cosine_recall@10 | 0.9143 | 0.92 | 0.9157 | 0.8886 | 0.8643 |
cosine_ndcg@10 | 0.7949 | 0.7936 | 0.7926 | 0.7767 | 0.7512 |
cosine_mrr@10 | 0.7568 | 0.7534 | 0.7535 | 0.7408 | 0.7149 |
cosine_map@100 | 0.7602 | 0.7564 | 0.7565 | 0.7454 | 0.7199 |
Training Details
Training Dataset
json
- Dataset: json
- Size: 6,300 training samples
- Columns:
positive
andanchor
- Approximate statistics based on the first 1000 samples:
positive anchor type string string details - min: 4 tokens
- mean: 46.71 tokens
- max: 281 tokens
- min: 7 tokens
- mean: 20.48 tokens
- max: 43 tokens
- Samples:
positive anchor Information on legal proceedings is included in Note 15 to the Consolidated Financial Statements.
What note in the Consolidated Financial Statements provides details on legal proceedings?
As of December 31, 2023, approximately $1.80 billion is available to be repatriated from Mainland China to the U.S.
What is the total amount of unrestricted cash available for repatriation from Mainland China to the U.S. as of the end of 2023?
Bank deposits amounted to $289,953 million as of December 31, 2023.
What was the balance of bank deposits at Charles Schwab Corporation as of December 31, 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
: Falseprompts
: Nonebatch_sampler
: no_duplicatesmulti_dataset_batch_sampler
: proportional
Training Logs
Epoch | Step | Training Loss | dim_768_cosine_ndcg@10 | dim_512_cosine_ndcg@10 | dim_256_cosine_ndcg@10 | dim_128_cosine_ndcg@10 | dim_64_cosine_ndcg@10 |
---|---|---|---|---|---|---|---|
0.8122 | 10 | 1.5517 | - | - | - | - | - |
0.9746 | 12 | - | 0.7830 | 0.7842 | 0.7814 | 0.7623 | 0.7215 |
1.6244 | 20 | 0.6616 | - | - | - | - | - |
1.9492 | 24 | - | 0.7918 | 0.7924 | 0.7884 | 0.7737 | 0.7429 |
2.4365 | 30 | 0.46 | - | - | - | - | - |
2.9239 | 36 | - | 0.7941 | 0.7920 | 0.7930 | 0.7764 | 0.7482 |
3.2487 | 40 | 0.3917 | - | - | - | - | - |
3.8985 | 48 | - | 0.7949 | 0.7936 | 0.7926 | 0.7767 | 0.7512 |
- The bold row denotes the saved checkpoint.
Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.3.1
- Transformers: 4.41.2
- PyTorch: 2.1.2+cu121
- Accelerate: 0.34.2
- 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}
}
- Downloads last month
- 22
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.
Model tree for SMARTICT/bge-base-financial-matryoshka
Base model
BAAI/bge-base-en-v1.5Evaluation results
- Cosine Accuracy@1 on dim 768self-reported0.677
- Cosine Accuracy@3 on dim 768self-reported0.814
- Cosine Accuracy@5 on dim 768self-reported0.864
- Cosine Accuracy@10 on dim 768self-reported0.914
- Cosine Precision@1 on dim 768self-reported0.677
- Cosine Precision@3 on dim 768self-reported0.271
- Cosine Precision@5 on dim 768self-reported0.173
- Cosine Precision@10 on dim 768self-reported0.091
- Cosine Recall@1 on dim 768self-reported0.677
- Cosine Recall@3 on dim 768self-reported0.814