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("pavanmantha/bge-base-en-sec10k-embed")
# Run inference
sentences = [
'The Chief Executive etc. does not manage segment results or allocate resources to segments when considering these costs and they are therefore excluded from our definition of segment income.',
'What components are excluded from segment income definition according to company management?',
'What was the increase in sales and marketing expenses for the year ended December 31, 2023 compared to 2022?',
]
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 | 0.7143 |
cosine_accuracy@3 | 0.83 |
cosine_accuracy@5 | 0.8586 |
cosine_accuracy@10 | 0.9043 |
cosine_precision@1 | 0.7143 |
cosine_precision@3 | 0.2767 |
cosine_precision@5 | 0.1717 |
cosine_precision@10 | 0.0904 |
cosine_recall@1 | 0.7143 |
cosine_recall@3 | 0.83 |
cosine_recall@5 | 0.8586 |
cosine_recall@10 | 0.9043 |
cosine_ndcg@10 | 0.8098 |
cosine_mrr@10 | 0.7797 |
cosine_map@100 | 0.7832 |
Information Retrieval
- Dataset:
dim_512
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 0.7157 |
cosine_accuracy@3 | 0.8243 |
cosine_accuracy@5 | 0.8543 |
cosine_accuracy@10 | 0.8943 |
cosine_precision@1 | 0.7157 |
cosine_precision@3 | 0.2748 |
cosine_precision@5 | 0.1709 |
cosine_precision@10 | 0.0894 |
cosine_recall@1 | 0.7157 |
cosine_recall@3 | 0.8243 |
cosine_recall@5 | 0.8543 |
cosine_recall@10 | 0.8943 |
cosine_ndcg@10 | 0.8057 |
cosine_mrr@10 | 0.7772 |
cosine_map@100 | 0.7814 |
Information Retrieval
- Dataset:
dim_256
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 0.7057 |
cosine_accuracy@3 | 0.8186 |
cosine_accuracy@5 | 0.8529 |
cosine_accuracy@10 | 0.8929 |
cosine_precision@1 | 0.7057 |
cosine_precision@3 | 0.2729 |
cosine_precision@5 | 0.1706 |
cosine_precision@10 | 0.0893 |
cosine_recall@1 | 0.7057 |
cosine_recall@3 | 0.8186 |
cosine_recall@5 | 0.8529 |
cosine_recall@10 | 0.8929 |
cosine_ndcg@10 | 0.7998 |
cosine_mrr@10 | 0.77 |
cosine_map@100 | 0.7739 |
Information Retrieval
- Dataset:
dim_128
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 0.6871 |
cosine_accuracy@3 | 0.8057 |
cosine_accuracy@5 | 0.8386 |
cosine_accuracy@10 | 0.8871 |
cosine_precision@1 | 0.6871 |
cosine_precision@3 | 0.2686 |
cosine_precision@5 | 0.1677 |
cosine_precision@10 | 0.0887 |
cosine_recall@1 | 0.6871 |
cosine_recall@3 | 0.8057 |
cosine_recall@5 | 0.8386 |
cosine_recall@10 | 0.8871 |
cosine_ndcg@10 | 0.7865 |
cosine_mrr@10 | 0.7544 |
cosine_map@100 | 0.7584 |
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: 9 tokens
- mean: 46.84 tokens
- max: 326 tokens
- min: 8 tokens
- mean: 20.44 tokens
- max: 43 tokens
- Samples:
positive anchor The federal banking regulators’ guidance on sound incentive compensation practices sets forth three key principles for incentive compensation arrangements that are designed to help ensure such plans do not encourage imprudent risk-taking and align with the safety and soundness of the organization. These principles include balancing risk with financial results, compatibility with internal controls and risk management, and support from strong corporate governance with effective oversight by the board.
What are the three principles set forth by federal banking regulators' guidance on incentive compensation practices?
Delta Air Lines generated a free cash flow of $2,003 million in 2023. This figure was adjusted for several factors including net redemptions of short-term investments and a pilot agreement payment of $735 million.
How much free cash flow did Delta Air Lines generate in 2023?
Inherent in the qualitative assessment are estimates and assumptions about our consideration of events and circumstances that may indicate a potential impairment, including industry and market conditions, expected cost pressures, expected financial performance, and general macroeconomic conditions.
What does the qualitative assessment of goodwill consider regarding possible impairment?
- Loss:
MatryoshkaLoss
with these parameters:{ "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 768, 512, 256, 128 ], "matryoshka_weights": [ 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
: 0.1fp16
: Truetf32
: Falseload_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
: Falsefp16
: Truefp16_opt_level
: O1half_precision_backend
: autobf16_full_eval
: Falsefp16_full_eval
: Falsetf32
: Falselocal_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_768_cosine_map@100 |
---|---|---|---|---|---|---|
0.8122 | 10 | 1.1625 | - | - | - | - |
0.9746 | 12 | - | 0.7429 | 0.7568 | 0.7688 | 0.7724 |
1.6244 | 20 | 0.4282 | - | - | - | - |
1.9492 | 24 | - | 0.7541 | 0.7691 | 0.7802 | 0.7828 |
2.4365 | 30 | 0.3086 | - | - | - | - |
2.9239 | 36 | - | 0.7581 | 0.7731 | 0.7810 | 0.7838 |
3.2487 | 40 | 0.2432 | - | - | - | - |
3.8985 | 48 | - | 0.7584 | 0.7739 | 0.7814 | 0.7832 |
- The bold row denotes the saved checkpoint.
Framework Versions
- Python: 3.10.13
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.1.2
- Accelerate: 0.31.0
- 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 pavanmantha/bge-base-en-sec10k-embed
Base model
BAAI/bge-base-en-v1.5Evaluation results
- Cosine Accuracy@1 on dim 768self-reported0.714
- Cosine Accuracy@3 on dim 768self-reported0.830
- Cosine Accuracy@5 on dim 768self-reported0.859
- Cosine Accuracy@10 on dim 768self-reported0.904
- Cosine Precision@1 on dim 768self-reported0.714
- Cosine Precision@3 on dim 768self-reported0.277
- Cosine Precision@5 on dim 768self-reported0.172
- Cosine Precision@10 on dim 768self-reported0.090
- Cosine Recall@1 on dim 768self-reported0.714
- Cosine Recall@3 on dim 768self-reported0.830