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("juanpablomesa/bge-base-financial-matryoshka")
# Run inference
sentences = [
'HTC called the Samsung Galaxy S4 “mainstream”.',
'What did HTC announce about the Samsung Galaxy S4?',
"What is the essential aspect of the vocation to marriage according to Benedict XVI's message on the 40th Anniversary of Humanae Vitae?",
]
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 | 0.9675 |
cosine_accuracy@3 | 0.9792 |
cosine_accuracy@5 | 0.9829 |
cosine_accuracy@10 | 0.9888 |
cosine_precision@1 | 0.9675 |
cosine_precision@3 | 0.3264 |
cosine_precision@5 | 0.1966 |
cosine_precision@10 | 0.0989 |
cosine_recall@1 | 0.9675 |
cosine_recall@3 | 0.9792 |
cosine_recall@5 | 0.9829 |
cosine_recall@10 | 0.9888 |
cosine_ndcg@10 | 0.9777 |
cosine_mrr@10 | 0.9742 |
cosine_map@100 | 0.9745 |
Information Retrieval
- Dataset:
dim_512
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 0.9642 |
cosine_accuracy@3 | 0.9775 |
cosine_accuracy@5 | 0.9817 |
cosine_accuracy@10 | 0.9888 |
cosine_precision@1 | 0.9642 |
cosine_precision@3 | 0.3258 |
cosine_precision@5 | 0.1963 |
cosine_precision@10 | 0.0989 |
cosine_recall@1 | 0.9642 |
cosine_recall@3 | 0.9775 |
cosine_recall@5 | 0.9817 |
cosine_recall@10 | 0.9888 |
cosine_ndcg@10 | 0.9759 |
cosine_mrr@10 | 0.9718 |
cosine_map@100 | 0.972 |
Information Retrieval
- Dataset:
dim_256
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 0.9621 |
cosine_accuracy@3 | 0.9742 |
cosine_accuracy@5 | 0.9804 |
cosine_accuracy@10 | 0.9862 |
cosine_precision@1 | 0.9621 |
cosine_precision@3 | 0.3247 |
cosine_precision@5 | 0.1961 |
cosine_precision@10 | 0.0986 |
cosine_recall@1 | 0.9621 |
cosine_recall@3 | 0.9742 |
cosine_recall@5 | 0.9804 |
cosine_recall@10 | 0.9862 |
cosine_ndcg@10 | 0.9738 |
cosine_mrr@10 | 0.9698 |
cosine_map@100 | 0.9702 |
Information Retrieval
- Dataset:
dim_128
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 0.9554 |
cosine_accuracy@3 | 0.97 |
cosine_accuracy@5 | 0.9767 |
cosine_accuracy@10 | 0.9838 |
cosine_precision@1 | 0.9554 |
cosine_precision@3 | 0.3233 |
cosine_precision@5 | 0.1953 |
cosine_precision@10 | 0.0984 |
cosine_recall@1 | 0.9554 |
cosine_recall@3 | 0.97 |
cosine_recall@5 | 0.9767 |
cosine_recall@10 | 0.9838 |
cosine_ndcg@10 | 0.9693 |
cosine_mrr@10 | 0.9647 |
cosine_map@100 | 0.9652 |
Information Retrieval
- Dataset:
dim_64
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 0.9392 |
cosine_accuracy@3 | 0.9617 |
cosine_accuracy@5 | 0.9667 |
cosine_accuracy@10 | 0.9758 |
cosine_precision@1 | 0.9392 |
cosine_precision@3 | 0.3206 |
cosine_precision@5 | 0.1933 |
cosine_precision@10 | 0.0976 |
cosine_recall@1 | 0.9392 |
cosine_recall@3 | 0.9617 |
cosine_recall@5 | 0.9667 |
cosine_recall@10 | 0.9758 |
cosine_ndcg@10 | 0.9577 |
cosine_mrr@10 | 0.9519 |
cosine_map@100 | 0.9525 |
Training Details
Training Dataset
Unnamed Dataset
- Size: 9,600 training samples
- Columns:
positive
andanchor
- Approximate statistics based on the first 1000 samples:
positive anchor type string string details - min: 3 tokens
- mean: 50.19 tokens
- max: 435 tokens
- min: 3 tokens
- mean: 18.66 tokens
- max: 43 tokens
- Samples:
positive anchor The Berry Export Summary 2028 is a dedicated export plan for the Australian strawberry, raspberry, and blackberry industries. It maps the sectors’ current position, where they want to be, high-opportunity markets, and next steps. The purpose of this plan is to grow their global presence over the next 10 years.
What is the Berry Export Summary 2028 and what is its purpose?
Benefits reported from having access to Self-supply water sources include convenience, less time spent for fetching water and access to more and better quality water. In some areas, Self-supply sources offer important added values such as water for productive use, income generation, family safety and improved food security.
What are some of the benefits reported from having access to Self-supply water sources?
The unique features of the Coolands for Twitter app include Real-Time updates without the need for a refresh button, Avatar Indicator which shows small avatars on the title bar for new messages, Direct Link for intuitive and convenient link opening, Smart Bookmark to easily return to previous reading position, and User Level Notification which allows customized notification settings for different users.
What are the unique features of the Coolands for Twitter app?
- 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 |
---|---|---|---|---|---|---|---|
0.5333 | 10 | 0.6065 | - | - | - | - | - |
0.96 | 18 | - | 0.9583 | 0.9674 | 0.9695 | 0.9372 | 0.9708 |
1.0667 | 20 | 0.3313 | - | - | - | - | - |
1.6 | 30 | 0.144 | - | - | - | - | - |
1.9733 | 37 | - | 0.9630 | 0.9699 | 0.9716 | 0.9488 | 0.9745 |
2.1333 | 40 | 0.1317 | - | - | - | - | - |
2.6667 | 50 | 0.0749 | - | - | - | - | - |
2.9867 | 56 | - | 0.9650 | 0.9701 | 0.9721 | 0.9522 | 0.9747 |
3.2 | 60 | 0.088 | - | - | - | - | - |
3.7333 | 70 | 0.0598 | - | - | - | - | - |
3.84 | 72 | - | 0.9652 | 0.9702 | 0.972 | 0.9525 | 0.9745 |
- The bold row denotes the saved checkpoint.
Framework Versions
- Python: 3.11.5
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.1.2+cu121
- 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 juanpablomesa/bge-base-financial-matryoshka
Base model
BAAI/bge-base-en-v1.5
Finetuned
this model
Evaluation results
- Cosine Accuracy@1 on dim 768self-reported0.968
- Cosine Accuracy@3 on dim 768self-reported0.979
- Cosine Accuracy@5 on dim 768self-reported0.983
- Cosine Accuracy@10 on dim 768self-reported0.989
- Cosine Precision@1 on dim 768self-reported0.968
- Cosine Precision@3 on dim 768self-reported0.326
- Cosine Precision@5 on dim 768self-reported0.197
- Cosine Precision@10 on dim 768self-reported0.099
- Cosine Recall@1 on dim 768self-reported0.968
- Cosine Recall@3 on dim 768self-reported0.979