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("cngcv/bge-base-financial-matryoshka")
# Run inference
sentences = [
'After text generation, the process involves providing test data to NT Q, which then undergoes article correction, including dealing with fragmented articles and errors.',
'What is the process for providing test data to NT Q after text generation?',
'What is the significance of the dates in the text?',
]
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 | 0.7755 |
cosine_accuracy@3 | 0.8776 |
cosine_accuracy@5 | 0.9592 |
cosine_accuracy@10 | 0.9796 |
cosine_precision@1 | 0.7755 |
cosine_precision@3 | 0.2925 |
cosine_precision@5 | 0.1918 |
cosine_precision@10 | 0.098 |
cosine_recall@1 | 0.7755 |
cosine_recall@3 | 0.8776 |
cosine_recall@5 | 0.9592 |
cosine_recall@10 | 0.9796 |
cosine_ndcg@10 | 0.8776 |
cosine_mrr@10 | 0.8448 |
cosine_map@100 | 0.8464 |
Information Retrieval
- Dataset:
dim_512
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 0.7959 |
cosine_accuracy@3 | 0.898 |
cosine_accuracy@5 | 0.9592 |
cosine_accuracy@10 | 0.9796 |
cosine_precision@1 | 0.7959 |
cosine_precision@3 | 0.2993 |
cosine_precision@5 | 0.1918 |
cosine_precision@10 | 0.098 |
cosine_recall@1 | 0.7959 |
cosine_recall@3 | 0.898 |
cosine_recall@5 | 0.9592 |
cosine_recall@10 | 0.9796 |
cosine_ndcg@10 | 0.8846 |
cosine_mrr@10 | 0.8539 |
cosine_map@100 | 0.8551 |
Information Retrieval
- Dataset:
dim_256
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 0.6939 |
cosine_accuracy@3 | 0.9184 |
cosine_accuracy@5 | 0.9592 |
cosine_accuracy@10 | 0.9592 |
cosine_precision@1 | 0.6939 |
cosine_precision@3 | 0.3061 |
cosine_precision@5 | 0.1918 |
cosine_precision@10 | 0.0959 |
cosine_recall@1 | 0.6939 |
cosine_recall@3 | 0.9184 |
cosine_recall@5 | 0.9592 |
cosine_recall@10 | 0.9592 |
cosine_ndcg@10 | 0.8397 |
cosine_mrr@10 | 0.7993 |
cosine_map@100 | 0.8017 |
Information Retrieval
- Dataset:
dim_128
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 0.6939 |
cosine_accuracy@3 | 0.9184 |
cosine_accuracy@5 | 0.9184 |
cosine_accuracy@10 | 0.9184 |
cosine_precision@1 | 0.6939 |
cosine_precision@3 | 0.3061 |
cosine_precision@5 | 0.1837 |
cosine_precision@10 | 0.0918 |
cosine_recall@1 | 0.6939 |
cosine_recall@3 | 0.9184 |
cosine_recall@5 | 0.9184 |
cosine_recall@10 | 0.9184 |
cosine_ndcg@10 | 0.8168 |
cosine_mrr@10 | 0.7823 |
cosine_map@100 | 0.7866 |
Information Retrieval
- Dataset:
dim_64
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 0.5918 |
cosine_accuracy@3 | 0.7959 |
cosine_accuracy@5 | 0.8163 |
cosine_accuracy@10 | 0.9184 |
cosine_precision@1 | 0.5918 |
cosine_precision@3 | 0.2653 |
cosine_precision@5 | 0.1633 |
cosine_precision@10 | 0.0918 |
cosine_recall@1 | 0.5918 |
cosine_recall@3 | 0.7959 |
cosine_recall@5 | 0.8163 |
cosine_recall@10 | 0.9184 |
cosine_ndcg@10 | 0.7471 |
cosine_mrr@10 | 0.6929 |
cosine_map@100 | 0.6978 |
Training Details
Training Dataset
Unnamed Dataset
- Size: 196 training samples
- Columns:
positive
andanchor
- Approximate statistics based on the first 1000 samples:
positive anchor type string string details - min: 15 tokens
- mean: 46.58 tokens
- max: 118 tokens
- min: 10 tokens
- mean: 17.25 tokens
- max: 43 tokens
- Samples:
positive anchor The document lists several tasks with their statuses, such as "Done", "In progress", and "To be done". These statuses indicate the current progress of each task within the project. For example, "Set up environment" and "Set up development environment" are marked as "Done", suggesting these tasks have been completed, while "Build translation data set" is marked as "In progress", indicating it is currently being worked on.
What is the status of the project tasks mentioned in the document?
The 'Web Application Construction' task is mentioned to be completed by NT Q, with a duration from July 17, 2023, to July 28, 2023, and is marked as 'Done' with a completion of 10 tasks.
What is the scope of the 'Web Application Construction' task?
"RE F" could potentially stand for "Reference File" or "Record File," indicating that this text might be part of a larger dataset or document used for reference or record-keeping purposes.
What is the significance of the "RE F" at the beginning of the text?
- 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
: 0.1tf32
: 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
: Falsefp16_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
: Falseeval_on_start
: Falsebatch_sampler
: no_duplicatesmulti_dataset_batch_sampler
: proportional
Training Logs
Epoch | Step | 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 |
---|---|---|---|---|---|---|
1.0 | 1 | 0.6908 | 0.7097 | 0.8111 | 0.6240 | 0.8011 |
2.0 | 2 | 0.7292 | 0.7692 | 0.8177 | 0.6634 | 0.8162 |
3.0 | 3 | 0.7555 | 0.8014 | 0.8541 | 0.6992 | 0.8451 |
4.0 | 4 | 0.7866 | 0.8017 | 0.8551 | 0.6978 | 0.8464 |
- The bold row denotes the saved checkpoint.
Framework Versions
- Python: 3.10.13
- Sentence Transformers: 3.0.1
- Transformers: 4.42.3
- PyTorch: 2.1.2
- Accelerate: 0.32.1
- Datasets: 2.20.0
- 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 cngcv/bge-base-financial-matryoshka
Base model
BAAI/bge-base-en-v1.5
Finetuned
this model
Evaluation results
- Cosine Accuracy@1 on dim 768self-reported0.776
- Cosine Accuracy@3 on dim 768self-reported0.878
- Cosine Accuracy@5 on dim 768self-reported0.959
- Cosine Accuracy@10 on dim 768self-reported0.980
- Cosine Precision@1 on dim 768self-reported0.776
- Cosine Precision@3 on dim 768self-reported0.293
- Cosine Precision@5 on dim 768self-reported0.192
- Cosine Precision@10 on dim 768self-reported0.098
- Cosine Recall@1 on dim 768self-reported0.776
- Cosine Recall@3 on dim 768self-reported0.878