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fine-tuned-matryoshka-1000

Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
Generated from Trainer
dataset_size:1000
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
Eval Results
text-embeddings-inference
Model card Files
xet
Community

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

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("joshuapb/fine-tuned-matryoshka-1000")
# Run inference
sentences = [
    '(1) Joint: join with step 2, where the few-shot examples are structured as (response, verification questions, verification answers); The drawback is that the original response is in the context, so the model may repeat similar hallucination.\n(2) 2-step: separate the verification planning and execution steps, such as the original response doesn’t impact\n(3) Factored: each verification question is answered separately. Say, if a long-form base generation results in multiple verification questions, we would answer each question one-by-one.\n(4) Factor+revise: adding a “cross-checking” step after factored verification execution, conditioned on both the baseline response and the verification question and answer. It detects inconsistency.\n\n\nFinal output: Generate the final, refined output. The output gets revised at this step if any inconsistency is discovered.',
    "In what ways does the 'Factor+revise' method enhance the reliability of responses when compared to the 'Joint' and '2-step' methods used for cross-checking?",
    'What obstacles arise when depending on the pre-training dataset in the context of extrinsic hallucination affecting model outputs?',
]
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

Metric Value
cosine_accuracy@1 0.8802
cosine_accuracy@3 0.9844
cosine_accuracy@5 0.9948
cosine_accuracy@10 0.9948
cosine_precision@1 0.8802
cosine_precision@3 0.3281
cosine_precision@5 0.199
cosine_precision@10 0.0995
cosine_recall@1 0.8802
cosine_recall@3 0.9844
cosine_recall@5 0.9948
cosine_recall@10 0.9948
cosine_ndcg@10 0.9495
cosine_mrr@10 0.9338
cosine_map@100 0.9342

Information Retrieval

Metric Value
cosine_accuracy@1 0.8854
cosine_accuracy@3 0.9844
cosine_accuracy@5 0.9948
cosine_accuracy@10 1.0
cosine_precision@1 0.8854
cosine_precision@3 0.3281
cosine_precision@5 0.199
cosine_precision@10 0.1
cosine_recall@1 0.8854
cosine_recall@3 0.9844
cosine_recall@5 0.9948
cosine_recall@10 1.0
cosine_ndcg@10 0.9537
cosine_mrr@10 0.9378
cosine_map@100 0.9378

Information Retrieval

Metric Value
cosine_accuracy@1 0.901
cosine_accuracy@3 0.9844
cosine_accuracy@5 1.0
cosine_accuracy@10 1.0
cosine_precision@1 0.901
cosine_precision@3 0.3281
cosine_precision@5 0.2
cosine_precision@10 0.1
cosine_recall@1 0.901
cosine_recall@3 0.9844
cosine_recall@5 1.0
cosine_recall@10 1.0
cosine_ndcg@10 0.9588
cosine_mrr@10 0.9446
cosine_map@100 0.9446

Information Retrieval

Metric Value
cosine_accuracy@1 0.9062
cosine_accuracy@3 0.9844
cosine_accuracy@5 1.0
cosine_accuracy@10 1.0
cosine_precision@1 0.9062
cosine_precision@3 0.3281
cosine_precision@5 0.2
cosine_precision@10 0.1
cosine_recall@1 0.9062
cosine_recall@3 0.9844
cosine_recall@5 1.0
cosine_recall@10 1.0
cosine_ndcg@10 0.9609
cosine_mrr@10 0.9475
cosine_map@100 0.9475

Information Retrieval

Metric Value
cosine_accuracy@1 0.8906
cosine_accuracy@3 0.9844
cosine_accuracy@5 1.0
cosine_accuracy@10 1.0
cosine_precision@1 0.8906
cosine_precision@3 0.3281
cosine_precision@5 0.2
cosine_precision@10 0.1
cosine_recall@1 0.8906
cosine_recall@3 0.9844
cosine_recall@5 1.0
cosine_recall@10 1.0
cosine_ndcg@10 0.9551
cosine_mrr@10 0.9397
cosine_map@100 0.9397

Training Details

Training Hyperparameters

Non-Default Hyperparameters

  • eval_strategy: epoch
  • per_device_eval_batch_size: 16
  • learning_rate: 2e-05
  • num_train_epochs: 5
  • lr_scheduler_type: cosine
  • warmup_ratio: 0.1
  • load_best_model_at_end: True

All Hyperparameters

Click to expand
  • overwrite_output_dir: False
  • do_predict: False
  • eval_strategy: epoch
  • prediction_loss_only: True
  • per_device_train_batch_size: 8
  • per_device_eval_batch_size: 16
  • per_gpu_train_batch_size: None
  • per_gpu_eval_batch_size: None
  • gradient_accumulation_steps: 1
  • eval_accumulation_steps: None
  • learning_rate: 2e-05
  • weight_decay: 0.0
  • adam_beta1: 0.9
  • adam_beta2: 0.999
  • adam_epsilon: 1e-08
  • max_grad_norm: 1.0
  • num_train_epochs: 5
  • max_steps: -1
  • lr_scheduler_type: cosine
  • lr_scheduler_kwargs: {}
  • warmup_ratio: 0.1
  • warmup_steps: 0
  • log_level: passive
  • log_level_replica: warning
  • log_on_each_node: True
  • logging_nan_inf_filter: True
  • save_safetensors: True
  • save_on_each_node: False
  • save_only_model: False
  • restore_callback_states_from_checkpoint: False
  • no_cuda: False
  • use_cpu: False
  • use_mps_device: False
  • seed: 42
  • data_seed: None
  • jit_mode_eval: False
  • use_ipex: False
  • bf16: False
  • fp16: False
  • fp16_opt_level: O1
  • half_precision_backend: auto
  • bf16_full_eval: False
  • fp16_full_eval: False
  • tf32: None
  • local_rank: 0
  • ddp_backend: None
  • tpu_num_cores: None
  • tpu_metrics_debug: False
  • debug: []
  • dataloader_drop_last: False
  • dataloader_num_workers: 0
  • dataloader_prefetch_factor: None
  • past_index: -1
  • disable_tqdm: False
  • remove_unused_columns: True
  • label_names: None
  • load_best_model_at_end: True
  • ignore_data_skip: False
  • fsdp: []
  • fsdp_min_num_params: 0
  • fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
  • fsdp_transformer_layer_cls_to_wrap: None
  • accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
  • deepspeed: None
  • label_smoothing_factor: 0.0
  • optim: adamw_torch
  • optim_args: None
  • adafactor: False
  • group_by_length: False
  • length_column_name: length
  • ddp_find_unused_parameters: None
  • ddp_bucket_cap_mb: None
  • ddp_broadcast_buffers: False
  • dataloader_pin_memory: True
  • dataloader_persistent_workers: False
  • skip_memory_metrics: True
  • use_legacy_prediction_loop: False
  • push_to_hub: False
  • resume_from_checkpoint: None
  • hub_model_id: None
  • hub_strategy: every_save
  • hub_private_repo: False
  • hub_always_push: False
  • gradient_checkpointing: False
  • gradient_checkpointing_kwargs: None
  • include_inputs_for_metrics: False
  • eval_do_concat_batches: True
  • fp16_backend: auto
  • push_to_hub_model_id: None
  • push_to_hub_organization: None
  • mp_parameters:
  • auto_find_batch_size: False
  • full_determinism: False
  • torchdynamo: None
  • ray_scope: last
  • ddp_timeout: 1800
  • torch_compile: False
  • torch_compile_backend: None
  • torch_compile_mode: None
  • dispatch_batches: None
  • split_batches: None
  • include_tokens_per_second: False
  • include_num_input_tokens_seen: False
  • neftune_noise_alpha: None
  • optim_target_modules: None
  • batch_eval_metrics: False
  • eval_on_start: False
  • batch_sampler: batch_sampler
  • multi_dataset_batch_sampler: proportional

Training Logs

Click to expand
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
0.04 5 4.9678 - - - - -
0.08 10 4.6482 - - - - -
0.12 15 5.0735 - - - - -
0.16 20 4.0336 - - - - -
0.2 25 3.7572 - - - - -
0.24 30 4.3054 - - - - -
0.28 35 2.6705 - - - - -
0.32 40 3.1929 - - - - -
0.36 45 3.1139 - - - - -
0.4 50 2.5219 - - - - -
0.44 55 3.1847 - - - - -
0.48 60 2.2306 - - - - -
0.52 65 2.251 - - - - -
0.56 70 2.2432 - - - - -
0.6 75 2.7462 - - - - -
0.64 80 2.9992 - - - - -
0.68 85 2.338 - - - - -
0.72 90 2.0169 - - - - -
0.76 95 1.257 - - - - -
0.8 100 1.5015 - - - - -
0.84 105 1.9198 - - - - -
0.88 110 2.2154 - - - - -
0.92 115 2.4026 - - - - -
0.96 120 1.911 - - - - -
1.0 125 2.079 0.9151 0.9098 0.9220 0.8788 0.9251
1.04 130 1.4704 - - - - -
1.08 135 0.7323 - - - - -
1.12 140 0.6308 - - - - -
1.16 145 0.4655 - - - - -
1.2 150 1.0186 - - - - -
1.24 155 1.1408 - - - - -
1.28 160 1.965 - - - - -
1.32 165 1.5987 - - - - -
1.3600 170 3.288 - - - - -
1.4 175 1.632 - - - - -
1.44 180 1.0376 - - - - -
1.48 185 0.9466 - - - - -
1.52 190 1.0106 - - - - -
1.56 195 1.4875 - - - - -
1.6 200 1.314 - - - - -
1.6400 205 1.3022 - - - - -
1.6800 210 1.5312 - - - - -
1.72 215 1.7982 - - - - -
1.76 220 1.7962 - - - - -
1.8 225 1.5788 - - - - -
1.8400 230 1.152 - - - - -
1.88 235 2.0556 - - - - -
1.92 240 1.3165 - - - - -
1.96 245 0.6941 - - - - -
2.0 250 1.2239 0.9404 0.944 0.9427 0.9327 0.9424
2.04 255 1.0423 - - - - -
2.08 260 0.8893 - - - - -
2.12 265 1.2859 - - - - -
2.16 270 1.4505 - - - - -
2.2 275 0.2728 - - - - -
2.24 280 0.6588 - - - - -
2.2800 285 0.8014 - - - - -
2.32 290 0.3053 - - - - -
2.36 295 1.4289 - - - - -
2.4 300 1.1458 - - - - -
2.44 305 0.6987 - - - - -
2.48 310 1.3389 - - - - -
2.52 315 1.2991 - - - - -
2.56 320 1.8088 - - - - -
2.6 325 0.4242 - - - - -
2.64 330 1.5873 - - - - -
2.68 335 1.3873 - - - - -
2.7200 340 1.4297 - - - - -
2.76 345 2.0637 - - - - -
2.8 350 1.1252 - - - - -
2.84 355 0.367 - - - - -
2.88 360 1.7606 - - - - -
2.92 365 1.196 - - - - -
2.96 370 1.8827 - - - - -
3.0 375 0.6822 0.9494 0.9479 0.9336 0.9414 0.9405
3.04 380 0.4954 - - - - -
3.08 385 0.1717 - - - - -
3.12 390 0.7435 - - - - -
3.16 395 1.4323 - - - - -
3.2 400 1.1207 - - - - -
3.24 405 1.9009 - - - - -
3.2800 410 1.6706 - - - - -
3.32 415 0.8378 - - - - -
3.36 420 1.0911 - - - - -
3.4 425 0.6565 - - - - -
3.44 430 1.0302 - - - - -
3.48 435 0.6425 - - - - -
3.52 440 1.1472 - - - - -
3.56 445 1.996 - - - - -
3.6 450 1.5308 - - - - -
3.64 455 0.7427 - - - - -
3.68 460 1.4596 - - - - -
3.7200 465 1.1984 - - - - -
3.76 470 0.7601 - - - - -
3.8 475 1.3544 - - - - -
3.84 480 1.6655 - - - - -
3.88 485 1.2596 - - - - -
3.92 490 0.9451 - - - - -
3.96 495 0.7079 - - - - -
4.0 500 1.3471 0.9453 0.9446 0.9404 0.9371 0.9335
4.04 505 0.4583 - - - - -
4.08 510 1.288 - - - - -
4.12 515 1.6946 - - - - -
4.16 520 1.1239 - - - - -
4.2 525 1.1026 - - - - -
4.24 530 1.4121 - - - - -
4.28 535 1.7113 - - - - -
4.32 540 0.8389 - - - - -
4.36 545 0.3117 - - - - -
4.4 550 0.3144 - - - - -
4.44 555 1.4694 - - - - -
4.48 560 1.3233 - - - - -
4.52 565 0.792 - - - - -
4.5600 570 0.4881 - - - - -
4.6 575 0.5097 - - - - -
4.64 580 1.6377 - - - - -
4.68 585 0.7273 - - - - -
4.72 590 1.5464 - - - - -
4.76 595 1.4392 - - - - -
4.8 600 1.4384 - - - - -
4.84 605 0.6375 - - - - -
4.88 610 1.0528 - - - - -
4.92 615 0.0276 - - - - -
4.96 620 0.9604 - - - - -
5.0 625 0.7219 0.9475 0.9446 0.9378 0.9397 0.9342
  • The bold row denotes the saved checkpoint.

Framework Versions

  • Python: 3.10.12
  • Sentence Transformers: 3.0.1
  • Transformers: 4.42.4
  • PyTorch: 2.3.1+cu121
  • Accelerate: 0.32.1
  • Datasets: 2.21.0
  • 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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