metadata

base_model: mixedbread-ai/mxbai-embed-large-v1
datasets: []
language:
  - en
library_name: sentence-transformers
license: apache-2.0
metrics:
  - cosine_accuracy@1
  - cosine_accuracy@3
  - cosine_accuracy@5
  - cosine_accuracy@10
  - cosine_precision@1
  - cosine_precision@3
  - cosine_precision@5
  - cosine_precision@10
  - cosine_recall@1
  - cosine_recall@3
  - cosine_recall@5
  - cosine_recall@10
  - cosine_ndcg@10
  - cosine_mrr@10
  - cosine_map@100
pipeline_tag: sentence-similarity
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:580
  - loss:MatryoshkaLoss
  - loss:MultipleNegativesRankingLoss
widget:
  - source_sentence: >-
      In response to hypothetical economic scenarios presented by the Federal
      Reserve, Wells Fargo formulated a capital action plan. This was done as a
      part of the CCAR (Comprehensive Capital Analysis and Review) process. The
      scenarios tested included a hypothetical severe global recession which, at
      its most stressful point, reduces our Pre-Provision Net Revenue (PPNR) to
      negative levels for four consecutive quarters.
    sentences:
      - >-
        What is the proposed dividend per share for the shareholders of Apple
        Inc. for the financial year ending in 2023?
      - >-
        What steps has Wells Fargo undertaken to sustain in the event of a
        severe global recession?
      - What was the total net income for Intel in 2021?
  - source_sentence: >-
      Microsoft Corporation has been paying consistent dividends to its
      shareholders on a quarterly basis. The company's Board of Directors
      reviews the dividend policy on a regular basis and plans to continue
      paying quarterly dividends, subject to capital availability and financial
      conditions
    sentences:
      - >-
        What did Amazon.com, Inc. anticipate regarding its free cash flows in
        the future?
      - What is Tesla's outlook for 2024 in terms of vehicle production?
      - What is Microsoft Corporation's dividend policy?
  - source_sentence: >-
      In the second quarter of 2023, Tesla's automotive revenue increased by 58%
      compared to the same period previous year. These results were primarily
      driven by increased vehicle deliveries and expansion in the China market.
    sentences:
      - >-
        What action did the Federal Reserve take to address the inflation surge
        in 2027?
      - What revenue did Apple Inc. report in the first quarter of 2021?
      - >-
        How did Tesla's automotive revenue perform in the second quarter of
        2023?
  - source_sentence: >-
      Intel Corporation is an American multinational corporation and technology
      company headquartered in Santa Clara, California. It's primarily known for
      designing and manufacturing semiconductors and various technology
      solutions, including processors for computer systems and servers,
      integrated digital technology platforms, and system-on-chip units for
      gateways.
    sentences:
      - What is Intel's main area of business?
      - >-
        What was the revenue growth percentage of Amazon in the second quarter
        of 2024?
      - How much capital expenditure did Amazon.com report in 2025?
  - source_sentence: In 2023, EnergyCorp declared a dividend of $2.5 per share.
    sentences:
      - >-
        How did Amazon’s shift to one-day prime delivery affect its operational
        costs in 2023?
      - What dividend did the EnergyCorp pay to its shareholders in 2023?
      - What was the profit margin of Airbus in the year 2025?
model-index:
  - name: Bmixedbread-ai/mxbai-embed-large-v1 Financial Matryoshka
    results:
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 1024
          type: dim_1024
        metrics:
          - type: cosine_accuracy@1
            value: 0.8923076923076924
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.9692307692307692
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.9692307692307692
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9846153846153847
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.8923076923076924
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.32307692307692304
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.1938461538461538
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09846153846153843
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.8923076923076924
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.9692307692307692
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.9692307692307692
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9846153846153847
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.941940347600734
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.927838827838828
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.928083028083028
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 768
          type: dim_768
        metrics:
          - type: cosine_accuracy@1
            value: 0.8923076923076924
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.9692307692307692
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.9692307692307692
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9846153846153847
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.8923076923076924
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.32307692307692304
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.1938461538461538
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09846153846153843
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.8923076923076924
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.9692307692307692
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.9692307692307692
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9846153846153847
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.9422922530434215
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.9282051282051282
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.9284418145956608
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 512
          type: dim_512
        metrics:
          - type: cosine_accuracy@1
            value: 0.8923076923076924
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.9692307692307692
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.9692307692307692
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9846153846153847
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.8923076923076924
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.32307692307692304
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.1938461538461538
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09846153846153843
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.8923076923076924
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.9692307692307692
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.9692307692307692
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9846153846153847
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.941940347600734
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.927838827838828
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.928113553113553
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 256
          type: dim_256
        metrics:
          - type: cosine_accuracy@1
            value: 0.8923076923076924
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.9692307692307692
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.9692307692307692
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9846153846153847
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.8923076923076924
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.32307692307692304
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.1938461538461538
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09846153846153843
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.8923076923076924
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.9692307692307692
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.9692307692307692
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9846153846153847
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.9416654482692324
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.9275641025641026
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.9278846153846154
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 128
          type: dim_128
        metrics:
          - type: cosine_accuracy@1
            value: 0.8461538461538461
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.9538461538461539
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.9692307692307692
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9846153846153847
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.8461538461538461
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.31794871794871793
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.1938461538461538
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09846153846153843
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.8461538461538461
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.9538461538461539
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.9692307692307692
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9846153846153847
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.9221774232775186
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.9012820512820513
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.9016398330351819
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 64
          type: dim_64
        metrics:
          - type: cosine_accuracy@1
            value: 0.8153846153846154
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.9692307692307692
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.9846153846153847
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9846153846153847
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.8153846153846154
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.32307692307692304
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.19692307692307687
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09846153846153843
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.8153846153846154
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.9692307692307692
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.9846153846153847
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9846153846153847
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.9123594012651499
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.8876923076923079
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.8879622132253712
            name: Cosine Map@100

Bmixedbread-ai/mxbai-embed-large-v1 Financial Matryoshka

This is a sentence-transformers model finetuned from mixedbread-ai/mxbai-embed-large-v1. It maps sentences & paragraphs to a 1024-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: mixedbread-ai/mxbai-embed-large-v1
Maximum Sequence Length: 512 tokens
Output Dimensionality: 1024 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': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 1024, '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})
)

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("rbhatia46/mxbai-embed-large-v1-financial-rag-matryoshka")
# Run inference
sentences = [
    'In 2023, EnergyCorp declared a dividend of $2.5 per share.',
    'What dividend did the EnergyCorp pay to its shareholders in 2023?',
    'How did Amazon’s shift to one-day prime delivery affect its operational costs in 2023?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Evaluation

Metrics

Information Retrieval

Dataset: dim_1024
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.8923
cosine_accuracy@3	0.9692
cosine_accuracy@5	0.9692
cosine_accuracy@10	0.9846
cosine_precision@1	0.8923
cosine_precision@3	0.3231
cosine_precision@5	0.1938
cosine_precision@10	0.0985
cosine_recall@1	0.8923
cosine_recall@3	0.9692
cosine_recall@5	0.9692
cosine_recall@10	0.9846
cosine_ndcg@10	0.9419
cosine_mrr@10	0.9278
cosine_map@100	0.9281

Information Retrieval

Dataset: dim_768
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.8923
cosine_accuracy@3	0.9692
cosine_accuracy@5	0.9692
cosine_accuracy@10	0.9846
cosine_precision@1	0.8923
cosine_precision@3	0.3231
cosine_precision@5	0.1938
cosine_precision@10	0.0985
cosine_recall@1	0.8923
cosine_recall@3	0.9692
cosine_recall@5	0.9692
cosine_recall@10	0.9846
cosine_ndcg@10	0.9423
cosine_mrr@10	0.9282
cosine_map@100	0.9284

Information Retrieval

Dataset: dim_512
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.8923
cosine_accuracy@3	0.9692
cosine_accuracy@5	0.9692
cosine_accuracy@10	0.9846
cosine_precision@1	0.8923
cosine_precision@3	0.3231
cosine_precision@5	0.1938
cosine_precision@10	0.0985
cosine_recall@1	0.8923
cosine_recall@3	0.9692
cosine_recall@5	0.9692
cosine_recall@10	0.9846
cosine_ndcg@10	0.9419
cosine_mrr@10	0.9278
cosine_map@100	0.9281

Information Retrieval

Dataset: dim_256
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.8923
cosine_accuracy@3	0.9692
cosine_accuracy@5	0.9692
cosine_accuracy@10	0.9846
cosine_precision@1	0.8923
cosine_precision@3	0.3231
cosine_precision@5	0.1938
cosine_precision@10	0.0985
cosine_recall@1	0.8923
cosine_recall@3	0.9692
cosine_recall@5	0.9692
cosine_recall@10	0.9846
cosine_ndcg@10	0.9417
cosine_mrr@10	0.9276
cosine_map@100	0.9279

Information Retrieval

Dataset: dim_128
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.8462
cosine_accuracy@3	0.9538
cosine_accuracy@5	0.9692
cosine_accuracy@10	0.9846
cosine_precision@1	0.8462
cosine_precision@3	0.3179
cosine_precision@5	0.1938
cosine_precision@10	0.0985
cosine_recall@1	0.8462
cosine_recall@3	0.9538
cosine_recall@5	0.9692
cosine_recall@10	0.9846
cosine_ndcg@10	0.9222
cosine_mrr@10	0.9013
cosine_map@100	0.9016

Information Retrieval

Dataset: dim_64
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.8154
cosine_accuracy@3	0.9692
cosine_accuracy@5	0.9846
cosine_accuracy@10	0.9846
cosine_precision@1	0.8154
cosine_precision@3	0.3231
cosine_precision@5	0.1969
cosine_precision@10	0.0985
cosine_recall@1	0.8154
cosine_recall@3	0.9692
cosine_recall@5	0.9846
cosine_recall@10	0.9846
cosine_ndcg@10	0.9124
cosine_mrr@10	0.8877
cosine_map@100	0.888

Training Details

Training Dataset

Unnamed Dataset

Size: 580 training samples
Columns: positive and anchor
Approximate statistics based on the first 1000 samples:
positive anchor
type string string
details
min: 16 tokens
mean: 44.21 tokens
max: 98 tokens

min: 9 tokens
mean: 17.5 tokens
max: 30 tokens

	positive	anchor
type	string	string
details	min: 16 tokens mean: 44.21 tokens max: 98 tokens	min: 9 tokens mean: 17.5 tokens max: 30 tokens

Samples:

positive	anchor
`For the fiscal year 2020, Microsoft Corporation reported a net income of $44.3 billion, showing a 13% increase from the previous year.`	`What was the net income of Microsoft Corporation for the fiscal year 2020?`
`As of the latest financial report, Amazon has a current price to earnings ratio (P/E ratio) of 76.6.`	`What is Amazon's current P/E ratio according to their latest financial report?`
`Microsoft Corporation posted an EBITDA (Earnings Before Interest, Taxes, Depreciation, and Amortization) margin of approximately 47% in 2021, showcasing strong profitability.`	`What was Microsoft Corporation's EBITDA margin in 2021?`

Loss: MatryoshkaLoss with these parameters:

{
    "loss": "MultipleNegativesRankingLoss",
    "matryoshka_dims": [
        1024,
        768,
        512,
        256,
        128,
        64
    ],
    "matryoshka_weights": [
        1,
        1,
        1,
        1,
        1,
        1
    ],
    "n_dims_per_step": -1
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: epoch
per_device_train_batch_size: 32
per_device_eval_batch_size: 16
gradient_accumulation_steps: 16
learning_rate: 2e-05
num_train_epochs: 4
lr_scheduler_type: cosine
warmup_ratio: 0.1
bf16: True
tf32: True
load_best_model_at_end: True
optim: adamw_torch_fused
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: epoch
prediction_loss_only: True
per_device_train_batch_size: 32
per_device_eval_batch_size: 16
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 16
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: 4
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: True
fp16: False
fp16_opt_level: O1
half_precision_backend: auto
bf16_full_eval: False
fp16_full_eval: False
tf32: True
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_fused
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
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	dim_1024_cosine_map@100	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
0.8421	1	0.9032	0.8846	0.9033	0.9109	0.8695	0.9186
1.6842	2	0.9121	0.8948	0.9174	0.9199	0.8777	0.9198
2.5263	3	0.9281	0.9013	0.9202	0.9281	0.8879	0.9204
3.3684	4	0.9281	0.9016	0.9279	0.9281	0.888	0.9284

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.10.6
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}
}