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:3550
  - loss:MatryoshkaLoss
  - loss:MultipleNegativesRankingLoss
widget:
  - source_sentence: >-
      At the end of 2023, Alphabet Inc. reported total debts amounting to $14.2
      billion, compared to $10.9 billion at the end of 2022.
    sentences:
      - What was the total debt of Alphabet Inc. as of the end of 2023?
      - >-
        What was ExxonMobil's contribution to the energy production in the
        Energy sector during 2020?
      - Describe Amazon's revenue growth in 2023?
  - source_sentence: >-
      In 2022, Pfizer strategically managed cash flow from investments by
      utilizing operating cash flow, issuing new debt, and through the
      monetization of certain non-core assets. This approach of diversifying the
      source of funding for investments was done to minimize risk and
      uncertainty in economic conditions.
    sentences:
      - >-
        How much capital expenditure did AUX Energy invest in renewable energy
        projects in 2022?
      - >-
        What effect did the 2023 market downturn have on Amazon's retail and
        cloud segments?
      - How did Pfizer manage cash flows from investments in 2022?
  - source_sentence: >-
      The primary revenue generators for JPMorgan Chase for the fiscal year 2023
      were the Corporate & Investment Bank (CIB) and the Asset & Wealth
      Management (AWM) sectors. The CIB sector benefited from a rise in merger
      and acquisition activities, while AWM saw large net inflows.
    sentences:
      - >-
        What is General Electric's strategic priority for its Aviation business
        segment?
      - >-
        Which sectors contributed the most to the revenue of JPMorgan Chase for
        FY 2023?
      - What is the principal activity of Apple Inc.?
  - source_sentence: >-
      For the fiscal year 2023, Microsoft's Intelligent Cloud segment generated
      revenues of $58 billion, demonstrating solid growth fueled by strong
      demand for cloud services and server products.
    sentences:
      - >-
        What is the primary strategy of McDonald’s to drive growth in the
        future?
      - >-
        What impact did the increase in gold prices have on Newmont
        Corporation's revenue in 2023?
      - >-
        What was the revenue generated by Microsoft's Intelligent Cloud segment
        for fiscal year 2023?
  - source_sentence: >-
      Microsoft, in their latest press release, revealed that they are
      anticipating a revenue growth of approximately 12% for the fiscal year
      ending in 2024.
    sentences:
      - What is Microsoft's projected revenue growth for fiscal year 2024?
      - >-
        What is the fair value of equity method investments of Microsoft in the
        fiscal year 2025?
      - What was the impact of COVID-19 on Zoom's profits?
model-index:
  - name: mxbai-embed-large-v1-financial-rag-matryoshka
    results:
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 1024
          type: dim_1024
        metrics:
          - type: cosine_accuracy@1
            value: 0.8455696202531645
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.9392405063291139
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.9670886075949368
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9898734177215189
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.8455696202531645
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.31308016877637135
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.19341772151898737
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.0989873417721519
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.8455696202531645
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.9392405063291139
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.9670886075949368
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9898734177215189
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.9212281141643793
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.898873819570022
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.8993853803492357
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 768
          type: dim_768
        metrics:
          - type: cosine_accuracy@1
            value: 0.8455696202531645
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.9392405063291139
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.9670886075949368
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9898734177215189
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.8455696202531645
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.3130801687763713
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.1934177215189873
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.0989873417721519
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.8455696202531645
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.9392405063291139
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.9670886075949368
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9898734177215189
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.9217284365901642
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.8994826200522402
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.8999494134557425
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 512
          type: dim_512
        metrics:
          - type: cosine_accuracy@1
            value: 0.8405063291139241
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.9367088607594937
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.9645569620253165
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9898734177215189
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.8405063291139241
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.31223628691983124
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.19291139240506328
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.0989873417721519
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.8405063291139241
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.9367088607594937
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.9645569620253165
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9898734177215189
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.9186273598847787
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.8954631303998389
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.8958871142668611
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 256
          type: dim_256
        metrics:
          - type: cosine_accuracy@1
            value: 0.8455696202531645
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.9392405063291139
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.9645569620253165
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9898734177215189
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.8455696202531645
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.3130801687763713
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.19291139240506328
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.0989873417721519
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.8455696202531645
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.9392405063291139
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.9645569620253165
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9898734177215189
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.9201161947922436
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.8975597749648381
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.8979721416614026
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 128
          type: dim_128
        metrics:
          - type: cosine_accuracy@1
            value: 0.8405063291139241
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.9417721518987342
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.9645569620253165
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9848101265822785
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.8405063291139241
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.3139240506329114
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.19291139240506328
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09848101265822784
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.8405063291139241
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.9417721518987342
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.9645569620253165
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9848101265822785
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.9170562815583235
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.8948693992364878
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.8957325656059834
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 64
          type: dim_64
        metrics:
          - type: cosine_accuracy@1
            value: 0.8405063291139241
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.9316455696202531
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.9569620253164557
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9822784810126582
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.8405063291139241
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.3105485232067511
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.19139240506329114
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09822784810126582
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.8405063291139241
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.9316455696202531
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.9569620253164557
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9822784810126582
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.9153318022971121
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.8934589109905566
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.8943102728098851
            name: Cosine Map@100

mxbai-embed-large-v1-financial-rag-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 = [
    'Microsoft, in their latest press release, revealed that they are anticipating a revenue growth of approximately 12% for the fiscal year ending in 2024.',
    "What is Microsoft's projected revenue growth for fiscal year 2024?",
    "What was the impact of COVID-19 on Zoom's profits?",
]
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	0.8456
cosine_accuracy@3	0.9392
cosine_accuracy@5	0.9671
cosine_accuracy@10	0.9899
cosine_precision@1	0.8456
cosine_precision@3	0.3131
cosine_precision@5	0.1934
cosine_precision@10	0.099
cosine_recall@1	0.8456
cosine_recall@3	0.9392
cosine_recall@5	0.9671
cosine_recall@10	0.9899
cosine_ndcg@10	0.9212
cosine_mrr@10	0.8989
cosine_map@100	0.8994

Information Retrieval

Dataset: dim_768
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.8456
cosine_accuracy@3	0.9392
cosine_accuracy@5	0.9671
cosine_accuracy@10	0.9899
cosine_precision@1	0.8456
cosine_precision@3	0.3131
cosine_precision@5	0.1934
cosine_precision@10	0.099
cosine_recall@1	0.8456
cosine_recall@3	0.9392
cosine_recall@5	0.9671
cosine_recall@10	0.9899
cosine_ndcg@10	0.9217
cosine_mrr@10	0.8995
cosine_map@100	0.8999

Information Retrieval

Dataset: dim_512
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.8405
cosine_accuracy@3	0.9367
cosine_accuracy@5	0.9646
cosine_accuracy@10	0.9899
cosine_precision@1	0.8405
cosine_precision@3	0.3122
cosine_precision@5	0.1929
cosine_precision@10	0.099
cosine_recall@1	0.8405
cosine_recall@3	0.9367
cosine_recall@5	0.9646
cosine_recall@10	0.9899
cosine_ndcg@10	0.9186
cosine_mrr@10	0.8955
cosine_map@100	0.8959

Information Retrieval

Dataset: dim_256
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.8456
cosine_accuracy@3	0.9392
cosine_accuracy@5	0.9646
cosine_accuracy@10	0.9899
cosine_precision@1	0.8456
cosine_precision@3	0.3131
cosine_precision@5	0.1929
cosine_precision@10	0.099
cosine_recall@1	0.8456
cosine_recall@3	0.9392
cosine_recall@5	0.9646
cosine_recall@10	0.9899
cosine_ndcg@10	0.9201
cosine_mrr@10	0.8976
cosine_map@100	0.898

Information Retrieval

Dataset: dim_128
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.8405
cosine_accuracy@3	0.9418
cosine_accuracy@5	0.9646
cosine_accuracy@10	0.9848
cosine_precision@1	0.8405
cosine_precision@3	0.3139
cosine_precision@5	0.1929
cosine_precision@10	0.0985
cosine_recall@1	0.8405
cosine_recall@3	0.9418
cosine_recall@5	0.9646
cosine_recall@10	0.9848
cosine_ndcg@10	0.9171
cosine_mrr@10	0.8949
cosine_map@100	0.8957

Information Retrieval

Dataset: dim_64
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.8405
cosine_accuracy@3	0.9316
cosine_accuracy@5	0.957
cosine_accuracy@10	0.9823
cosine_precision@1	0.8405
cosine_precision@3	0.3105
cosine_precision@5	0.1914
cosine_precision@10	0.0982
cosine_recall@1	0.8405
cosine_recall@3	0.9316
cosine_recall@5	0.957
cosine_recall@10	0.9823
cosine_ndcg@10	0.9153
cosine_mrr@10	0.8935
cosine_map@100	0.8943

Training Details

Training Dataset

Unnamed Dataset

Size: 3,550 training samples
Columns: positive and anchor
Approximate statistics based on the first 1000 samples:
positive anchor
type string string
details
min: 17 tokens
mean: 44.69 tokens
max: 105 tokens

min: 10 tokens
mean: 18.26 tokens
max: 30 tokens

	positive	anchor
type	string	string
details	min: 17 tokens mean: 44.69 tokens max: 105 tokens	min: 10 tokens mean: 18.26 tokens max: 30 tokens

Samples:

positive	anchor
`The total revenue for Google as of 2021 stands at approximately $181 billion, primarily driven by the performance of its advertising and cloud segments, hailing from the Information Technology sector.`	`What is the total revenue of Google as of 2021?`
`In Q4 2021, Amazon.com Inc. reported a significant increase in net income, reaching $14.3 billion, due to the surge in online shopping during the pandemic.`	`What was the Net Income of Amazon.com Inc. in Q4 2021?`
`Coca-Cola reported full-year 2021 revenue of $37.3 billion, a rise of 13% compared to $33.0 billion in 2020. This was primarily due to strong volume growth as well as improved pricing and mix.`	`How did Coca-Cola's revenue performance in 2021 measure against its previous year?`

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: 10
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: 10
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	Training Loss	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
0.8649	6	-	0.8783	0.8651	0.8713	0.8783	0.8439	0.8809
1.4414	10	0.7682	-	-	-	-	-	-
1.8739	13	-	0.8918	0.8827	0.8875	0.8918	0.8729	0.8933
2.8829	20	0.1465	0.8948	0.8896	0.8928	0.8961	0.8884	0.8953
3.8919	27	-	0.8930	0.8884	0.8917	0.8959	0.8900	0.8945
4.3243	30	0.0646	-	-	-	-	-	-
4.9009	34	-	0.8972	0.8883	0.8947	0.8955	0.8925	0.8970
5.7658	40	0.0397	-	-	-	-	-	-
5.9099	41	-	0.8964	0.8915	0.8953	0.8943	0.8926	0.8979
6.9189	48	-	0.8994	0.8930	0.8966	0.8955	0.8932	0.8974
7.2072	50	0.0319	-	-	-	-	-	-
7.9279	55	-	0.8998	0.8945	0.8967	0.8961	0.8943	0.8999
8.6486	60	0.0296	0.8994	0.8957	0.898	0.8959	0.8943	0.8999

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: 0.32.1
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}
}