metadata

language:
  - en
license: apache-2.0
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:6300
  - loss:MatryoshkaLoss
  - loss:MultipleNegativesRankingLoss
base_model: BAAI/bge-base-en-v1.5
widget:
  - source_sentence: What are the components of Comcast's domestic distribution revenue?
    sentences:
      - >-
        Cash used in investing activities was $2.3 billion for fiscal 2023,
        compared to $2.1 billion for fiscal 2022.
      - >-
        Domestic distribution revenue primarily includes revenue generated from
        the distribution of our television networks operating predominantly in
        the United States to traditional and virtual multichannel video
        providers, and from NBC-affiliated and Telemundo-affiliated local
        broadcast television stations. Our revenue from distribution agreements
        is generally based on the number of subscribers receiving the
        programming on our television networks and a per subscriber fee.
        Distribution revenue also includes Peacock subscription fees.
      - >-
        In January 2023, Alphabet Inc. announced a reduction of its workforce,
        consequently recording employee severance and related charges of $2.1
        billion for the year.
  - source_sentence: >-
      What was the noncash pre-tax impairment charge recorded due to the
      disposal of Vrio's operations in 2021, and what are the main components
      contributing to this amount?
    sentences:
      - >-
        The cash equities rate per contract (per 100 shares) for NYSE increased
        by 6%, from $0.045 in 2022 to $0.048 in 2023.
      - >-
        In the second quarter of 2021, we classified the Vrio disposal group as
        held-for-sale and reported the disposal group at fair value less cost to
        sell, which resulted in a noncash, pre-tax impairment charge of $4,555,
        including approximately $2,100 related to accumulated foreign currency
        translation adjustments and $2,500 related to property, plant and
        equipment and intangible assets.
      - >-
        SECRET LAIR - our internet-based storefront where MAGIC: THE GATHERING
        fans can purchase exclusive and limited versions of cards.
  - source_sentence: What does the Corporate and Other segment include in its composition?
    sentences:
      - >-
        The segment consists of unallocated corporate expenses and
        administrative costs and activities not considered when evaluating
        segment performance as well as certain assets benefiting more than one
        segment. In addition, intersegment transactions are eliminated within
        the Corporate and Other segment.
      - >-
        Net cash provided by (used in) operating activities was recorded at
        $20,930 million for the reported year.
      - >-
        Forward-Looking Statements Certain statements in this report, other than
        purely historical information, including estimates, projections,
        statements relating to our business plans, objectives and expected
        operating results, and the assumptions upon which those statements are
        based, are “forward-looking statements” within the meaning of the
        Private Securities Litigation Reform Act of 1995, Section 27A of the
        Securities Act of 1933 and Section 21E of the Securities Exchange Act of
        1934.
  - source_sentence: >-
      What was the purchase price for the repurchase of Mobility preferred
      interests by AT&T in 2023?
    sentences:
      - >-
        Net revenue increased $1.5 billion, or 19%, to $9.6 billion in 2023 from
        $8.1 billion in 2022. On a constant dollar basis, net revenue increased
        20%. Comparable sales increased 13%, or 14% on a constant dollar basis.
        The increase in net revenue was primarily due to increased Americas net
        revenue. China Mainland and Rest of World net revenue also increased.
      - >-
        Google Services includes products and services such as ads, Android,
        Chrome, devices, Google Maps, Google Play, Search, and YouTube. Google
        Services generates revenues primarily from advertising; fees received
        for consumer subscription-based products such. as YouTube TV, YouTube
        Music and Premium, and NFL Sunday Ticket; and the sale of apps and
        in-app purchases and devices.
      - >-
        In April 2023, we also accepted the December 2022 put option notice from
        the AT&T pension trust and repurchased the remaining 213 million
        Mobility preferred interests for a purchase price, including accrued and
        unpaid distributions, of $5,414.
  - source_sentence: >-
      What is the maximum leverage ratio allowed before default under the
      company's credit facility?
    sentences:
      - >-
        If the company's leverage ratio exceeds 3.50 to 1, it would be in
        default of its revolving credit facility, impairing its ability to
        borrow under the facility.
      - >-
        Research and Development Because the industries in which the Company
        competes are characterized by rapid technological advances, the
        Company’s ability to compete successfully depends heavily upon its
        ability to ensure a continual and timely flow of competitive products,
        services and technologies to the marketplace.
      - >-
        Visa is focused on extending, enhancing and investing in VisaNet, their
        proprietary advanced transaction processing network, to offer a single
        connection point for facilitating payment transactions to multiple
        endpoints through various form factors.
datasets:
  - philschmid/finanical-rag-embedding-dataset
pipeline_tag: sentence-similarity
library_name: sentence-transformers
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
model-index:
  - name: BGE base Financial Matryoshka
    results:
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 768
          type: dim_768
        metrics:
          - type: cosine_accuracy@1
            value: 0.6771428571428572
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8371428571428572
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8685714285714285
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9185714285714286
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6771428571428572
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.27904761904761904
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17371428571428568
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09185714285714283
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6771428571428572
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8371428571428572
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8685714285714285
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9185714285714286
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.800782444183487
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.762721088435374
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7655884035994069
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 512
          type: dim_512
        metrics:
          - type: cosine_accuracy@1
            value: 0.6828571428571428
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8371428571428572
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8757142857142857
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.92
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6828571428571428
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.27904761904761904
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17514285714285713
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09199999999999998
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6828571428571428
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8371428571428572
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8757142857142857
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.92
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.80444342170685
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7670583900226756
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7699510134898729
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 256
          type: dim_256
        metrics:
          - type: cosine_accuracy@1
            value: 0.6757142857142857
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8228571428571428
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8642857142857143
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9185714285714286
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6757142857142857
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2742857142857143
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17285714285714285
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09185714285714283
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6757142857142857
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8228571428571428
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8642857142857143
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9185714285714286
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7984105242762846
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7599024943310656
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7625291382895937
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 128
          type: dim_128
        metrics:
          - type: cosine_accuracy@1
            value: 0.6714285714285714
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8114285714285714
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8485714285714285
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9014285714285715
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6714285714285714
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2704761904761904
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.16971428571428568
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09014285714285714
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6714285714285714
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8114285714285714
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8485714285714285
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9014285714285715
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7872870842648211
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7507193877551018
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7542921487122674
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 64
          type: dim_64
        metrics:
          - type: cosine_accuracy@1
            value: 0.6242857142857143
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.7842857142857143
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.82
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8828571428571429
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6242857142857143
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.26142857142857145
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.16399999999999998
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08828571428571429
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6242857142857143
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.7842857142857143
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.82
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8828571428571429
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7546358861091382
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7135277777777775
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7174129354945035
            name: Cosine Map@100

BGE base Financial Matryoshka

This is a sentence-transformers model finetuned from BAAI/bge-base-en-v1.5 on the finanical-rag-embedding-dataset dataset. 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 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- finanical-rag-embedding-dataset
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("bnkc123/bge-base-financial-matryoshka")
# Run inference
sentences = [
    "What is the maximum leverage ratio allowed before default under the company's credit facility?",
    "If the company's leverage ratio exceeds 3.50 to 1, it would be in default of its revolving credit facility, impairing its ability to borrow under the facility.",
    'Research and Development Because the industries in which the Company competes are characterized by rapid technological advances, the Company’s ability to compete successfully depends heavily upon its ability to ensure a continual and timely flow of competitive products, services and technologies to the marketplace.',
]
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 with these parameters:
```
{
    "truncate_dim": 768
}
```

Metric	Value
cosine_accuracy@1	0.6771
cosine_accuracy@3	0.8371
cosine_accuracy@5	0.8686
cosine_accuracy@10	0.9186
cosine_precision@1	0.6771
cosine_precision@3	0.279
cosine_precision@5	0.1737
cosine_precision@10	0.0919
cosine_recall@1	0.6771
cosine_recall@3	0.8371
cosine_recall@5	0.8686
cosine_recall@10	0.9186
cosine_ndcg@10	0.8008
cosine_mrr@10	0.7627
cosine_map@100	0.7656

Information Retrieval

Dataset: dim_512
Evaluated with InformationRetrievalEvaluator with these parameters:
```
{
    "truncate_dim": 512
}
```

Metric	Value
cosine_accuracy@1	0.6829
cosine_accuracy@3	0.8371
cosine_accuracy@5	0.8757
cosine_accuracy@10	0.92
cosine_precision@1	0.6829
cosine_precision@3	0.279
cosine_precision@5	0.1751
cosine_precision@10	0.092
cosine_recall@1	0.6829
cosine_recall@3	0.8371
cosine_recall@5	0.8757
cosine_recall@10	0.92
cosine_ndcg@10	0.8044
cosine_mrr@10	0.7671
cosine_map@100	0.77

Information Retrieval

Dataset: dim_256
Evaluated with InformationRetrievalEvaluator with these parameters:
```
{
    "truncate_dim": 256
}
```

Metric	Value
cosine_accuracy@1	0.6757
cosine_accuracy@3	0.8229
cosine_accuracy@5	0.8643
cosine_accuracy@10	0.9186
cosine_precision@1	0.6757
cosine_precision@3	0.2743
cosine_precision@5	0.1729
cosine_precision@10	0.0919
cosine_recall@1	0.6757
cosine_recall@3	0.8229
cosine_recall@5	0.8643
cosine_recall@10	0.9186
cosine_ndcg@10	0.7984
cosine_mrr@10	0.7599
cosine_map@100	0.7625

Information Retrieval

Dataset: dim_128
Evaluated with InformationRetrievalEvaluator with these parameters:
```
{
    "truncate_dim": 128
}
```

Metric	Value
cosine_accuracy@1	0.6714
cosine_accuracy@3	0.8114
cosine_accuracy@5	0.8486
cosine_accuracy@10	0.9014
cosine_precision@1	0.6714
cosine_precision@3	0.2705
cosine_precision@5	0.1697
cosine_precision@10	0.0901
cosine_recall@1	0.6714
cosine_recall@3	0.8114
cosine_recall@5	0.8486
cosine_recall@10	0.9014
cosine_ndcg@10	0.7873
cosine_mrr@10	0.7507
cosine_map@100	0.7543

Information Retrieval

Dataset: dim_64
Evaluated with InformationRetrievalEvaluator with these parameters:
```
{
    "truncate_dim": 64
}
```

Metric	Value
cosine_accuracy@1	0.6243
cosine_accuracy@3	0.7843
cosine_accuracy@5	0.82
cosine_accuracy@10	0.8829
cosine_precision@1	0.6243
cosine_precision@3	0.2614
cosine_precision@5	0.164
cosine_precision@10	0.0883
cosine_recall@1	0.6243
cosine_recall@3	0.7843
cosine_recall@5	0.82
cosine_recall@10	0.8829
cosine_ndcg@10	0.7546
cosine_mrr@10	0.7135
cosine_map@100	0.7174

Training Details

Training Dataset

finanical-rag-embedding-dataset

Dataset: finanical-rag-embedding-dataset at e0b1781
Size: 6,300 training samples
Columns: anchor and positive
Approximate statistics based on the first 1000 samples:
anchor positive
type string string
details
min: 7 tokens
mean: 20.5 tokens
max: 43 tokens

min: 9 tokens
mean: 46.09 tokens
max: 512 tokens

	anchor	positive
type	string	string
details	min: 7 tokens mean: 20.5 tokens max: 43 tokens	min: 9 tokens mean: 46.09 tokens max: 512 tokens

Samples:

anchor	positive
`What was the amount of premiums written by Berkshire Hathaway's Insurance Underwriting in 2023, and how did it compare to the previous year?`	`Premiums written increased $3.5 billion (24.1%) in 2023 compared to 2022. The increase was primarily due to RSUI and CapSpecialty ($2.1 billion), as well as comparative increases from BHSI and BH Direct, and to a lesser extent the other businesses. Premiums written`
`What types of transportation equipment does XTRA Corporation manage in its fleet?`	`XTRA manages a diverse fleet of approximately 90,000 units located at 47 facilities throughout the U.S. The fleet includes over-the-road and storage trailers, chassis, temperature-controlled vans and flatbed trailers.`
`What seasonal trends affect the company's sales volumes?`	`Sales volumes for the company are highest in the second fiscal quarter due to seasonal influences, particularly during the spring season in the regions it serves.`

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: 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
push_to_hub: True
hub_model_id: bnkc123/bge-base-financial-matryoshka
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
torch_empty_cache_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}
tp_size: 0
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: True
resume_from_checkpoint: None
hub_model_id: bnkc123/bge-base-financial-matryoshka
hub_strategy: every_save
hub_private_repo: None
hub_always_push: False
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
include_for_metrics: []
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
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
use_liger_kernel: False
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: None
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	dim_768_cosine_ndcg@10	dim_512_cosine_ndcg@10	dim_256_cosine_ndcg@10	dim_128_cosine_ndcg@10	dim_64_cosine_ndcg@10
0.8122	10	25.483	-	-	-	-	-
1.0	13	-	0.7890	0.7887	0.7815	0.7647	0.7280
1.5685	20	9.1323	-	-	-	-	-
2.0	26	-	0.7952	0.7982	0.7933	0.7801	0.7477
2.3249	30	6.7535	-	-	-	-	-
3.0	39	-	0.8019	0.8048	0.7989	0.7865	0.7547
3.0812	40	6.5646	-	-	-	-	-
3.731	48	-	0.8008	0.8044	0.7984	0.7873	0.7546

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.12.6
Sentence Transformers: 4.1.0
Transformers: 4.51.3
PyTorch: 2.7.0+cu126
Accelerate: 1.6.0
Datasets: 3.5.1
Tokenizers: 0.21.1

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}
}