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: nomic-ai/nomic-embed-text-v1
widget:
- source_sentence: What amount of senior notes was repaid during fiscal 2022?
sentences:
- >-
The following table sets forth the breakdown of revenue by geography,
determined based on the location of the Host’s listing (in millions): |
Year Ended December 31, | 2021 | 2022 | 2023 United States | $ | 2,996 |
| $ | 3,890 | $ | 4,290 International(1) | 2,996 | | 4,509 | | 5,627
Total revenue | $ | 5,992 | | $ | 8,399 | $ | 9,917
- During fiscal 2022, $2.25 billion of senior notes was repaid.
- >-
Several factors are considered in developing the estimate for the
long-term expected rate of return on plan assets. For the defined
benefit retirement plans, these factors include historical rates of
return of broad equity and bond indices and projected long-term rates of
return obtained from pension investment consultants. The expected
long-term rates of return for plan assets are 8 - 9% for equities and 3
- 5% for bonds. For other retiree benefit plans, the expected long-term
rate of return reflects that the assets are comprised primarily of
Company stock. The expected rate of return on Company stock is based on
the long-term projected return of 8.5% and reflects the historical
pattern of returns.
- source_sentence: What does GameStop Corp. offer to its customers?
sentences:
- >-
State fraud and abuse laws could lead to criminal, civil, or
administrative consequences, including licensure loss, exclusion from
healthcare programs, and significant negative effects on the violating
entity's business operations and financial health if the laws are
violated.
- >-
GameStop Corp. offers games and entertainment products through its
stores and ecommerce platforms.
- >-
Stribild is an oral formulation dosed once a day for the treatment of
HIV-1 infection in certain patients.
- source_sentence: >-
How might a 10% change in the obsolescence reserve percentage impact net
earnings?
sentences:
- >-
A 10% change in our obsolescence reserve percentage at January 28, 2023
would have affected net earnings by approximately $2.5 million in fiscal
2022.
- >-
The information required by Item 3 on Legal Proceedings is provided by
referencing Note 19 of the Notes to Consolidated Financial Statements in
Item 8.
- >-
ured notes for an aggregate principal amount of $18.50 billion. These
notes were issued in multiple series, which mature from 2027 through
2063.
- source_sentence: >-
What are the SEC's regulations for security-based swap dealers like
Goldman Sachs' subsidiaries?
sentences:
- >-
The increase in other income, net was primarily due to an increase in
interest income as a result of higher cash balances and higher interest
rates.
- >-
Through our Stubs loyalty programs, we have developed a consumer
database of approximately 32 million households, representing
approximately 64 million individuals.
- >-
SEC rules govern the registration and regulation of security-based swap
dealers. Security-based swaps are defined as swaps on single securities,
single loans or narrow-based baskets or indices of securities. The SEC
has adopted a number of rules for security-based swap dealers, including
(i) capital, margin and segregation requirements; (ii) record-keeping,
financial reporting and notification requirements; (iii) business
conduct standards; (iv) regulatory and public trade reporting; and (v)
the application of risk mitigation techniques to uncleared portfolios of
security-based swaps.
- source_sentence: >-
How is the information about legal proceedings organized in the financial
documents according to the provided context?
sentences:
- >-
The information about legal proceedings is organized under Part II, Item
8 in the section titled 'Financial Statements and Supplementary Data –
Note 14'.
- >-
We have a match-funding policy that addresses the interest rate risk by
aligning the interest rate profile (fixed or floating rate and duration)
of our debt portfolio with the interest rate profile of our finance
receivable portfolio within a predetermined range on an ongoing basis.
In connection with that policy, we use interest rate derivative
instruments to modify the debt structure to match assets within the
finance receivable portfolio.
- >-
Achieved adjusted FIFO operating profit of $5.1 billion, which
represents an 18% increase compared to 2021.
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: Nomic Financial Matryoshka
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 768
type: dim_768
metrics:
- type: cosine_accuracy@1
value: 0.7457142857142857
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8614285714285714
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8957142857142857
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.93
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7457142857142857
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.28714285714285714
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1791428571428571
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09299999999999999
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7457142857142857
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8614285714285714
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8957142857142857
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.93
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8398915226132163
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.8107896825396824
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.8136819482601757
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.7357142857142858
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8514285714285714
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8914285714285715
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.93
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7357142857142858
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2838095238095238
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17828571428571427
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09299999999999999
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7357142857142858
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8514285714285714
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8914285714285715
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.93
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8352581932886503
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.8047103174603173
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.8075415578285141
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.7285714285714285
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8614285714285714
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8857142857142857
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9271428571428572
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7285714285714285
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.28714285714285714
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17714285714285713
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09271428571428571
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7285714285714285
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8614285714285714
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8857142857142857
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9271428571428572
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8319809230146766
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.8011235827664398
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.8040552556779361
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.7128571428571429
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8328571428571429
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8671428571428571
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9142857142857143
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7128571428571429
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2776190476190476
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1734285714285714
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09142857142857141
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7128571428571429
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8328571428571429
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8671428571428571
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9142857142857143
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8145627876253931
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7825572562358278
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7859620809117356
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.6642857142857143
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8042857142857143
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8457142857142858
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9028571428571428
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6642857142857143
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2680952380952381
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.16914285714285712
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09028571428571427
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6642857142857143
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8042857142857143
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8457142857142858
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9028571428571428
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7821373629924483
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7436649659863942
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7468498882402747
name: Cosine Map@100
Nomic Financial Matryoshka
This is a sentence-transformers model finetuned from nomic-ai/nomic-embed-text-v1 on the json 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: nomic-ai/nomic-embed-text-v1
- Maximum Sequence Length: 8192 tokens
- Output Dimensionality: 768 tokens
- Similarity Function: Cosine Similarity
- Training Dataset:
- Language: en
- License: apache-2.0
Model Sources
Full Model Architecture
SentenceTransformer(
(0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: NomicBertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, '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
model = SentenceTransformer("aniket0898/bge-base-financial-matryoshka")
sentences = [
'How is the information about legal proceedings organized in the financial documents according to the provided context?',
"The information about legal proceedings is organized under Part II, Item 8 in the section titled 'Financial Statements and Supplementary Data – Note 14'.",
'We have a match-funding policy that addresses the interest rate risk by aligning the interest rate profile (fixed or floating rate and duration) of our debt portfolio with the interest rate profile of our finance receivable portfolio within a predetermined range on an ongoing basis. In connection with that policy, we use interest rate derivative instruments to modify the debt structure to match assets within the finance receivable portfolio.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
Evaluation
Metrics
Information Retrieval
Metric |
Value |
cosine_accuracy@1 |
0.7457 |
cosine_accuracy@3 |
0.8614 |
cosine_accuracy@5 |
0.8957 |
cosine_accuracy@10 |
0.93 |
cosine_precision@1 |
0.7457 |
cosine_precision@3 |
0.2871 |
cosine_precision@5 |
0.1791 |
cosine_precision@10 |
0.093 |
cosine_recall@1 |
0.7457 |
cosine_recall@3 |
0.8614 |
cosine_recall@5 |
0.8957 |
cosine_recall@10 |
0.93 |
cosine_ndcg@10 |
0.8399 |
cosine_mrr@10 |
0.8108 |
cosine_map@100 |
0.8137 |
Information Retrieval
Metric |
Value |
cosine_accuracy@1 |
0.7357 |
cosine_accuracy@3 |
0.8514 |
cosine_accuracy@5 |
0.8914 |
cosine_accuracy@10 |
0.93 |
cosine_precision@1 |
0.7357 |
cosine_precision@3 |
0.2838 |
cosine_precision@5 |
0.1783 |
cosine_precision@10 |
0.093 |
cosine_recall@1 |
0.7357 |
cosine_recall@3 |
0.8514 |
cosine_recall@5 |
0.8914 |
cosine_recall@10 |
0.93 |
cosine_ndcg@10 |
0.8353 |
cosine_mrr@10 |
0.8047 |
cosine_map@100 |
0.8075 |
Information Retrieval
Metric |
Value |
cosine_accuracy@1 |
0.7286 |
cosine_accuracy@3 |
0.8614 |
cosine_accuracy@5 |
0.8857 |
cosine_accuracy@10 |
0.9271 |
cosine_precision@1 |
0.7286 |
cosine_precision@3 |
0.2871 |
cosine_precision@5 |
0.1771 |
cosine_precision@10 |
0.0927 |
cosine_recall@1 |
0.7286 |
cosine_recall@3 |
0.8614 |
cosine_recall@5 |
0.8857 |
cosine_recall@10 |
0.9271 |
cosine_ndcg@10 |
0.832 |
cosine_mrr@10 |
0.8011 |
cosine_map@100 |
0.8041 |
Information Retrieval
Metric |
Value |
cosine_accuracy@1 |
0.7129 |
cosine_accuracy@3 |
0.8329 |
cosine_accuracy@5 |
0.8671 |
cosine_accuracy@10 |
0.9143 |
cosine_precision@1 |
0.7129 |
cosine_precision@3 |
0.2776 |
cosine_precision@5 |
0.1734 |
cosine_precision@10 |
0.0914 |
cosine_recall@1 |
0.7129 |
cosine_recall@3 |
0.8329 |
cosine_recall@5 |
0.8671 |
cosine_recall@10 |
0.9143 |
cosine_ndcg@10 |
0.8146 |
cosine_mrr@10 |
0.7826 |
cosine_map@100 |
0.786 |
Information Retrieval
Metric |
Value |
cosine_accuracy@1 |
0.6643 |
cosine_accuracy@3 |
0.8043 |
cosine_accuracy@5 |
0.8457 |
cosine_accuracy@10 |
0.9029 |
cosine_precision@1 |
0.6643 |
cosine_precision@3 |
0.2681 |
cosine_precision@5 |
0.1691 |
cosine_precision@10 |
0.0903 |
cosine_recall@1 |
0.6643 |
cosine_recall@3 |
0.8043 |
cosine_recall@5 |
0.8457 |
cosine_recall@10 |
0.9029 |
cosine_ndcg@10 |
0.7821 |
cosine_mrr@10 |
0.7437 |
cosine_map@100 |
0.7468 |
Training Details
Training Dataset
json
- Dataset: json
- Size: 6,300 training samples
- Columns:
anchor
and positive
- Approximate statistics based on the first 1000 samples:
|
anchor |
positive |
type |
string |
string |
details |
- min: 2 tokens
- mean: 20.47 tokens
- max: 40 tokens
|
- min: 9 tokens
- mean: 45.09 tokens
- max: 272 tokens
|
- Samples:
anchor |
positive |
What was the stored value of cards and loyalty program balances at the end of fiscal year 2022? |
Stored value cards and loyalty program at October 2, 2022 showed a balance of approximately $1.503 billion. |
What transformation is planned for Le Jardin located at The Londoner Macao? |
Le Jardin, located on the southern flank of The Londoner Macao, is to undergo a transformation into a distinctive garden-themed attraction spanning approximately 50,000 square meters. |
What are the key terms of the new Labor Agreement ratified by the UAW in 2023? |
The key terms and provisions of the Labor Agreement are: General wage increases of 11% upon ratification in 2023, 3% in September each of 2024, 2025 and 2026, and 5% in September 2027; Consolidation of applicable wage classifications for in-progression, temporary and other employees – with employees reaching the top classification rate upon the completion of 156 weeks of active service; The re-establishment of a cost-of-living allowance; Lump sum ratification bonus payments of $5,000 paid to eligible employees in the three months ended December 31, 2023; For members currently employed and enrolled in the Employees’ Pension Plan, an increase of $5.00 to the monthly basic benefit for past and future service provided; A 3.6% increase in company contributions to eligible employees' defined contribution retirement accounts; and Annual contribution of $500 to eligible retirees or surviving spouses. |
- 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
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 |
Training Loss |
dim_768_cosine_map@100 |
dim_512_cosine_map@100 |
dim_256_cosine_map@100 |
dim_128_cosine_map@100 |
dim_64_cosine_map@100 |
0.8122 |
10 |
0.7331 |
- |
- |
- |
- |
- |
0.9746 |
12 |
- |
0.7871 |
0.7796 |
0.7747 |
0.7546 |
0.7214 |
1.6244 |
20 |
0.2506 |
- |
- |
- |
- |
- |
1.9492 |
24 |
- |
0.8021 |
0.7990 |
0.7869 |
0.7691 |
0.7371 |
2.4365 |
30 |
0.1029 |
- |
- |
- |
- |
- |
2.9239 |
36 |
- |
0.8030 |
0.8017 |
0.7926 |
0.7760 |
0.7402 |
3.2487 |
40 |
0.054 |
- |
- |
- |
- |
- |
3.8985 |
48 |
- |
0.8055 |
0.799 |
0.7924 |
0.7754 |
0.7383 |
0.8122 |
10 |
0.0397 |
- |
- |
- |
- |
- |
0.9746 |
12 |
- |
0.8109 |
0.7983 |
0.7974 |
0.7795 |
0.7373 |
1.6244 |
20 |
0.0301 |
- |
- |
- |
- |
- |
1.9492 |
24 |
- |
0.8115 |
0.8049 |
0.8026 |
0.7839 |
0.7486 |
2.4365 |
30 |
0.0236 |
- |
- |
- |
- |
- |
2.9239 |
36 |
- |
0.8138 |
0.8082 |
0.8045 |
0.7858 |
0.7470 |
3.2487 |
40 |
0.0131 |
- |
- |
- |
- |
- |
3.8985 |
48 |
- |
0.8137 |
0.8075 |
0.8041 |
0.786 |
0.7468 |
- The bold row denotes the saved checkpoint.
Framework Versions
- Python: 3.8.10
- Sentence Transformers: 3.2.1
- Transformers: 4.41.2
- PyTorch: 2.1.2+cu121
- Accelerate: 1.0.1
- 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}
}