metadata

base_model: Alibaba-NLP/gte-large-en-v1.5
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:4275
  - loss:MatryoshkaLoss
  - loss:MultipleNegativesRankingLoss
widget:
  - source_sentence: >-
      The fundamental elements of Goldman Sachs’ robust risk culture include
      governance, risk identification, measurement, mitigation, culture and
      conduct, and infrastructure. They believe these elements work together to
      complement and reinforce each other to produce a comprehensive view of
      risk management.
    sentences:
      - >-
        What are the financial highlights for Bank of America Corp. in its
        latest fiscal year report?
      - What is Berkshire Hathaway's involvement in the energy sector?
      - >-
        What is Goldman Sach’s approach towards maintaining a robust risk
        culture?
  - source_sentence: >-
      HealthTech Inc.'s new drug for diabetes treatment, launched in 2021,
      contributed to approximately 30% of its total revenues for that year.
    sentences:
      - What is IBM's debt to equity ratio as of 2022?
      - >-
        In what way does HealthTech Inc's new drug contribute to its revenue
        generation?
      - What is the revenue breakdown of Alphabet for the year 2021?
  - source_sentence: >-
      The driving factor behind Tesla’s 2023 growth was the surge in demand for
      electric vehicles.
    sentences:
      - >-
        Why did McDonald's observe a decrease in overall revenue in 2023
        relative to 2022?
      - What key strategy did Walmart employ to boost its sales in 2016?
      - What was the driving factor behind Tesla's growth in 2023?
  - source_sentence: >-
      Pfizer is committed to ensuring that people around the world have access
      to its medical products. In line with this commitment, Pfizer has
      implemented programs such as donation drives, price reduction initiatives,
      and patient assistance programs to aid those in need. Furthermore, through
      partnerships with NGOs and governments, Pfizer strives to strengthen
      healthcare systems in underprivileged regions.
    sentences:
      - >-
        What is the strategy of Pfizer to improve access to medicines in
        underprivileged areas?
      - >-
        What percentage of growth in revenue did Adobe Systems report in June
        2020?
      - How is Citigroup differentiating itself among other banks?
  - source_sentence: >-
      JP Morgan reported total deposits of $2.6 trillion in the year ending
      December 31, 2023.
    sentences:
      - >-
        In the fiscal year 2023, what impact did the acquisition of T-Mobile
        bring to the revenue of AT&T?
      - >-
        What is the primary source of revenue for the software company,
        Microsoft?
      - What were JP Morgan's total deposits in 2023?
model-index:
  - name: gte-large-en-v1.5-financial-rag-matryoshka
    results:
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 1024
          type: dim_1024
        metrics:
          - type: cosine_accuracy@1
            value: 0.88
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.96
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.9866666666666667
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9955555555555555
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.88
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.32
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.19733333333333336
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09955555555555556
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.88
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.96
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.9866666666666667
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9955555555555555
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.9426916896167131
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.9251851851851851
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.925362962962963
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 768
          type: dim_768
        metrics:
          - type: cosine_accuracy@1
            value: 0.88
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.96
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.9866666666666667
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9911111111111112
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.88
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.32
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.19733333333333336
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09911111111111114
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.88
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.96
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.9866666666666667
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9911111111111112
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.940825047039427
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.924
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.9245274971941638
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 512
          type: dim_512
        metrics:
          - type: cosine_accuracy@1
            value: 0.8711111111111111
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.96
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.9866666666666667
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9911111111111112
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.8711111111111111
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.32
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.19733333333333336
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09911111111111114
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.8711111111111111
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.96
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.9866666666666667
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9911111111111112
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.938126332642602
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.9202962962962962
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.9207248677248678
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 256
          type: dim_256
        metrics:
          - type: cosine_accuracy@1
            value: 0.8755555555555555
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.96
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.9866666666666667
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9911111111111112
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.8755555555555555
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.32
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.19733333333333336
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09911111111111114
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.8755555555555555
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.96
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.9866666666666667
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9911111111111112
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.9395718726230007
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.9222962962962963
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.9227724867724867
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 128
          type: dim_128
        metrics:
          - type: cosine_accuracy@1
            value: 0.8666666666666667
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.9555555555555556
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.9866666666666667
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9911111111111112
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.8666666666666667
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.3185185185185185
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.19733333333333336
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09911111111111114
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.8666666666666667
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.9555555555555556
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.9866666666666667
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9911111111111112
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.9346269584282435
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.9157037037037037
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.9160403095943067
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 64
          type: dim_64
        metrics:
          - type: cosine_accuracy@1
            value: 0.8311111111111111
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.96
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.9733333333333334
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9911111111111112
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.8311111111111111
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.32
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.19466666666666665
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09911111111111114
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.8311111111111111
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.96
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.9733333333333334
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9911111111111112
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.9208110890988729
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.8971957671957672
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.8975242479721762
            name: Cosine Map@100

financial-rag-matryoshka

Model finetuned for financial use-cases from Alibaba-NLP/gte-large-en-v1.5. 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.

This model strives to excel tremendously in Financial Document Retrieval Tasks, concurrently preserving a maximum level of generalized performance.

Model Details

Model Description

Model Type: Sentence Transformer
Base model: Alibaba-NLP/gte-large-en-v1.5
Maximum Sequence Length: 8192 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': 8192, 'do_lower_case': False}) with Transformer model: NewModel 
  (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/gte-large-en-v1.5-financial-rag-matryoshka")
# Run inference
sentences = [
    'JP Morgan reported total deposits of $2.6 trillion in the year ending December 31, 2023.',
    "What were JP Morgan's total deposits in 2023?",
    'What is the primary source of revenue for the software company, Microsoft?',
]
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	0.88
cosine_accuracy@3	0.96
cosine_accuracy@5	0.9867
cosine_accuracy@10	0.9956
cosine_precision@1	0.88
cosine_precision@3	0.32
cosine_precision@5	0.1973
cosine_precision@10	0.0996
cosine_recall@1	0.88
cosine_recall@3	0.96
cosine_recall@5	0.9867
cosine_recall@10	0.9956
cosine_ndcg@10	0.9427
cosine_mrr@10	0.9252
cosine_map@100	0.9254

Information Retrieval

Dataset: dim_768
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.88
cosine_accuracy@3	0.96
cosine_accuracy@5	0.9867
cosine_accuracy@10	0.9911
cosine_precision@1	0.88
cosine_precision@3	0.32
cosine_precision@5	0.1973
cosine_precision@10	0.0991
cosine_recall@1	0.88
cosine_recall@3	0.96
cosine_recall@5	0.9867
cosine_recall@10	0.9911
cosine_ndcg@10	0.9408
cosine_mrr@10	0.924
cosine_map@100	0.9245

Information Retrieval

Dataset: dim_512
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.8711
cosine_accuracy@3	0.96
cosine_accuracy@5	0.9867
cosine_accuracy@10	0.9911
cosine_precision@1	0.8711
cosine_precision@3	0.32
cosine_precision@5	0.1973
cosine_precision@10	0.0991
cosine_recall@1	0.8711
cosine_recall@3	0.96
cosine_recall@5	0.9867
cosine_recall@10	0.9911
cosine_ndcg@10	0.9381
cosine_mrr@10	0.9203
cosine_map@100	0.9207

Information Retrieval

Dataset: dim_256
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.8756
cosine_accuracy@3	0.96
cosine_accuracy@5	0.9867
cosine_accuracy@10	0.9911
cosine_precision@1	0.8756
cosine_precision@3	0.32
cosine_precision@5	0.1973
cosine_precision@10	0.0991
cosine_recall@1	0.8756
cosine_recall@3	0.96
cosine_recall@5	0.9867
cosine_recall@10	0.9911
cosine_ndcg@10	0.9396
cosine_mrr@10	0.9223
cosine_map@100	0.9228

Information Retrieval

Dataset: dim_128
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.8667
cosine_accuracy@3	0.9556
cosine_accuracy@5	0.9867
cosine_accuracy@10	0.9911
cosine_precision@1	0.8667
cosine_precision@3	0.3185
cosine_precision@5	0.1973
cosine_precision@10	0.0991
cosine_recall@1	0.8667
cosine_recall@3	0.9556
cosine_recall@5	0.9867
cosine_recall@10	0.9911
cosine_ndcg@10	0.9346
cosine_mrr@10	0.9157
cosine_map@100	0.916

Information Retrieval

Dataset: dim_64
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.8311
cosine_accuracy@3	0.96
cosine_accuracy@5	0.9733
cosine_accuracy@10	0.9911
cosine_precision@1	0.8311
cosine_precision@3	0.32
cosine_precision@5	0.1947
cosine_precision@10	0.0991
cosine_recall@1	0.8311
cosine_recall@3	0.96
cosine_recall@5	0.9733
cosine_recall@10	0.9911
cosine_ndcg@10	0.9208
cosine_mrr@10	0.8972
cosine_map@100	0.8975

Training Details

Training Dataset

Unnamed Dataset

Size: 4,275 training samples
Columns: positive and anchor
Approximate statistics based on the first 1000 samples:
positive anchor
type string string
details
min: 15 tokens
mean: 44.74 tokens
max: 114 tokens

min: 9 tokens
mean: 18.12 tokens
max: 32 tokens

	positive	anchor
type	string	string
details	min: 15 tokens mean: 44.74 tokens max: 114 tokens	min: 9 tokens mean: 18.12 tokens max: 32 tokens

Samples:

positive	anchor
`At the end of fiscal year 2023, Exxon Mobil reported a debt-to-equity ratio of 0.32, implying that the company used more equity than debt in its capital structure.`	`What was the debt-to-equity ratio for Exxon Mobil at the end of fiscal year 2023?`
`Amazon Web Services (AWS) generated $12.7 billion in net sales in the fourth quarter of 2020, up 28% from the same period of the previous year. It accounted for about 10% of Amazon’s total net sales for the quarter.`	`How did Amazon's AWS segment perform in the fourth quarter of 2020?`
`JPMorgan Chase generates revenues by providing a wide range of banking and financial services. These include investment banking (M&As, advisory), consumer and community banking (home mortgages, auto loans), commercial banking, and asset and wealth management.`	`What are the key revenue sources for JPMorgan Chase?`

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
0.9552	8	-	0.9090	0.8848	0.8992	0.9052	0.8775	0.9030
1.1940	10	0.4749	-	-	-	-	-	-
1.9104	16	-	0.9170	0.9095	0.9109	0.9201	0.8961	0.9212
2.3881	20	0.0862	-	-	-	-	-	-
2.9851	25	-	0.9190	0.9071	0.9160	0.9278	0.8998	0.9234
3.5821	30	0.0315	-	-	-	-	-	-
3.9403	33	-	0.9183	0.9053	0.9122	0.9287	0.8998	0.9183
4.7761	40	0.0184	-	-	-	-	-	-
4.8955	41	-	0.9225	0.9125	0.9164	0.9260	0.8985	0.9220
5.9701	50	0.0135	0.9268	0.9132	0.9208	0.9257	0.8961	0.9271
6.9254	58	-	0.9254	0.9158	0.9202	0.9212	0.8938	0.9213
7.1642	60	0.0123	-	-	-	-	-	-
8.0	67	-	0.9253	0.916	0.9228	0.9207	0.8972	0.9243
8.3582	70	0.01	-	-	-	-	-	-
8.9552	75	-	0.9254	0.9160	0.9213	0.9207	0.9005	0.9245
9.5522	80	0.0088	0.9254	0.9160	0.9228	0.9207	0.8975	0.9245

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}
}