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Add new SentenceTransformer model
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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:
    • json
  • 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

# Download from the 🤗 Hub
model = SentenceTransformer("aniket0898/bge-base-financial-matryoshka")
# Run inference
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)
# [3, 768]

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

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}
}