--- 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](https://www.SBERT.net) model finetuned from [nomic-ai/nomic-embed-text-v1](https://huggingface.co/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](https://huggingface.co/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 - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### 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: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python 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 * Dataset: `dim_768` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | 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 * Dataset: `dim_512` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | 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 * Dataset: `dim_256` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | 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 * Dataset: `dim_128` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | 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 * Dataset: `dim_64` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | 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 | | | * 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](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "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 ```bibtex @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 ```bibtex @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 ```bibtex @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} } ```