--- base_model: mixedbread-ai/mxbai-embed-large-v1 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:3550 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss widget: - source_sentence: At the end of 2023, Alphabet Inc. reported total debts amounting to $14.2 billion, compared to $10.9 billion at the end of 2022. sentences: - What was the total debt of Alphabet Inc. as of the end of 2023? - What was ExxonMobil's contribution to the energy production in the Energy sector during 2020? - Describe Amazon's revenue growth in 2023? - source_sentence: In 2022, Pfizer strategically managed cash flow from investments by utilizing operating cash flow, issuing new debt, and through the monetization of certain non-core assets. This approach of diversifying the source of funding for investments was done to minimize risk and uncertainty in economic conditions. sentences: - How much capital expenditure did AUX Energy invest in renewable energy projects in 2022? - What effect did the 2023 market downturn have on Amazon's retail and cloud segments? - How did Pfizer manage cash flows from investments in 2022? - source_sentence: The primary revenue generators for JPMorgan Chase for the fiscal year 2023 were the Corporate & Investment Bank (CIB) and the Asset & Wealth Management (AWM) sectors. The CIB sector benefited from a rise in merger and acquisition activities, while AWM saw large net inflows. sentences: - What is General Electric's strategic priority for its Aviation business segment? - Which sectors contributed the most to the revenue of JPMorgan Chase for FY 2023? - What is the principal activity of Apple Inc.? - source_sentence: For the fiscal year 2023, Microsoft's Intelligent Cloud segment generated revenues of $58 billion, demonstrating solid growth fueled by strong demand for cloud services and server products. sentences: - What is the primary strategy of McDonald’s to drive growth in the future? - What impact did the increase in gold prices have on Newmont Corporation's revenue in 2023? - What was the revenue generated by Microsoft's Intelligent Cloud segment for fiscal year 2023? - source_sentence: Microsoft, in their latest press release, revealed that they are anticipating a revenue growth of approximately 12% for the fiscal year ending in 2024. sentences: - What is Microsoft's projected revenue growth for fiscal year 2024? - What is the fair value of equity method investments of Microsoft in the fiscal year 2025? - What was the impact of COVID-19 on Zoom's profits? model-index: - name: mxbai-embed-large-v1-financial-rag-matryoshka results: - task: type: information-retrieval name: Information Retrieval dataset: name: dim 1024 type: dim_1024 metrics: - type: cosine_accuracy@1 value: 0.8455696202531645 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.9392405063291139 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9670886075949368 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9898734177215189 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.8455696202531645 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.31308016877637135 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.19341772151898737 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.0989873417721519 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.8455696202531645 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.9392405063291139 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.9670886075949368 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9898734177215189 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.9212281141643793 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.898873819570022 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.8993853803492357 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 768 type: dim_768 metrics: - type: cosine_accuracy@1 value: 0.8455696202531645 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.9392405063291139 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9670886075949368 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9898734177215189 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.8455696202531645 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.3130801687763713 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1934177215189873 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.0989873417721519 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.8455696202531645 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.9392405063291139 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.9670886075949368 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9898734177215189 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.9217284365901642 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.8994826200522402 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.8999494134557425 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 512 type: dim_512 metrics: - type: cosine_accuracy@1 value: 0.8405063291139241 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.9367088607594937 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9645569620253165 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9898734177215189 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.8405063291139241 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.31223628691983124 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.19291139240506328 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.0989873417721519 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.8405063291139241 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.9367088607594937 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.9645569620253165 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9898734177215189 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.9186273598847787 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.8954631303998389 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.8958871142668611 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 256 type: dim_256 metrics: - type: cosine_accuracy@1 value: 0.8455696202531645 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.9392405063291139 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9645569620253165 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9898734177215189 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.8455696202531645 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.3130801687763713 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.19291139240506328 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.0989873417721519 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.8455696202531645 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.9392405063291139 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.9645569620253165 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9898734177215189 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.9201161947922436 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.8975597749648381 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.8979721416614026 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 128 type: dim_128 metrics: - type: cosine_accuracy@1 value: 0.8405063291139241 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.9417721518987342 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9645569620253165 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9848101265822785 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.8405063291139241 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.3139240506329114 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.19291139240506328 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09848101265822784 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.8405063291139241 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.9417721518987342 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.9645569620253165 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9848101265822785 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.9170562815583235 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.8948693992364878 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.8957325656059834 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 64 type: dim_64 metrics: - type: cosine_accuracy@1 value: 0.8405063291139241 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.9316455696202531 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9569620253164557 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9822784810126582 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.8405063291139241 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.3105485232067511 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.19139240506329114 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09822784810126582 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.8405063291139241 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.9316455696202531 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.9569620253164557 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9822784810126582 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.9153318022971121 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.8934589109905566 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.8943102728098851 name: Cosine Map@100 --- # mxbai-embed-large-v1-financial-rag-matryoshka This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [mixedbread-ai/mxbai-embed-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1). 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. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [mixedbread-ai/mxbai-embed-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 1024 tokens - **Similarity Function:** Cosine Similarity - **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': 512, 'do_lower_case': False}) with Transformer model: BertModel (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: ```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("rbhatia46/mxbai-embed-large-v1-financial-rag-matryoshka") # Run inference sentences = [ 'Microsoft, in their latest press release, revealed that they are anticipating a revenue growth of approximately 12% for the fiscal year ending in 2024.', "What is Microsoft's projected revenue growth for fiscal year 2024?", "What was the impact of COVID-19 on Zoom's profits?", ] 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](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.8456 | | cosine_accuracy@3 | 0.9392 | | cosine_accuracy@5 | 0.9671 | | cosine_accuracy@10 | 0.9899 | | cosine_precision@1 | 0.8456 | | cosine_precision@3 | 0.3131 | | cosine_precision@5 | 0.1934 | | cosine_precision@10 | 0.099 | | cosine_recall@1 | 0.8456 | | cosine_recall@3 | 0.9392 | | cosine_recall@5 | 0.9671 | | cosine_recall@10 | 0.9899 | | cosine_ndcg@10 | 0.9212 | | cosine_mrr@10 | 0.8989 | | **cosine_map@100** | **0.8994** | #### 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 | 0.8456 | | cosine_accuracy@3 | 0.9392 | | cosine_accuracy@5 | 0.9671 | | cosine_accuracy@10 | 0.9899 | | cosine_precision@1 | 0.8456 | | cosine_precision@3 | 0.3131 | | cosine_precision@5 | 0.1934 | | cosine_precision@10 | 0.099 | | cosine_recall@1 | 0.8456 | | cosine_recall@3 | 0.9392 | | cosine_recall@5 | 0.9671 | | cosine_recall@10 | 0.9899 | | cosine_ndcg@10 | 0.9217 | | cosine_mrr@10 | 0.8995 | | **cosine_map@100** | **0.8999** | #### 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 | 0.8405 | | cosine_accuracy@3 | 0.9367 | | cosine_accuracy@5 | 0.9646 | | cosine_accuracy@10 | 0.9899 | | cosine_precision@1 | 0.8405 | | cosine_precision@3 | 0.3122 | | cosine_precision@5 | 0.1929 | | cosine_precision@10 | 0.099 | | cosine_recall@1 | 0.8405 | | cosine_recall@3 | 0.9367 | | cosine_recall@5 | 0.9646 | | cosine_recall@10 | 0.9899 | | cosine_ndcg@10 | 0.9186 | | cosine_mrr@10 | 0.8955 | | **cosine_map@100** | **0.8959** | #### 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 | 0.8456 | | cosine_accuracy@3 | 0.9392 | | cosine_accuracy@5 | 0.9646 | | cosine_accuracy@10 | 0.9899 | | cosine_precision@1 | 0.8456 | | cosine_precision@3 | 0.3131 | | cosine_precision@5 | 0.1929 | | cosine_precision@10 | 0.099 | | cosine_recall@1 | 0.8456 | | cosine_recall@3 | 0.9392 | | cosine_recall@5 | 0.9646 | | cosine_recall@10 | 0.9899 | | cosine_ndcg@10 | 0.9201 | | cosine_mrr@10 | 0.8976 | | **cosine_map@100** | **0.898** | #### 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 | 0.8405 | | cosine_accuracy@3 | 0.9418 | | cosine_accuracy@5 | 0.9646 | | cosine_accuracy@10 | 0.9848 | | cosine_precision@1 | 0.8405 | | cosine_precision@3 | 0.3139 | | cosine_precision@5 | 0.1929 | | cosine_precision@10 | 0.0985 | | cosine_recall@1 | 0.8405 | | cosine_recall@3 | 0.9418 | | cosine_recall@5 | 0.9646 | | cosine_recall@10 | 0.9848 | | cosine_ndcg@10 | 0.9171 | | cosine_mrr@10 | 0.8949 | | **cosine_map@100** | **0.8957** | #### 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 | 0.8405 | | cosine_accuracy@3 | 0.9316 | | cosine_accuracy@5 | 0.957 | | cosine_accuracy@10 | 0.9823 | | cosine_precision@1 | 0.8405 | | cosine_precision@3 | 0.3105 | | cosine_precision@5 | 0.1914 | | cosine_precision@10 | 0.0982 | | cosine_recall@1 | 0.8405 | | cosine_recall@3 | 0.9316 | | cosine_recall@5 | 0.957 | | cosine_recall@10 | 0.9823 | | cosine_ndcg@10 | 0.9153 | | cosine_mrr@10 | 0.8935 | | **cosine_map@100** | **0.8943** | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 3,550 training samples * Columns: positive and anchor * Approximate statistics based on the first 1000 samples: | | positive | anchor | |:--------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | positive | anchor | |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------| | The total revenue for Google as of 2021 stands at approximately $181 billion, primarily driven by the performance of its advertising and cloud segments, hailing from the Information Technology sector. | What is the total revenue of Google as of 2021? | | In Q4 2021, Amazon.com Inc. reported a significant increase in net income, reaching $14.3 billion, due to the surge in online shopping during the pandemic. | What was the Net Income of Amazon.com Inc. in Q4 2021? | | Coca-Cola reported full-year 2021 revenue of $37.3 billion, a rise of 13% compared to $33.0 billion in 2020. This was primarily due to strong volume growth as well as improved pricing and mix. | How did Coca-Cola's revenue performance in 2021 measure against its previous year? | * Loss: [MatryoshkaLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "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 | |:----------:|:------:|:-------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:| | 0.8649 | 6 | - | 0.8783 | 0.8651 | 0.8713 | 0.8783 | 0.8439 | 0.8809 | | 1.4414 | 10 | 0.7682 | - | - | - | - | - | - | | 1.8739 | 13 | - | 0.8918 | 0.8827 | 0.8875 | 0.8918 | 0.8729 | 0.8933 | | 2.8829 | 20 | 0.1465 | 0.8948 | 0.8896 | 0.8928 | 0.8961 | 0.8884 | 0.8953 | | 3.8919 | 27 | - | 0.8930 | 0.8884 | 0.8917 | 0.8959 | 0.8900 | 0.8945 | | 4.3243 | 30 | 0.0646 | - | - | - | - | - | - | | 4.9009 | 34 | - | 0.8972 | 0.8883 | 0.8947 | 0.8955 | 0.8925 | 0.8970 | | 5.7658 | 40 | 0.0397 | - | - | - | - | - | - | | 5.9099 | 41 | - | 0.8964 | 0.8915 | 0.8953 | 0.8943 | 0.8926 | 0.8979 | | 6.9189 | 48 | - | 0.8994 | 0.8930 | 0.8966 | 0.8955 | 0.8932 | 0.8974 | | 7.2072 | 50 | 0.0319 | - | - | - | - | - | - | | 7.9279 | 55 | - | 0.8998 | 0.8945 | 0.8967 | 0.8961 | 0.8943 | 0.8999 | | **8.6486** | **60** | **0.0296** | **0.8994** | **0.8957** | **0.898** | **0.8959** | **0.8943** | **0.8999** | * 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 ```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} } ```