--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:23404 - loss:MultipleNegativesRankingLoss base_model: BAAI/bge-m3 widget: - source_sentence: What concern did analysts have regarding Microsoft's canceled data center leases, and how might recent growth in Azure address these concerns? sentences: - 'Microsoft''s Azure cloud division in particular grew by a third over the quarter. The company says about half of that was contributed by AI, news that could ease worries about a slowdown in demand for the new tech. Some analysts had previously pointed to canceled data center leases by Microsoft as a sign of excess capacity.' - "The 2-year note yield, which typically moves in\nstep with interest\ \ rate expectations for the Federal Reserve,\nfell 2.5 basis points to 3.66%,\ \ from 3.685% late on Monday. Oil prices slid on fears of a global recession and\ \ dampening\ndemand due to Trump's trade war. U.S. crude fell 2.63% to\ \ settle at $60.42 per barrel,\n while Brent settled at $64.25 per barrel,\ \ down 2.44% on\nthe day." - 'They tried to bring back tariffs to save our country, but it was gone. It was too late. Nothing could have been done.' - source_sentence: How are evolving domestic alternatives in China expected to impact U.S. semiconductor firms? sentences: - 'And Mr. Secretary, why don''t you talk a little bit about? >> So it''s really a three legged stool in the economic policy. It''s trade, it''s tax, and it''s deregulation.' - "* \n Arm's CPUs gain popularity due to lower power consumption\n \n\n\ \ * \n Amazon, Google, Microsoft design Arm-based data center\nchips\n\ \ \n\n * \n Arm's journey to 50% market share took 20 years\n \ \ \n\n \n By Max A. Cherney\n SAN FRANCISCO, March 31 (Reuters) -\ \ Arm Holdings\n expects its share of the global market for data center\n\ central processing units to surge to 50% by the end of the year,\nup from about\ \ 15% in 2024 with gains driven by the boom in\nartificial intelligence, a senior\ \ executive said. Arm's CPUs are often used as a \"host\" chip inside of an AI\n\ computing system and act as a kind of traffic controller for\nother AI chips.\ \ Nvidia , for example, uses an Arm-based\nchip called Grace in some of\ \ its advanced AI systems which\ncontain two of its Blackwell chips." - "\"Semiconductors will feel a greater impact ... We're already\nwitnessing a domestic\ \ ecosystem evolve in China, with direct\nalternatives for every major US semiconductor\ \ firm. This trend\nis likely to accelerate,\" Udupa said. NATURAL RESOURCES\n\ \ Crude prices, already under pressure from an expected OPEC+\noil output hike\ \ in May, added to the losses." - source_sentence: Which company has declared a quarterly coupon on the Alerian MLP Index ETN? sentences: - JPMORGAN CHASE FINANCIAL COMPANY LLC DECLARES QUARTERLY COUPON ON ALERIAN MLP INDEX ETN - 'The industry has been clouded by fears that a trade dispute could temper consumer confidence, reduce spending, weaken loan demand and pressure fees from advising on deals. JPMorgan Chase , the biggest U.S. bank by assets, sank 7%. Wall Street titans Goldman Sachs and Morgan Stanley dropped more than 7% each.' - 'They went to foreign countries and they built companies are pouring into our country at levels never seen before with jobs and money to follow, and it''s really beautiful. In the coming days, there will be complaints from the globalists and the outsources and special interests. And always fake news will always complain.' - source_sentence: What reasons did the Data Center Coalition give for opposing ERCOT's proposal regarding data centers and crypto miners? sentences: - "The company said in August 2024 that it was set to start\ntalks with other pharmaceutical\ \ companies in the second half of\nthis year for potential partnerships to develop\ \ and\ncommercialize petrelintide. STRUCTURE THERAPEUTICS\n Structure Therapeutics\ \ said last year its\nexperimental oral obesity drug helped reduce weight\ \ by 6.2% on\naverage at the end of 12 weeks in a mid-stage study. <^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\ Weight-loss drug forecasts jump to $150 billion as supply grows \n https://www.reuters.com/business/healthcare-pharmaceuticals/weight-loss-drug-forecasts-jump-150-billion-supply-grows-2024-05-28/\n\ \ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^>\n (Reporting\ \ by Mariam Sunny, Kamal Choudhury, Pratik Jain,\nSriparna Roy, Leroy Leo, Sneha\ \ S K and Nathan Gomes in\nBengaluru; Editing by Shilpi Majumdar and Alan Barona)\n\ \ ((Mariam.ESunny@thomsonreuters.com))" - '"When you make that shift from de-risking your books to getting back in a risk-on attitude, that is going to show up first in all things technology and specifically the Mag Seven for sure." The group''s 2025 struggles are a dramatic shift from the prior two years. The Magnificent Seven''s stunning gains meant they were responsible for well over half of the S&P 500''s 58% two-year return in 2023 and 2024.' - 'But data center operators are opposed because of the risk of damaging electronic equipment and cooling systems. ERCOT last year withdrew a proposal that would have imposed ride-through restrictions on data centers and crypto miners after facing pushback from an industry group, the Data Center Coalition. The group, whose members include Amazon, Google, and Meta, cited costs and the risk of damaging computer chips and cooling systems exposed to fluctuating voltage levels.' - source_sentence: What factors are contributing to pressure on Apple's market share in China? sentences: - 'The company forecast low-to-mid single-digit revenue growth, in line with muted expectations. In China, Apple posted $16 billion in revenue, slightly above forecasts, though competition from Huawei and slower AI rollout continue to pressure market share. If losses hold, Apple is on track to shed more than $150 billion in market value, while a bullish outlook from Microsoft earlier this week has helped the Windows-maker become the world''s most valuable company.' - 'With recent exchange rate fluctuations adding to the uncertainty, we are taking a more cautious outlook for the near future." While Washington and Beijing on Monday agreed to slash tariffs for at least 90 days, the cheer over the temporary truce was tempered by caution given a more permanent trade deal needs to be struck, while higher tariffs overall could still weigh on the global economy. Most of the iPhones Foxconn makes for Apple are assembled in China.' - "It sounds\nlike what youâ\x80\x99re saying CATL obviously major battery supplier,\ \ huge kind\nof company undergirding the electric revolution, you have DeepSeek,\ \ as you\nsaid, kind of prove the viability of AI engineering in a world where\ \ the US is\ntrying to crack down on Chinese access to leading edge technology.\ \ So, like\nboth of those are like, â\x80\x9COh, you can actually get a tech rally\ \ over in\nChina despite whatâ\x80\x99s happening in the trade war.â\x80\x9D Is\ \ that kind of\nlike itâ\x80\x99s just the same mania, itâ\x80\x99s just going\ \ where it has a new\noutlet or? Well, I mean, I think to some extent." 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: SentenceTransformer based on BAAI/bge-m3 results: - task: type: information-retrieval name: Information Retrieval dataset: name: Unknown type: unknown metrics: - type: cosine_accuracy@1 value: 0.34541104084771834 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.6057084259101009 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.7222696974876089 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8465219620577679 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.34541104084771834 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.20190280863670027 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.14445393949752178 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08465219620577678 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.34541104084771834 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.6057084259101009 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.7222696974876089 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8465219620577679 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.5858686159391115 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.503433682480002 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.5104997716404978 name: Cosine Map@100 --- # SentenceTransformer based on BAAI/bge-m3 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3). 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:** [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) - **Maximum Sequence Length:** 8192 tokens - **Output Dimensionality:** 1024 dimensions - **Similarity Function:** Cosine Similarity ### 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: XLMRobertaModel (1): Pooling({'word_embedding_dimension': 1024, '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}) ) ``` ## 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("sentence_transformers_model_id") # Run inference sentences = [ "What factors are contributing to pressure on Apple's market share in China?", "The company forecast low-to-mid single-digit\nrevenue growth, in line with muted expectations. In China, Apple posted $16 billion in revenue, slightly\nabove forecasts, though competition from Huawei and slower AI\nrollout continue to pressure market share. If losses hold, Apple is on track to shed more than $150\nbillion in market value, while a bullish outlook from Microsoft\n earlier this week has helped the Windows-maker become\nthe world's most valuable company.", 'With recent\nexchange rate fluctuations adding to the uncertainty, we are\ntaking a more cautious outlook for the near future." While Washington and Beijing on Monday agreed to slash\ntariffs for at least 90 days, the cheer over the temporary truce\nwas tempered by caution given a more permanent trade deal needs\nto be struck, while higher tariffs overall could still weigh on\nthe global economy. Most of the iPhones Foxconn makes for Apple are assembled in\nChina.', ] 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 * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.3454 | | cosine_accuracy@3 | 0.6057 | | cosine_accuracy@5 | 0.7223 | | cosine_accuracy@10 | 0.8465 | | cosine_precision@1 | 0.3454 | | cosine_precision@3 | 0.2019 | | cosine_precision@5 | 0.1445 | | cosine_precision@10 | 0.0847 | | cosine_recall@1 | 0.3454 | | cosine_recall@3 | 0.6057 | | cosine_recall@5 | 0.7223 | | cosine_recall@10 | 0.8465 | | **cosine_ndcg@10** | **0.5859** | | cosine_mrr@10 | 0.5034 | | cosine_map@100 | 0.5105 | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 23,404 training samples * Columns: sentence_0 and sentence_1 * Approximate statistics based on the first 1000 samples: | | sentence_0 | sentence_1 | |:--------|:-----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------| | type | string | string | | details |
  • min: 10 tokens
  • mean: 24.88 tokens
  • max: 53 tokens
|
  • min: 10 tokens
  • mean: 257.76 tokens
  • max: 8022 tokens
| * Samples: | sentence_0 | sentence_1 | |:--------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | By approximately what percentage did Meta's shares increase in after-hours trading following the announcement of its results? | Its shares
jumped around 7% in after-hours trade on the news of the results. Meanwhile,
Meta beat estimates with $42 billion in revenue last quarter. It's also said
its daily active users across Facebook, Instagram, and the rest of its
services rose 6% year-on-year, marking welcome news for advertisers. | | How have drugmakers responded to proposed tariffs on imported pharmaceutical products during the Commerce Department's investigation? | The move triggered a 21-day public comment period as part of
the investigation led by the Commerce Department. Drugmakers see the probe as a chance to show the
administration that high tariffs would hinder their efforts to
swiftly ramp up U.S. production, and to propose alternatives,
said Ted Murphy, a trade lawyer at law firm Sidley Austin, which
is advising companies on their submissions to the Commerce
Department. Drugmakers have also lobbied Trump to phase in tariffs on
imported pharmaceutical products in hopes of reducing the sting
from the charges. | | Which South American companies currently use the company's regional services, and what growth expectations does Estevez have for the area? | The company already has 36 regions and 114 availability
zones worldwide used by companies such as Netflix, General
Electric and Sony for storage, networking and remote security. Chilean retailer Cencosud, online retail giant MercadoLibre,
and mining companies already use the company's other regional
services, it said. Amazon's first-quarter cloud revenue and income forecast
came in below estimates last Thursday, but Estevez said he's
expecting strong growth in Chile and across the region. | * Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 3 - `per_device_eval_batch_size`: 3 - `num_train_epochs`: 2 - `multi_dataset_batch_sampler`: round_robin #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 3 - `per_device_eval_batch_size`: 3 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1 - `num_train_epochs`: 2 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.0 - `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`: False - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `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`: False - `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 - `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`: None - `hub_always_push`: False - `hub_revision`: None - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `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 - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `liger_kernel_config`: None - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: round_robin
### Training Logs
Click to expand | Epoch | Step | Training Loss | cosine_ndcg@10 | |:------:|:----:|:-------------:|:--------------:| | 0.0192 | 50 | - | 0.5170 | | 0.0384 | 100 | - | 0.5279 | | 0.0577 | 150 | - | 0.5324 | | 0.0769 | 200 | - | 0.5336 | | 0.0961 | 250 | - | 0.5456 | | 0.1153 | 300 | - | 0.5535 | | 0.1346 | 350 | - | 0.5507 | | 0.1538 | 400 | - | 0.5532 | | 0.1730 | 450 | - | 0.5591 | | 0.1922 | 500 | 0.2091 | 0.5693 | | 0.2115 | 550 | - | 0.5666 | | 0.2307 | 600 | - | 0.5669 | | 0.2499 | 650 | - | 0.5668 | | 0.2691 | 700 | - | 0.5636 | | 0.2884 | 750 | - | 0.5650 | | 0.3076 | 800 | - | 0.5636 | | 0.3268 | 850 | - | 0.5677 | | 0.3460 | 900 | - | 0.5686 | | 0.3652 | 950 | - | 0.5678 | | 0.3845 | 1000 | 0.0546 | 0.5624 | | 0.4037 | 1050 | - | 0.5659 | | 0.4229 | 1100 | - | 0.5687 | | 0.4421 | 1150 | - | 0.5704 | | 0.4614 | 1200 | - | 0.5695 | | 0.4806 | 1250 | - | 0.5702 | | 0.4998 | 1300 | - | 0.5582 | | 0.5190 | 1350 | - | 0.5703 | | 0.5383 | 1400 | - | 0.5688 | | 0.5575 | 1450 | - | 0.5722 | | 0.5767 | 1500 | 0.0529 | 0.5673 | | 0.5959 | 1550 | - | 0.5669 | | 0.6151 | 1600 | - | 0.5597 | | 0.6344 | 1650 | - | 0.5666 | | 0.6536 | 1700 | - | 0.5626 | | 0.6728 | 1750 | - | 0.5627 | | 0.6920 | 1800 | - | 0.5641 | | 0.7113 | 1850 | - | 0.5572 | | 0.7305 | 1900 | - | 0.5632 | | 0.7497 | 1950 | - | 0.5733 | | 0.7689 | 2000 | 0.0478 | 0.5644 | | 0.7882 | 2050 | - | 0.5658 | | 0.8074 | 2100 | - | 0.5608 | | 0.8266 | 2150 | - | 0.5687 | | 0.8458 | 2200 | - | 0.5728 | | 0.8651 | 2250 | - | 0.5581 | | 0.8843 | 2300 | - | 0.5612 | | 0.9035 | 2350 | - | 0.5616 | | 0.9227 | 2400 | - | 0.5650 | | 0.9419 | 2450 | - | 0.5626 | | 0.9612 | 2500 | 0.0482 | 0.5665 | | 0.9804 | 2550 | - | 0.5668 | | 0.9996 | 2600 | - | 0.5552 | | 1.0 | 2601 | - | 0.5556 | | 1.0188 | 2650 | - | 0.5681 | | 1.0381 | 2700 | - | 0.5620 | | 1.0573 | 2750 | - | 0.5639 | | 1.0765 | 2800 | - | 0.5646 | | 1.0957 | 2850 | - | 0.5714 | | 1.1150 | 2900 | - | 0.5748 | | 1.1342 | 2950 | - | 0.5739 | | 1.1534 | 3000 | 0.033 | 0.5630 | | 1.1726 | 3050 | - | 0.5655 | | 1.1918 | 3100 | - | 0.5711 | | 1.2111 | 3150 | - | 0.5680 | | 1.2303 | 3200 | - | 0.5742 | | 1.2495 | 3250 | - | 0.5714 | | 1.2687 | 3300 | - | 0.5657 | | 1.2880 | 3350 | - | 0.5636 | | 1.3072 | 3400 | - | 0.5701 | | 1.3264 | 3450 | - | 0.5720 | | 1.3456 | 3500 | 0.0276 | 0.5733 | | 1.3649 | 3550 | - | 0.5738 | | 1.3841 | 3600 | - | 0.5743 | | 1.4033 | 3650 | - | 0.5702 | | 1.4225 | 3700 | - | 0.5732 | | 1.4418 | 3750 | - | 0.5705 | | 1.4610 | 3800 | - | 0.5774 | | 1.4802 | 3850 | - | 0.5735 | | 1.4994 | 3900 | - | 0.5781 | | 1.5186 | 3950 | - | 0.5691 | | 1.5379 | 4000 | 0.0266 | 0.5729 | | 1.5571 | 4050 | - | 0.5712 | | 1.5763 | 4100 | - | 0.5685 | | 1.5955 | 4150 | - | 0.5711 | | 1.6148 | 4200 | - | 0.5712 | | 1.6340 | 4250 | - | 0.5716 | | 1.6532 | 4300 | - | 0.5762 | | 1.6724 | 4350 | - | 0.5813 | | 1.6917 | 4400 | - | 0.5822 | | 1.7109 | 4450 | - | 0.5805 | | 1.7301 | 4500 | 0.0337 | 0.5789 | | 1.7493 | 4550 | - | 0.5745 | | 1.7686 | 4600 | - | 0.5752 | | 1.7878 | 4650 | - | 0.5780 | | 1.8070 | 4700 | - | 0.5815 | | 1.8262 | 4750 | - | 0.5833 | | 1.8454 | 4800 | - | 0.5809 | | 1.8647 | 4850 | - | 0.5711 | | 1.8839 | 4900 | - | 0.5716 | | 1.9031 | 4950 | - | 0.5816 | | 1.9223 | 5000 | 0.0299 | 0.5815 | | 1.9416 | 5050 | - | 0.5816 | | 1.9608 | 5100 | - | 0.5847 | | 1.9800 | 5150 | - | 0.5831 | | 1.9992 | 5200 | - | 0.5847 | | 2.0 | 5202 | - | 0.5859 |
### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.4.1 - Transformers: 4.53.0 - PyTorch: 2.2.0+cu121 - Accelerate: 1.8.1 - Datasets: 3.6.0 - Tokenizers: 0.21.2 ## 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", } ``` #### 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} } ```