--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:784827 - loss:ContrastiveLoss base_model: intfloat/e5-large-v2 widget: - source_sentence: >- query: The study addresses the need for effective tools that allow both novice and expert users to analyze the diversity of news coverage about events. It highlights the importance of tailoring the interface to accommodate non-expert users while also considering the insights of journalism-savvy users, indicating a gap in existing systems that cater to varying levels of expertise in news analysis.We suggest combining 'a coordinated visualization interface tailored for visualization non-expert users' and sentences: - graph convolution - Monte-Carlo sampling - geometric features derived from perception sensor data - source_sentence: >- query: The accuracy of pixel flows is crucial for achieving high-quality video enhancement, yet most prior works focus on estimating dense flows that are generally less robust and computationally expensive. This highlights a gap in existing methodologies that fail to prioritize accuracy over density, necessitating a more efficient approach to flow estimation for video enhancement tasks.We suggest combining 'sparse point cloud data' and sentences: - a human cognition mechanism, object unity - Bayesian Optimization - offline supervised learning - source_sentence: >- query: The traditional frame of discernment lacks a crucial factor, the sequence of propositions, which limits the effectiveness of existing methods to measure uncertainty. This gap highlights the need for a more comprehensive approach that can better represent the relationships between the elements of the frame of discernment.We suggest 'combine the order of propositions and the mass of them' inspired by sentences: - a MIA-Module - an Explore-m problem--a well-studied problem related to multi-armed bandits - based on the novel method UGPIG - source_sentence: >- query: Existing methods for anomaly detection on dynamic graphs struggle with capturing complex time information in graph structures and generating effective negative samples for unsupervised learning. These challenges highlight the need for improved methodologies that can address the limitations of current approaches in this field.We suggest combining 'a message-passing framework' and sentences: - an LSTM encoder-decoder - an energy-based model - >- learning the frame-wise associations between detections in consecutive frames - source_sentence: >- query: The study addresses the need for effective time series forecasting methods to estimate the spread of epidemics, particularly in light of the resurgence of COVID-19 cases. It highlights the importance of accurately modeling both linear and non-linear features of epidemic data to provide state authorities and health officials with reliable short-term forecasts and strategies.We suggest combining 'ARIMA' and sentences: - visualization methodologies - geometry - the utilization of a gradient signed distance field (gradient-SDF) pipeline_tag: sentence-similarity library_name: sentence-transformers license: cc datasets: - noystl/Recombination-Pred language: - en --- # SentenceTransformer based on intfloat/e5-large-v2 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/e5-large-v2](https://huggingface.co/intfloat/e5-large-v2). 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:** [intfloat/e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) - **Maximum Sequence Length:** 512 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': 512, 'do_lower_case': False}) with Transformer model: BertModel (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}) (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("sentence_transformers_model_id") # Run inference sentences = [ "query: The study addresses the need for effective time series forecasting methods to estimate the spread of epidemics, particularly in light of the resurgence of COVID-19 cases. It highlights the importance of accurately modeling both linear and non-linear features of epidemic data to provide state authorities and health officials with reliable short-term forecasts and strategies.We suggest combining 'ARIMA' and ", 'visualization methodologies', 'the utilization of a gradient signed distance field (gradient-SDF)', ] 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] ``` ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 784,827 training samples * Columns: query, answer, and label * Approximate statistics based on the first 1000 samples: | | query | answer | label | |:--------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:-----------------------------------------------| | type | string | string | int | | details | | | | * Samples: | query | answer | label | |:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------|:---------------| | query: The study addresses the challenge of action segmentation under weak supervision, where the available ground truth only indicates the presence of actions without providing their temporal ordering or occurrence timing in training videos. This limitation necessitates the development of a method to generate pseudo-ground truth for effective training and improve performance in action segmentation and alignment tasks.We suggest combining 'a Hidden Markov Model' and | a multilayer perceptron | 1 | | query: The study addresses the challenge of action segmentation under weak supervision, where the available ground truth only indicates the presence of actions without providing their temporal ordering or occurrence timing in training videos. This limitation necessitates the development of a method to generate pseudo-ground truth for effective training and improve performance in action segmentation and alignment tasks.We suggest combining 'a Hidden Markov Model' and | an optimal transport problem | 0 | | query: The study addresses the challenge of action segmentation under weak supervision, where the available ground truth only indicates the presence of actions without providing their temporal ordering or occurrence timing in training videos. This limitation necessitates the development of a method to generate pseudo-ground truth for effective training and improve performance in action segmentation and alignment tasks.We suggest combining 'a Hidden Markov Model' and | a context enhancement module | 0 | * Loss: [ContrastiveLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters: ```json { "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE", "margin": 0.5, "size_average": true } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `per_device_train_batch_size`: 64 - `learning_rate`: 4.0560820385265185e-06 - `warmup_ratio`: 0.21933051020273267 - `bf16`: True - `prompts`: {'query': 'query: '} - `batch_sampler`: no_duplicates #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: no - `prediction_loss_only`: True - `per_device_train_batch_size`: 64 - `per_device_eval_batch_size`: 8 - `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`: 4.0560820385265185e-06 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 3 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.21933051020273267 - `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`: 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 - `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 - `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 - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: {'query': 'query: '} - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional
### Training Logs
Click to expand | Epoch | Step | Training Loss | |:------:|:-----:|:-------------:| | 0.0082 | 100 | 0.0321 | | 0.0163 | 200 | 0.0312 | | 0.0245 | 300 | 0.0268 | | 0.0326 | 400 | 0.0139 | | 0.0408 | 500 | 0.0052 | | 0.0489 | 600 | 0.0037 | | 0.0571 | 700 | 0.0037 | | 0.0652 | 800 | 0.0037 | | 0.0734 | 900 | 0.0047 | | 0.0815 | 1000 | 0.0038 | | 0.0897 | 1100 | 0.0037 | | 0.0979 | 1200 | 0.0037 | | 0.1060 | 1300 | 0.0037 | | 0.1142 | 1400 | 0.0049 | | 0.1223 | 1500 | 0.0037 | | 0.1305 | 1600 | 0.0036 | | 0.1386 | 1700 | 0.0037 | | 0.1468 | 1800 | 0.0048 | | 0.1549 | 1900 | 0.0037 | | 0.1631 | 2000 | 0.0036 | | 0.1712 | 2100 | 0.0037 | | 0.1794 | 2200 | 0.0037 | | 0.1876 | 2300 | 0.0048 | | 0.1957 | 2400 | 0.0036 | | 0.2039 | 2500 | 0.0037 | | 0.2120 | 2600 | 0.0036 | | 0.2202 | 2700 | 0.0046 | | 0.2283 | 2800 | 0.0036 | | 0.2365 | 2900 | 0.0035 | | 0.2446 | 3000 | 0.0035 | | 0.2528 | 3100 | 0.0038 | | 0.2609 | 3200 | 0.0042 | | 0.2691 | 3300 | 0.0036 | | 0.2773 | 3400 | 0.0035 | | 0.2854 | 3500 | 0.0035 | | 0.2936 | 3600 | 0.0045 | | 0.3017 | 3700 | 0.0034 | | 0.3099 | 3800 | 0.0035 | | 0.3180 | 3900 | 0.0034 | | 0.3262 | 4000 | 0.0043 | | 0.3343 | 4100 | 0.0036 | | 0.3425 | 4200 | 0.0033 | | 0.3506 | 4300 | 0.0034 | | 0.3588 | 4400 | 0.0035 | | 0.3670 | 4500 | 0.0042 | | 0.3751 | 4600 | 0.0033 | | 0.3833 | 4700 | 0.0035 | | 0.3914 | 4800 | 0.0034 | | 0.3996 | 4900 | 0.0043 | | 0.4077 | 5000 | 0.0034 | | 0.4159 | 5100 | 0.0033 | | 0.4240 | 5200 | 0.0033 | | 0.4322 | 5300 | 0.0033 | | 0.4403 | 5400 | 0.0043 | | 0.4485 | 5500 | 0.0033 | | 0.4567 | 5600 | 0.0033 | | 0.4648 | 5700 | 0.0034 | | 0.4730 | 5800 | 0.0042 | | 0.4811 | 5900 | 0.0033 | | 0.4893 | 6000 | 0.0033 | | 0.4974 | 6100 | 0.0032 | | 0.5056 | 6200 | 0.0035 | | 0.5137 | 6300 | 0.0037 | | 0.5219 | 6400 | 0.0034 | | 0.5300 | 6500 | 0.0034 | | 0.5382 | 6600 | 0.0033 | | 0.5464 | 6700 | 0.0041 | | 0.5545 | 6800 | 0.0033 | | 0.5627 | 6900 | 0.0033 | | 0.5708 | 7000 | 0.0031 | | 0.5790 | 7100 | 0.004 | | 0.5871 | 7200 | 0.0035 | | 0.5953 | 7300 | 0.0033 | | 0.6034 | 7400 | 0.0032 | | 0.6116 | 7500 | 0.0032 | | 0.6198 | 7600 | 0.0041 | | 0.6279 | 7700 | 0.0033 | | 0.6361 | 7800 | 0.0033 | | 0.6442 | 7900 | 0.0032 | | 0.6524 | 8000 | 0.0041 | | 0.6605 | 8100 | 0.0032 | | 0.6687 | 8200 | 0.0033 | | 0.6768 | 8300 | 0.003 | | 0.6850 | 8400 | 0.003 | | 0.6931 | 8500 | 0.0038 | | 0.7013 | 8600 | 0.0033 | | 0.7095 | 8700 | 0.0031 | | 0.7176 | 8800 | 0.0029 | | 0.7258 | 8900 | 0.0037 | | 0.7339 | 9000 | 0.0034 | | 0.7421 | 9100 | 0.0031 | | 0.7502 | 9200 | 0.003 | | 0.7584 | 9300 | 0.0031 | | 0.7665 | 9400 | 0.0037 | | 0.7747 | 9500 | 0.0032 | | 0.7828 | 9600 | 0.0029 | | 0.7910 | 9700 | 0.0028 | | 0.7992 | 9800 | 0.0036 | | 0.8073 | 9900 | 0.0033 | | 0.8155 | 10000 | 0.0031 | | 0.8236 | 10100 | 0.0029 | | 0.8318 | 10200 | 0.0034 | | 0.8399 | 10300 | 0.0033 | | 0.8481 | 10400 | 0.0032 | | 0.8562 | 10500 | 0.003 | | 0.8644 | 10600 | 0.003 | | 0.8725 | 10700 | 0.0034 | | 0.8807 | 10800 | 0.0033 | | 0.8889 | 10900 | 0.003 | | 0.8970 | 11000 | 0.0029 | | 0.9052 | 11100 | 0.0036 | | 0.9133 | 11200 | 0.0031 | | 0.9215 | 11300 | 0.0031 | | 0.9296 | 11400 | 0.003 | | 0.9378 | 11500 | 0.003 | | 0.9459 | 11600 | 0.0035 | | 0.9541 | 11700 | 0.0032 | | 0.9622 | 11800 | 0.0029 | | 0.9704 | 11900 | 0.0031 | | 0.9786 | 12000 | 0.0036 | | 0.9867 | 12100 | 0.0033 | | 0.9949 | 12200 | 0.0031 | | 1.0030 | 12300 | 0.0034 | | 1.0112 | 12400 | 0.0031 | | 1.0193 | 12500 | 0.0032 | | 1.0275 | 12600 | 0.0029 | | 1.0356 | 12700 | 0.0037 | | 1.0438 | 12800 | 0.0031 | | 1.0519 | 12900 | 0.0028 | | 1.0601 | 13000 | 0.0029 | | 1.0683 | 13100 | 0.0029 | | 1.0764 | 13200 | 0.0038 | | 1.0846 | 13300 | 0.0029 | | 1.0927 | 13400 | 0.0029 | | 1.1009 | 13500 | 0.0029 | | 1.1090 | 13600 | 0.0037 | | 1.1172 | 13700 | 0.003 | | 1.1253 | 13800 | 0.003 | | 1.1335 | 13900 | 0.0029 | | 1.1416 | 14000 | 0.0034 | | 1.1498 | 14100 | 0.0031 | | 1.1580 | 14200 | 0.0029 | | 1.1661 | 14300 | 0.0029 | | 1.1743 | 14400 | 0.0028 | | 1.1824 | 14500 | 0.0037 | | 1.1906 | 14600 | 0.0029 | | 1.1987 | 14700 | 0.0028 | | 1.2069 | 14800 | 0.0029 | | 1.2150 | 14900 | 0.0035 | | 1.2232 | 15000 | 0.0029 | | 1.2313 | 15100 | 0.0029 | | 1.2395 | 15200 | 0.0027 | | 1.2477 | 15300 | 0.003 | | 1.2558 | 15400 | 0.0035 | | 1.2640 | 15500 | 0.0027 | | 1.2721 | 15600 | 0.0028 | | 1.2803 | 15700 | 0.0028 | | 1.2884 | 15800 | 0.0037 | | 1.2966 | 15900 | 0.0028 | | 1.3047 | 16000 | 0.0028 | | 1.3129 | 16100 | 0.0028 | | 1.3210 | 16200 | 0.0029 | | 1.3292 | 16300 | 0.0034 | | 1.3374 | 16400 | 0.0028 | | 1.3455 | 16500 | 0.0026 | | 1.3537 | 16600 | 0.0029 | | 1.3618 | 16700 | 0.0034 | | 1.3700 | 16800 | 0.0028 | | 1.3781 | 16900 | 0.0027 | | 1.3863 | 17000 | 0.003 | | 1.3944 | 17100 | 0.0034 | | 1.4026 | 17200 | 0.0028 | | 1.4107 | 17300 | 0.0028 | | 1.4189 | 17400 | 0.0027 | | 1.4271 | 17500 | 0.0028 | | 1.4352 | 17600 | 0.0036 | | 1.4434 | 17700 | 0.0028 | | 1.4515 | 17800 | 0.0027 | | 1.4597 | 17900 | 0.0028 | | 1.4678 | 18000 | 0.0032 | | 1.4760 | 18100 | 0.0029 | | 1.4841 | 18200 | 0.0028 | | 1.4923 | 18300 | 0.0028 | | 1.5004 | 18400 | 0.0028 | | 1.5086 | 18500 | 0.0033 | | 1.5168 | 18600 | 0.0026 | | 1.5249 | 18700 | 0.0027 | | 1.5331 | 18800 | 0.0028 | | 1.5412 | 18900 | 0.0035 | | 1.5494 | 19000 | 0.0026 | | 1.5575 | 19100 | 0.0027 | | 1.5657 | 19200 | 0.0027 | | 1.5738 | 19300 | 0.0028 | | 1.5820 | 19400 | 0.0033 | | 1.5901 | 19500 | 0.0026 | | 1.5983 | 19600 | 0.0028 | | 1.6065 | 19700 | 0.0026 | | 1.6146 | 19800 | 0.0033 | | 1.6228 | 19900 | 0.0026 | | 1.6309 | 20000 | 0.0027 | | 1.6391 | 20100 | 0.0029 | | 1.6472 | 20200 | 0.0032 | | 1.6554 | 20300 | 0.0028 | | 1.6635 | 20400 | 0.0025 | | 1.6717 | 20500 | 0.0025 | | 1.6798 | 20600 | 0.0025 | | 1.6880 | 20700 | 0.003 | | 1.6962 | 20800 | 0.0028 | | 1.7043 | 20900 | 0.0026 | | 1.7125 | 21000 | 0.0024 | | 1.7206 | 21100 | 0.0028 | | 1.7288 | 21200 | 0.0028 | | 1.7369 | 21300 | 0.0026 | | 1.7451 | 21400 | 0.0026 | | 1.7532 | 21500 | 0.0025 | | 1.7614 | 21600 | 0.003 | | 1.7696 | 21700 | 0.0027 | | 1.7777 | 21800 | 0.0023 | | 1.7859 | 21900 | 0.0025 | | 1.7940 | 22000 | 0.0028 | | 1.8022 | 22100 | 0.0025 | | 1.8103 | 22200 | 0.0026 | | 1.8185 | 22300 | 0.0024 | | 1.8266 | 22400 | 0.0025 | | 1.8348 | 22500 | 0.0029 | | 1.8429 | 22600 | 0.0028 | | 1.8511 | 22700 | 0.0024 | | 1.8593 | 22800 | 0.0026 | | 1.8674 | 22900 | 0.003 | | 1.8756 | 23000 | 0.0026 | | 1.8837 | 23100 | 0.0025 | | 1.8919 | 23200 | 0.0025 | | 1.9000 | 23300 | 0.0027 | | 1.9082 | 23400 | 0.0025 | | 1.9163 | 23500 | 0.0026 | | 1.9245 | 23600 | 0.0026 | | 1.9326 | 23700 | 0.0026 | | 1.9408 | 23800 | 0.003 | | 1.9490 | 23900 | 0.0026 | | 1.9571 | 24000 | 0.0026 | | 1.9653 | 24100 | 0.0025 | | 1.9734 | 24200 | 0.003 | | 1.9816 | 24300 | 0.0028 | | 1.9897 | 24400 | 0.0025 | | 1.9979 | 24500 | 0.0028 | | 2.0060 | 24600 | 0.0029 | | 2.0142 | 24700 | 0.0025 | | 2.0223 | 24800 | 0.0026 | | 2.0305 | 24900 | 0.0031 | | 2.0387 | 25000 | 0.0025 | | 2.0468 | 25100 | 0.0025 | | 2.0550 | 25200 | 0.0023 | | 2.0631 | 25300 | 0.0024 | | 2.0713 | 25400 | 0.0031 | | 2.0794 | 25500 | 0.0024 | | 2.0876 | 25600 | 0.0025 | | 2.0957 | 25700 | 0.0024 | | 2.1039 | 25800 | 0.0031 | | 2.1120 | 25900 | 0.0024 | | 2.1202 | 26000 | 0.0025 | | 2.1284 | 26100 | 0.0025 | | 2.1365 | 26200 | 0.0024 | | 2.1447 | 26300 | 0.003 | | 2.1528 | 26400 | 0.0025 | | 2.1610 | 26500 | 0.0024 | | 2.1691 | 26600 | 0.0026 | | 2.1773 | 26700 | 0.003 | | 2.1854 | 26800 | 0.0025 | | 2.1936 | 26900 | 0.0025 | | 2.2017 | 27000 | 0.0024 | | 2.2099 | 27100 | 0.003 | | 2.2181 | 27200 | 0.0024 | | 2.2262 | 27300 | 0.0026 | | 2.2344 | 27400 | 0.0023 | | 2.2425 | 27500 | 0.0023 | | 2.2507 | 27600 | 0.0031 | | 2.2588 | 27700 | 0.0023 | | 2.2670 | 27800 | 0.0022 | | 2.2751 | 27900 | 0.0024 | | 2.2833 | 28000 | 0.0032 | | 2.2914 | 28100 | 0.0024 | | 2.2996 | 28200 | 0.0023 | | 2.3078 | 28300 | 0.0026 | | 2.3159 | 28400 | 0.0023 | | 2.3241 | 28500 | 0.0031 | | 2.3322 | 28600 | 0.0024 | | 2.3404 | 28700 | 0.0023 | | 2.3485 | 28800 | 0.0023 | | 2.3567 | 28900 | 0.0031 | | 2.3648 | 29000 | 0.0024 | | 2.3730 | 29100 | 0.0023 | | 2.3811 | 29200 | 0.0025 | | 2.3893 | 29300 | 0.0027 | | 2.3975 | 29400 | 0.0029 | | 2.4056 | 29500 | 0.0022 | | 2.4138 | 29600 | 0.0024 | | 2.4219 | 29700 | 0.0023 | | 2.4301 | 29800 | 0.0031 | | 2.4382 | 29900 | 0.0024 | | 2.4464 | 30000 | 0.0023 | | 2.4545 | 30100 | 0.0022 | | 2.4627 | 30200 | 0.0029 | | 2.4708 | 30300 | 0.0024 | | 2.4790 | 30400 | 0.0025 | | 2.4872 | 30500 | 0.0024 | | 2.4953 | 30600 | 0.0024 | | 2.5035 | 30700 | 0.003 | | 2.5116 | 30800 | 0.0021 | | 2.5198 | 30900 | 0.0023 | | 2.5279 | 31000 | 0.0024 | | 2.5361 | 31100 | 0.0032 | | 2.5442 | 31200 | 0.0023 | | 2.5524 | 31300 | 0.0022 | | 2.5605 | 31400 | 0.0024 | | 2.5687 | 31500 | 0.0023 | | 2.5769 | 31600 | 0.0029 | | 2.5850 | 31700 | 0.0023 | | 2.5932 | 31800 | 0.0023 | | 2.6013 | 31900 | 0.0023 | | 2.6095 | 32000 | 0.003 | | 2.6176 | 32100 | 0.0023 | | 2.6258 | 32200 | 0.0023 | | 2.6339 | 32300 | 0.0024 | | 2.6421 | 32400 | 0.0027 | | 2.6502 | 32500 | 0.0028 | | 2.6584 | 32600 | 0.0023 | | 2.6666 | 32700 | 0.0021 | | 2.6747 | 32800 | 0.0023 | | 2.6829 | 32900 | 0.0026 | | 2.6910 | 33000 | 0.0024 | | 2.6992 | 33100 | 0.0023 | | 2.7073 | 33200 | 0.0023 | | 2.7155 | 33300 | 0.0024 | | 2.7236 | 33400 | 0.0024 | | 2.7318 | 33500 | 0.0024 | | 2.7399 | 33600 | 0.0023 | | 2.7481 | 33700 | 0.0022 | | 2.7563 | 33800 | 0.0027 | | 2.7644 | 33900 | 0.0023 | | 2.7726 | 34000 | 0.0023 | | 2.7807 | 34100 | 0.0021 | | 2.7889 | 34200 | 0.0025 | | 2.7970 | 34300 | 0.0022 | | 2.8052 | 34400 | 0.0022 | | 2.8133 | 34500 | 0.0021 | | 2.8215 | 34600 | 0.0022 | | 2.8297 | 34700 | 0.0026 | | 2.8378 | 34800 | 0.0024 | | 2.8460 | 34900 | 0.0023 | | 2.8541 | 35000 | 0.0022 | | 2.8623 | 35100 | 0.0026 | | 2.8704 | 35200 | 0.0023 | | 2.8786 | 35300 | 0.0022 | | 2.8867 | 35400 | 0.0023 | | 2.8949 | 35500 | 0.0022 | | 2.9030 | 35600 | 0.0025 | | 2.9112 | 35700 | 0.0023 | | 2.9194 | 35800 | 0.0022 | | 2.9275 | 35900 | 0.0022 | | 2.9357 | 36000 | 0.0028 | | 2.9438 | 36100 | 0.0022 | | 2.9520 | 36200 | 0.0023 | | 2.9601 | 36300 | 0.0022 | | 2.9683 | 36400 | 0.0026 | | 2.9764 | 36500 | 0.0024 | | 2.9846 | 36600 | 0.0024 | | 2.9927 | 36700 | 0.0023 |
### Framework Versions - Python: 3.11.2 - Sentence Transformers: 3.3.1 - Transformers: 4.49.0 - PyTorch: 2.5.1+cu124 - Accelerate: 1.0.1 - Datasets: 3.1.0 - Tokenizers: 0.21.0 ## Citation ### BibTeX ```bibtex @misc{sternlicht2025chimeraknowledgebaseidea, title={CHIMERA: A Knowledge Base of Idea Recombination in Scientific Literature}, author={Noy Sternlicht and Tom Hope}, year={2025}, eprint={2505.20779}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2505.20779}, } ``` #### 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", } ``` #### ContrastiveLoss ```bibtex @inproceedings{hadsell2006dimensionality, author={Hadsell, R. and Chopra, S. and LeCun, Y.}, booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)}, title={Dimensionality Reduction by Learning an Invariant Mapping}, year={2006}, volume={2}, number={}, pages={1735-1742}, doi={10.1109/CVPR.2006.100} } ``` **Quick Links** - 🌐 [Project](https://noy-sternlicht.github.io/CHIMERA-Web) - 📃 [Paper](https://arxiv.org/abs/2505.20779) - 🛠️ [Code](https://github.com/noy-sternlicht/CHIMERA-KB)