Update model card metadata and add links to project page and code repository
#1
by nielsr HF Staff - opened
README.md
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tags:
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- sentence-transformers
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- sentence-similarity
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- generated_from_trainer
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- dataset_size:784827
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- loss:ContrastiveLoss
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base_model: BAAI/bge-large-en-v1.5
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widget:
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- source_sentence:
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field.We suggest combining 'a message-passing framework' and
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sentences:
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- a single global model
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- videos
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- sequential polygon generation
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- source_sentence:
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tailored for visualization non-expert users' and
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sentences:
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- worst-case resource analysis
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- Graph Convolution Networks
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- a text encoder
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- source_sentence:
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suggest combining 'sparse point cloud data' and
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sentences:
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- a Temporal Eigenvalue Loss
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- diffusion models
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- explicit 3D representations, such as polygonal meshes
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- source_sentence:
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discernment.We suggest 'combine the order of propositions and the mass of
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them' inspired by
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sentences:
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to handle large displacements before energy-based optimizations
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- encoder-decoder models
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non-linear features of epidemic data to provide state authorities and health
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officials with reliable short-term forecasts and strategies.We suggest
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combining 'ARIMA' and
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sentences:
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- Transformers
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- a traditional feature-mixed branch
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using limited resources
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pipeline_tag: sentence-similarity
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library_name: sentence-transformers
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license: cc
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datasets:
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- noystl/Recombination-Pred
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language:
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- en
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---
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# SentenceTransformer based on BAAI/bge-large-en-v1.5
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5). 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.
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## Model Details
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| 1.1009 | 13500 | 0.0029 |
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| 1.1090 | 13600 | 0.0037 |
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| 1.1172 | 13700 | 0.0029 |
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| 1.1253 | 13800 | 0.
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| 1.1335 | 13900 | 0.0027 |
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| 1.1416 | 14000 | 0.0033 |
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| 1.1498 | 14100 | 0.003 |
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| 1.2313 | 15100 | 0.0027 |
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| 1.2395 | 15200 | 0.0027 |
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| 1.2477 | 15300 | 0.0028 |
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| 1.2558 | 15400 |
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| 1.2640 | 15500 | 0.0027 |
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| 1.2721 | 15600 | 0.0027 |
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| 1.2803 | 15700 | 0.0027 |
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| 1.2884 | 15800 | 0.0037 |
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| 1.2966 | 15900 | 0.0027 |
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| 1.3047 | 16000 | 0.0027 |
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| 1.3129 | 16100 | 0.0027 |
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| 1.3210 | 16200 | 0.0028 |
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| 1.3292 | 16300 | 0.0033 |
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| 1.3374 | 16400 | 0.0026 |
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| 1.3455 | 16500 | 0.0025 |
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| 1.3537 | 16600 | 0.0028 |
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| 1.3618 | 16700 | 0.0034 |
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| 1.3700 | 16800 | 0.0027 |
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| 1.3781 | 16900 | 0.0026 |
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| 1.3863 | 17000 | 0.0027 |
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| 1.3944 | 17100 | 0.0033 |
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| 1.4026 | 17200 | 0.0027 |
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| 1.4107 | 17300 | 0.0027 |
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| 1.4189 | 17400 | 0.0026 |
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| 1.4271 | 17500 | 0.0027 |
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| 1.4352 | 17600 | 0.0034 |
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| 1.4434 | 17700 | 0.0027 |
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| 1.4515 | 17800 | 0.0025 |
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| 1.4597 | 17900 | 0.0027 |
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| 1.4678 | 18000 | 0.0031 |
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| 1.4760 | 18100 | 0.0027 |
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| 1.4841 | 18200 | 0.0027 |
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| 1.4923 | 18300 | 0.0027 |
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| 1.5004 | 18400 | 0.0027 |
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| 1.5086 | 18500 | 0.0031 |
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| 1.5168 | 18600 | 0.0025 |
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| 1.5249 | 18700 | 0.0026 |
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| 1.5331 | 18800 | 0.0027 |
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| 1.5412 | 18900 | 0.0035 |
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| 1.5494 | 19000 | 0.0025 |
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| 1.5575 | 19100 | 0.0027 |
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| 1.5657 | 19200 | 0.0026 |
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| 1.5738 | 19300 | 0.0028 |
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| 1.5820 | 19400 | 0.0032 |
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| 1.5901 | 19500 | 0.0025 |
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| 1.5983 | 19600 | 0.0027 |
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| 1.6065 | 19700 | 0.0026 |
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| 1.6146 | 19800 | 0.0034 |
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| 1.6228 | 19900 | 0.0027 |
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| 1.6309 | 20000 | 0.0027 |
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| 1.6391 | 20100 | 0.0028 |
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| 1.6472 | 20200 | 0.0031 |
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| 1.6554 | 20300 | 0.0028 |
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| 1.6635 | 20400 | 0.0025 |
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| 1.6717 | 20500 | 0.0025 |
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| 1.6798 | 20600 | 0.0026 |
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| 1.6880 | 20700 | 0.003 |
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| 1.6962 | 20800 | 0.0029 |
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| 1.7043 | 20900 | 0.0027 |
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| 1.7125 | 21000 | 0.0025 |
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| 1.7206 | 21100 | 0.0029 |
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| 1.7288 | 21200 | 0.0029 |
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| 1.7369 | 21300 | 0.0027 |
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| 1.7451 | 21400 | 0.0026 |
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| 1.7532 | 21500 | 0.0025 |
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| 1.7614 | 21600 | 0.003 |
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| 1.7696 | 21700 | 0.0028 |
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| 1.7777 | 21800 | 0.0024 |
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| 1.7859 | 21900 | 0.0025 |
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| 1.7940 | 22000 | 0.003 |
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| 1.8022 | 22100 | 0.0026 |
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| 1.8103 | 22200 | 0.0027 |
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| 1.8185 | 22300 | 0.0027 |
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| 1.8266 | 22400 | 0.0026 |
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| 1.8348 | 22500 | 0.003 |
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| 1.8429 | 22600 | 0.0029 |
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| 1.8511 | 22700 | 0.0025 |
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| 1.8593 | 22800 | 0.0026 |
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| 1.8674 | 22900 | 0.0031 |
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| 1.8756 | 23000 | 0.0027 |
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| 1.8837 | 23100 | 0.0026 |
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| 1.8919 | 23200 | 0.0025 |
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| 1.9000 | 23300 | 0.0028 |
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| 1.9082 | 23400 | 0.0027 |
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| 1.9163 | 23500 | 0.0027 |
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| 1.9245 | 23600 | 0.0027 |
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| 1.9326 | 23700 | 0.0026 |
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| 1.9408 | 23800 | 0.0031 |
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| 1.9490 | 23900 | 0.0027 |
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| 1.9571 | 24000 | 0.0027 |
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| 1.9653 | 24100 | 0.0026 |
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| 1.9734 | 24200 | 0.0032 |
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| 1.9816 | 24300 | 0.0029 |
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| 1.9897 | 24400 | 0.0026 |
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| 1.9979 | 24500 | 0.0028 |
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| 2.0060 | 24600 | 0.0029 |
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| 2.0142 | 24700 | 0.0026 |
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| 2.0223 | 24800 | 0.0027 |
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| 2.0305 | 24900 | 0.0033 |
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| 2.0387 | 25000 | 0.0026 |
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| 2.0468 | 25100 | 0.0026 |
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| 2.0550 | 25200 | 0.0024 |
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| 2.0631 | 25300 | 0.0026 |
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| 2.0713 | 25400 | 0.0033 |
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| 2.0794 | 25500 | 0.0025 |
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| 2.0876 | 25600 | 0.0026 |
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| 2.0957 | 25700 | 0.0026 |
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| 2.1039 | 25800 | 0.0033 |
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| 2.1120 | 25900 | 0.0025 |
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| 2.1202 | 26000 | 0.0026 |
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| 2.1284 | 26100 | 0.0026 |
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| 2.1365 | 26200 | 0.0025 |
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| 613 |
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| 2.1447 | 26300 | 0.0031 |
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| 2.1528 | 26400 | 0.0026 |
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| 2.1610 | 26500 | 0.0025 |
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| 2.1691 | 26600 | 0.0026 |
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| 2.1773 | 26700 | 0.0032 |
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| 2.1854 | 26800 | 0.0026 |
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| 2.1936 | 26900 | 0.0026 |
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| 2.2017 | 27000 | 0.0025 |
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| 2.2099 | 27100 | 0.0032 |
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| 2.2181 | 27200 | 0.0025 |
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| 2.2262 | 27300 | 0.0025 |
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| 2.2344 | 27400 | 0.0024 |
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| 2.2425 | 27500 | 0.0025 |
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| 2.2507 | 27600 | 0.0033 |
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| 2.2588 | 27700 | 0.0024 |
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| 2.2670 | 27800 | 0.0024 |
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| 2.2751 | 27900 | 0.0024 |
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| 2.2833 | 28000 | 0.0033 |
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| 2.2914 | 28100 | 0.0025 |
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| 2.2996 | 28200 | 0.0024 |
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| 2.3078 | 28300 | 0.0026 |
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| 2.3159 | 28400 | 0.0024 |
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| 2.3241 | 28500 | 0.0032 |
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| 2.3322 | 28600 | 0.0025 |
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| 2.3404 | 28700 | 0.0024 |
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| 2.3485 | 28800 | 0.0024 |
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| 2.3567 | 28900 | 0.0032 |
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| 2.3648 | 29000 | 0.0025 |
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| 2.3730 | 29100 | 0.0024 |
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| 2.3811 | 29200 | 0.0024 |
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| 2.3893 | 29300 | 0.0028 |
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| 2.3975 | 29400 | 0.003 |
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| 2.4056 | 29500 | 0.0023 |
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| 2.4138 | 29600 | 0.0025 |
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| 2.4219 | 29700 | 0.0024 |
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| 2.4301 | 29800 | 0.0032 |
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| 2.4382 | 29900 | 0.0025 |
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| 2.4464 | 30000 | 0.0024 |
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| 2.4545 | 30100 | 0.0023 |
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| 2.4627 | 30200 | 0.003 |
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| 2.4708 | 30300 | 0.0024 |
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| 2.4790 | 30400 | 0.0025 |
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| 2.4872 | 30500 | 0.0025 |
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| 2.4953 | 30600 | 0.0025 |
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| 2.5035 | 30700 | 0.0031 |
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| 2.5116 | 30800 | 0.0022 |
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| 2.5198 | 30900 | 0.0024 |
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| 2.5279 | 31000 | 0.0024 |
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| 2.5361 | 31100 | 0.0032 |
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| 2.5442 | 31200 | 0.0024 |
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| 2.5524 | 31300 | 0.0023 |
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| 2.5605 | 31400 | 0.0025 |
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| 2.5687 | 31500 | 0.0024 |
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| 2.5769 | 31600 | 0.0031 |
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| 2.5850 | 31700 | 0.0024 |
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| 2.5932 | 31800 | 0.0024 |
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| 2.6013 | 31900 | 0.0024 |
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| 2.6095 | 32000 | 0.0031 |
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| 2.6176 | 32100 | 0.0025 |
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| 2.6258 | 32200 | 0.0025 |
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| 2.6339 | 32300 | 0.0025 |
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| 2.6421 | 32400 | 0.0027 |
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| 675 |
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| 2.6502 | 32500 | 0.0029 |
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| 2.6584 | 32600 | 0.0024 |
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| 2.6666 | 32700 | 0.0023 |
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| 2.6747 | 32800 | 0.0025 |
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| 2.6829 | 32900 | 0.0028 |
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| 2.6910 | 33000 | 0.0026 |
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| 2.6992 | 33100 | 0.0025 |
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| 2.7073 | 33200 | 0.0024 |
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| 2.7155 | 33300 | 0.0025 |
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| 2.7236 | 33400 | 0.0026 |
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| 2.7318 | 33500 | 0.0027 |
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| 2.7399 | 33600 | 0.0025 |
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| 2.7481 | 33700 | 0.0024 |
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| 2.7563 | 33800 | 0.0028 |
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| 2.7644 | 33900 | 0.0025 |
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| 2.7726 | 34000 | 0.0024 |
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| 2.7807 | 34100 | 0.0023 |
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| 2.7889 | 34200 | 0.0027 |
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| 2.7970 | 34300 | 0.0024 |
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| 2.8052 | 34400 | 0.0025 |
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| 2.8133 | 34500 | 0.0024 |
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| 2.8215 | 34600 | 0.0024 |
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| 2.8297 | 34700 | 0.0029 |
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| 2.8378 | 34800 | 0.0027 |
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| 2.8460 | 34900 | 0.0025 |
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| 2.8541 | 35000 | 0.0023 |
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| 2.8623 | 35100 | 0.0029 |
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| 2.8704 | 35200 | 0.0025 |
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| 2.8786 | 35300 | 0.0024 |
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| 2.8867 | 35400 | 0.0024 |
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| 2.8949 | 35500 | 0.0024 |
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| 2.9030 | 35600 | 0.0028 |
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| 2.9112 | 35700 | 0.0026 |
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| 2.9194 | 35800 | 0.0023 |
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| 2.9275 | 35900 | 0.0024 |
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| 2.9357 | 36000 | 0.003 |
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| 2.9438 | 36100 | 0.0025 |
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| 2.9520 | 36200 | 0.0025 |
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| 2.9601 | 36300 | 0.0024 |
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| 2.9683 | 36400 | 0.0028 |
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| 2.9764 | 36500 | 0.0027 |
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</details>
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### Framework Versions
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- Python: 3.11.2
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- Sentence Transformers: 3.3.1
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- Transformers: 4.49.0
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- PyTorch: 2.5.1+cu124
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- Accelerate: 1.0.1
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- Datasets: 3.1.0
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- Tokenizers: 0.21.0
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## Citation
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### BibTeX
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```bibtex
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@misc{sternlicht2025chimeraknowledgebaseidea,
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title={CHIMERA: A Knowledge Base of Idea Recombination in Scientific Literature},
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author={Noy Sternlicht and Tom Hope},
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year={2025},
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eprint={2505.20779},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2505.20779},
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}
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```
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#### Sentence Transformers
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```bibtex
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@inproceedings{reimers-2019-sentence-bert,
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| 748 |
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title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
|
| 749 |
-
author = "Reimers, Nils and Gurevych, Iryna",
|
| 750 |
-
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
|
| 751 |
-
month = "11",
|
| 752 |
-
year = "2019",
|
| 753 |
-
publisher = "Association for Computational Linguistics",
|
| 754 |
-
url = "https://arxiv.org/abs/1908.10084",
|
| 755 |
-
}
|
| 756 |
-
```
|
| 757 |
-
|
| 758 |
-
#### ContrastiveLoss
|
| 759 |
-
```bibtex
|
| 760 |
-
@inproceedings{hadsell2006dimensionality,
|
| 761 |
-
author={Hadsell, R. and Chopra, S. and LeCun, Y.},
|
| 762 |
-
booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)},
|
| 763 |
-
title={Dimensionality Reduction by Learning an Invariant Mapping},
|
| 764 |
-
year={2006},
|
| 765 |
-
volume={2},
|
| 766 |
-
number={},
|
| 767 |
-
pages={1735-1742},
|
| 768 |
-
doi={10.1109/CVPR.2006.100}
|
| 769 |
-
}
|
| 770 |
-
```
|
| 771 |
-
|
| 772 |
-
**Quick Links**
|
| 773 |
-
- 🌐 [Project](https://noy-sternlicht.github.io/CHIMERA-Web)
|
| 774 |
-
- 📃 [Paper](https://arxiv.org/abs/2505.20779)
|
| 775 |
-
- 🛠️ [Code](https://github.com/noy-sternlicht/CHIMERA-KB)
|
| 776 |
-
|
| 777 |
-
|
| 778 |
-
<!--
|
| 779 |
-
## Glossary
|
| 780 |
-
|
| 781 |
-
*Clearly define terms in order to be accessible across audiences.*
|
| 782 |
-
-->
|
| 783 |
-
|
| 784 |
-
<!--
|
| 785 |
-
## Model Card Authors
|
| 786 |
-
|
| 787 |
-
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
|
| 788 |
-
-->
|
| 789 |
-
|
| 790 |
-
<!--
|
| 791 |
-
## Model Card Contact
|
| 792 |
-
|
| 793 |
-
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
|
| 794 |
-
-->
|
|
|
|
| 1 |
---
|
| 2 |
+
base_model: BAAI/bge-large-en-v1.5
|
| 3 |
+
datasets:
|
| 4 |
+
- noystl/Recombination-Pred
|
| 5 |
+
language:
|
| 6 |
+
- en
|
| 7 |
+
library_name: sentence-transformers
|
| 8 |
+
license: cc
|
| 9 |
+
pipeline_tag: text-ranking
|
| 10 |
tags:
|
| 11 |
- sentence-transformers
|
| 12 |
- sentence-similarity
|
|
|
|
| 14 |
- generated_from_trainer
|
| 15 |
- dataset_size:784827
|
| 16 |
- loss:ContrastiveLoss
|
|
|
|
| 17 |
widget:
|
| 18 |
+
- source_sentence: 'Represent this sentence for searching relevant passages: Existing
|
| 19 |
+
methods for anomaly detection on dynamic graphs struggle with capturing complex
|
| 20 |
+
time information in graph structures and generating effective negative samples
|
| 21 |
+
for unsupervised learning. These challenges highlight the need for improved methodologies
|
| 22 |
+
that can address the limitations of current approaches in this field.We suggest
|
| 23 |
+
combining ''a message-passing framework'' and '
|
|
|
|
| 24 |
sentences:
|
| 25 |
- a single global model
|
| 26 |
- videos
|
| 27 |
- sequential polygon generation
|
| 28 |
+
- source_sentence: 'Represent this sentence for searching relevant passages: The study
|
| 29 |
+
addresses the need for effective tools that allow both novice and expert users
|
| 30 |
+
to analyze the diversity of news coverage about events. It highlights the importance
|
| 31 |
+
of tailoring the interface to accommodate non-expert users while also considering
|
| 32 |
+
the insights of journalism-savvy users, indicating a gap in existing systems that
|
| 33 |
+
cater to varying levels of expertise in news analysis.We suggest combining ''a
|
| 34 |
+
coordinated visualization interface tailored for visualization non-expert users''
|
| 35 |
+
and '
|
|
|
|
| 36 |
sentences:
|
| 37 |
- worst-case resource analysis
|
| 38 |
- Graph Convolution Networks
|
| 39 |
- a text encoder
|
| 40 |
+
- source_sentence: 'Represent this sentence for searching relevant passages: The accuracy
|
| 41 |
+
of pixel flows is crucial for achieving high-quality video enhancement, yet most
|
| 42 |
+
prior works focus on estimating dense flows that are generally less robust and
|
| 43 |
+
computationally expensive. This highlights a gap in existing methodologies that
|
| 44 |
+
fail to prioritize accuracy over density, necessitating a more efficient approach
|
| 45 |
+
to flow estimation for video enhancement tasks.We suggest combining ''sparse point
|
| 46 |
+
cloud data'' and '
|
|
|
|
| 47 |
sentences:
|
| 48 |
- a Temporal Eigenvalue Loss
|
| 49 |
- diffusion models
|
| 50 |
- explicit 3D representations, such as polygonal meshes
|
| 51 |
+
- source_sentence: 'Represent this sentence for searching relevant passages: The traditional
|
| 52 |
+
frame of discernment lacks a crucial factor, the sequence of propositions, which
|
| 53 |
+
limits the effectiveness of existing methods to measure uncertainty. This gap
|
| 54 |
+
highlights the need for a more comprehensive approach that can better represent
|
| 55 |
+
the relationships between the elements of the frame of discernment.We suggest
|
| 56 |
+
''combine the order of propositions and the mass of them'' inspired by '
|
|
|
|
|
|
|
| 57 |
sentences:
|
| 58 |
+
- the traditional matching-optimization methods where matching is introduced to
|
| 59 |
+
handle large displacements before energy-based optimizations
|
|
|
|
| 60 |
- encoder-decoder models
|
| 61 |
+
- In another vein, researchers propose new attention augmentation methods to make
|
| 62 |
+
transformers more accurate, efficient and interpretable
|
| 63 |
+
- source_sentence: 'Represent this sentence for searching relevant passages: The study
|
| 64 |
+
addresses the need for effective time series forecasting methods to estimate the
|
| 65 |
+
spread of epidemics, particularly in light of the resurgence of COVID-19 cases.\
|
| 66 |
+
It highlights the importance of accurately modeling both linear and non-linear
|
| 67 |
+
features of epidemic data to provide state authorities and health officials with
|
| 68 |
+
reliable short-term forecasts and strategies.We suggest combining ''ARIMA'' and '
|
|
|
|
|
|
|
|
|
|
| 69 |
sentences:
|
| 70 |
- Transformers
|
| 71 |
- a traditional feature-mixed branch
|
| 72 |
+
- the human brain is able to efficiently learn effective control strategies using
|
| 73 |
+
limited resources
|
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|
| 74 |
---
|
| 75 |
|
| 76 |
# SentenceTransformer based on BAAI/bge-large-en-v1.5
|
| 77 |
|
| 78 |
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5). 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.
|
| 79 |
+
It is based on the method described in [CHIMERA: A Knowledge Base of Idea Recombination in Scientific Literature](https://huggingface.co/papers/2505.20779).
|
| 80 |
+
|
| 81 |
+
[Project page](https://noy-sternlicht.github.io/CHIMERA-Web).
|
| 82 |
+
[Code](https://github.com/noy-sternlicht/CHIMERA-KB).
|
| 83 |
|
| 84 |
## Model Details
|
| 85 |
|
|
|
|
| 479 |
| 1.1009 | 13500 | 0.0029 |
|
| 480 |
| 1.1090 | 13600 | 0.0037 |
|
| 481 |
| 1.1172 | 13700 | 0.0029 |
|
| 482 |
+
| 1.1253 | 13800 | 0.0027 |
|
| 483 |
| 1.1335 | 13900 | 0.0027 |
|
| 484 |
| 1.1416 | 14000 | 0.0033 |
|
| 485 |
| 1.1498 | 14100 | 0.003 |
|
|
|
|
| 495 |
| 1.2313 | 15100 | 0.0027 |
|
| 496 |
| 1.2395 | 15200 | 0.0027 |
|
| 497 |
| 1.2477 | 15300 | 0.0028 |
|
| 498 |
+
| 1.2558 | 15400 |
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