Improve model card metadata and add project links

This PR improves the model card for better documentation of the model:

* Sets the correct pipeline tag (`text-ranking`) for the model.
* Specifies the library used (`sentence-transformers`).
* Adds links to the project page, paper, and code repository for quick access.

README.md

@@ -1,4 +1,12 @@
 ---
+base_model: intfloat/e5-large-v2
+datasets:
+- noystl/Recombination-Pred
+language:
+- en
+library_name: sentence-transformers
+license: cc
+pipeline_tag: text-ranking
 tags:
 - sentence-transformers
 - sentence-similarity
@@ -6,76 +14,65 @@ tags:
 - 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 
+- 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 
+- 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 
+- 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 
+- 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 
+  - 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
 ---
 
+**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)
+
 # 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.
@@ -429,8 +426,8 @@ You can finetune this model on your own dataset.
 | 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        |
+| 0.7258 | 8900  | 0.0036        |
+| 0.7339 | 9000  | 0.0033        |
 | 0.7421 | 9100  | 0.0031        |
 | 0.7502 | 9200  | 0.003         |
 | 0.7584 | 9300  | 0.0031        |
@@ -483,304 +480,18 @@ You can finetune this model on your own dataset.
 | 1.1416 | 14000 | 0.0034        |
 | 1.1498 | 14100 | 0.0031        |
 | 1.1580 | 14200 | 0.0029        |
-| 1.1661 | 14300 | 0.0029        |
+| 1.1661 | 14300 | 0.0027        |
 | 1.1743 | 14400 | 0.0028        |
 | 1.1824 | 14500 | 0.0037        |
 | 1.1906 | 14600 | 0.0029        |
-| 1.1987 | 14700 | 0.0028        |
+| 1.1987 | 14700 | 0.0027        |
 | 1.2069 | 14800 | 0.0029        |
 | 1.2150 | 14900 | 0.0035        |
 | 1.2232 | 15000 | 0.0029        |
-| 1.2313 | 15100 | 0.0029        |
+| 1.2313 | 15100 | 0.0028        |
 | 1.2395 | 15200 | 0.0027        |
 | 1.2477 | 15300 | 0.003         |
-| 1.2558 | 15400 | 0.0035        |
+| 1.2558 | 15400 | 0.0034        |
 | 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        |
-
-</details>
-
-### 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)
-
-<!--
-## Glossary
-
-*Clearly define terms in order to be accessible across audiences.*
--->
-
-<!--
-## Model Card Authors
-
-*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
--->
-
-<!--
-## Model Card Contact
-
-*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--->
+| 1.2803 | 15700 | 0.0028        |