Track large CSV files with Git LFS

.gitattributes

@@ -56,3 +56,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+*.csv filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,3 +1,21 @@
----
-license: mit
----
+# Description
+
+Three Datasets released by rllm. Each dataset has a seperated Readme.
+
+Github Repo for rllm: https://github.com/rllm-team/rllm
+
+Arxiv Paper for rllm: https://arxiv.org/abs/2407.20157
+
+# Citation
+
+```
+@article{rllm2024,
+      title={rLLM: Relational Table Learning with LLMs}, 
+      author={Weichen Li and Xiaotong Huang and Jianwu Zheng and Zheng Wang and Chaokun Wang and Li Pan and Jianhua Li},
+      year={2024},
+      eprint={2407.20157},
+      archivePrefix={arXiv},
+      primaryClass={cs.AI},
+      url={https://arxiv.org/abs/2407.20157}, 
+}
+```

TACM12K/README.md

@@ -0,0 +1,48 @@
+# TACM12K
+
+**Table-ACM12K (TACM12K)** is a relational table dataset reorganized from the ACM heterogeneous graph dataset[1]. It includes four tables: papers, authors, Paper-Author relationships, and citation relationships. The paper features include year, title, and abstract, while author features include name and affiliation. Additionally, we label the papers based on the conferences in which they were published.
+
+## Processed
+
+First, we created the Author table by extracting the original authors' IDs and names from the file. We added the firm information to each author based on the 'AvsF' data, resulting in the Author table (17431 entries).
+
+Next, we worked on the Paper table, manually annotating the year information for the venues. Using the 'PvsV' matrix, we added the year attribute to the Paper entries. Due to mislabeling in the original data where the STOC conference was incorrectly marked as the COLT conference in the "PvsC" matrix, we corrected this in the "VvsC" matrix. We then recalculated the PvsV * VvsC to obtain the corrected PvsC matrix, ultimately assigning the conference attribute to each paper. The number of papers per conference is shown in Table 1.
+
+The original dataset contained text information for each paper as a concatenation of title and abstract. To make this structure clearer, we utilized three methods to extract the title from the original text information:
+1. Use the scholarly library in Python to accurately query the last two sentences of the original text.
+2. Utilize the Tongyi Qianwen language model to extract the title from the original text.
+3. Match the abstract in the Aminer dataset [tj2008Aminer] to obtain the title.
+
+We then compared the results of these three methods. If two results were the same, we considered it the title. If all three results differed, we manually verified and supplemented the title information. This process allowed us to separate the original text into a distinct title and abstract. Additionally, we supplemented some incomplete abstracts.
+
+The Write table inherited the "PvsA" matrix from the original data. The Cite table inherited the "PvsP" matrix from the original data.
+
+- 
+
+<center>Table 1: Each conference corresponds to its number of papers</center>
+
+| Conference | Number | Conference | Number |
+| :--------: | :----: | :--------: | :----: |
+|    KDD     |  1061  |    SOSP    |  332   |
+|   SIGMOD   |  1417  |    SPAA    |  473   |
+|    WWW     |  1653  |  SIGCOMM   |  648   |
+|   SIGIR    |  1572  |  MobiCOMM  |  322   |
+|    CIKM    |  1724  |    ICML    |  456   |
+|    SODA    |  662   |    COLT    |   83   |
+|    STOC    |  1519  |    VLDB    |  577   |
+
+## Content
+
+- paper.csv: It contains 12,499 papers of 14 conferences in different years, in which each row of data is the paper's unique identification paper_id, publication year, published conference, title of the paper, and abstract of the paper.
+- author.csv: It includes 17,431 authors, each with a unique identification of author_id, name, and firm or organization for which they work.
+- cite.csv: A total of 30789 paper citation relationships are included, two of which are listed as paper_id and paper_id_cited, that is, the former cited the latter.
+- paper_author.csv: It includes 37055 papers and author relationships, and each line is paper ID paper_id and author ID author_id. A paper may have multiple authors.
+- masks.pt: The training set (280), verification set(500) and test set(1000) are divided. The training set selected 20 papers for each category. While the verification set and the test set meet the natural distribution law as much as possible, the balance adjustment is made so that the number of papers in the least category and the number of papers in the most category are not more than four times.
+- paper_embeddings.npy: The paper information embedding was carried out using the "all-MiniLM-L6-v2" model.
+- author_embeddings.npy: The author information embedding was carried out using the "all-MiniLM-L6-v2" model.
+
+## Refferences
+
+[1]. Wang X, Ji H, Shi C, et al. Heterogeneous graph attention network[C]//The world wide web conference. 2019: 2022-2032.
+
+[2]. Tang J, Zhang J, Yao L, et al. Arnetminer: extraction and mining of academic social networks[C]//Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. 2008: 990-998.

TACM12K/author_embeddings.npy

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TACM12K/writings.csv

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TLF2K/README.md

@@ -0,0 +1,189 @@
+-----------
+Description
+-----------
+
+Table-LastFM2K extends the original lastfm-2k dataset (https://grouplens.org/datasets/hetrec-2011/) with enriched artist data. As the original dataset was collected in 2011 and last.fm data is continuously updated, we have selected pre-2011 information for each artist to augment the data. The augmented data is available in the 'regression' folder as artists.csv. This new version comprises two tasks, each utilizing distinct sets of data.
+
+The basic task is to classify artists' genre label.
+
+-----------
+Artists file format
+-----------
+artists.csv follows the format outlined below:
+artistID,type,name,born,yearsActive,location,label,tag_list,biography,url
+
+* artistID: The identifier given in the original lastfm dataset.
+* type: Used to indicate whether the artist is an individual (such as a singer, DJ, or composer) or a group (band, musical ensemble), left blank if indeterminate.
+* name: The name of the artist.
+* born: Only present for single-type artists, indicating the artist's birthdate.
+* yearsActive: Only present for multiple-type artists, indicating the artist's years of activity.
+* location: The birthplace or location of establishment of the artist.
+* label: The artist's genre label, generated by ChatGPT along with manual identification, is one of the following: [country, electronic, hip-Hop, jazz, latin, pop, punk, reggae, rock, metal, soul].
+* tag_list: Added used to identify tags associated with the artist.
+* biography: The artist's personal biography, collected from the final version of information predating 2011. The information contains line breaks represented as "\n".
+* url: The artist's homepage on the last.fm website.
+
+### ---------------- The Following is lastfm-2k's original Readme ----------------
+
+hetrec2011-lastfm-2k
+====================
+
+-------
+Version
+-------
+
+Version 1.0 (May 2011)
+
+-----------
+Description
+-----------
+
+    This dataset contains social networking, tagging, and music artist listening information 
+    from a set of 2K users from Last.fm online music system.
+    http://www.last.fm 
+    
+    The dataset is released in the framework of the 2nd International Workshop on 
+    Information Heterogeneity and Fusion in Recommender Systems (HetRec 2011) 
+    http://ir.ii.uam.es/hetrec2011 
+    at the 5th ACM Conference on Recommender Systems (RecSys 2011)
+    http://recsys.acm.org/2011 
+
+---------------
+Data statistics
+---------------
+
+     1892 users
+     17632 artists
+      
+     12717 bi-directional user friend relations, i.e. 25434 (user_i, user_j) pairs
+         avg. 13.443 friend relations per user
+         
+     92834 user-listened artist relations, i.e. tuples [user, artist, listeningCount]
+         avg. 49.067 artists most listened by each user
+         avg. 5.265 users who listened each artist
+            
+     11946 tags  
+       
+     186479 tag assignments (tas), i.e. tuples [user, tag, artist]
+         avg. 98.562 tas per user
+         avg. 14.891 tas per artist
+         avg. 18.930 distinct tags used by each user
+         avg. 8.764 distinct tags used for each artist
+
+-----
+Files
+-----
+
+   * artists.dat
+   
+        This file contains information about music artists listened and tagged by the users.
+   
+   * tags.dat
+
+   	This file contains the set of tags available in the dataset.
+
+   * user_artists.dat
+   
+        This file contains the artists listened by each user.
+        
+        It also provides a listening count for each [user, artist] pair.
+
+   * user_taggedartists.dat - user_taggedartists-timestamps.dat
+   
+        These files contain the tag assignments of artists provided by each particular user.
+        
+        They also contain the timestamps when the tag assignments were done.
+   
+   * user_friends.dat
+
+   	These files contain the friend relations between users in the database.
+
+-----------
+Data format
+-----------
+
+   The data is formatted one entry per line as follows (tab separated, "\t"):
+
+   * artists.dat
+   
+        id \t name \t url \t pictureURL
+
+        Example:
+        707	Metallica	http://www.last.fm/music/Metallica	http://userserve-ak.last.fm/serve/252/7560709.jpg
+
+   * tags.dat
+
+        tagID \t tagValue
+        1	metal
+
+   * user_artists.dat
+   
+        userID \t artistID \t weight
+        2	51	13883
+   
+   * user_taggedartists.dat
+  
+        userID \t artistID \t tagID \t day \t month \t year
+        2	52	13	1	4	2009  
+  
+   * user_taggedartists-timestamps.dat
+
+        userID \t artistID \t tagID \t timestamp
+        2	52	13	1238536800000
+
+   * user_friends.dat
+
+        userID \t friendID
+        2	275
+
+-------
+License
+-------
+
+   The users' names and other personal information in Last.fm are not provided in the dataset.
+
+   The data contained in hetrec2011-lastfm-2k.zip is made available for non-commercial use.
+
+   Those interested in using the data in a commercial context should contact Last.fm staff:
+   http://www.lastfm.com/about/contact
+
+----------------
+Acknowledgements
+----------------
+
+   This work was supported by the Spanish Ministry of Science and Innovation (TIN2008-06566-C04-02), 
+   and the Regional Government of Madrid (S2009TIC-1542).
+
+----------
+References
+----------
+
+   When using this dataset you should cite:
+      - Last.fm website, http://www.lastfm.com
+
+   You may also cite HetRec'11 workshop as follows:
+
+   @inproceedings{Cantador:RecSys2011,
+      author = {Cantador, Iv\'{a}n and Brusilovsky, Peter and Kuflik, Tsvi},
+      title = {2nd Workshop on Information Heterogeneity and Fusion in Recommender Systems (HetRec 2011)},
+      booktitle = {Proceedings of the 5th ACM conference on Recommender systems},
+      series = {RecSys 2011},
+      year = {2011},
+      location = {Chicago, IL, USA},
+      publisher = {ACM},
+      address = {New York, NY, USA},
+      keywords = {information heterogeneity, information integration, recommender systems},
+   } 
+
+-------
+Credits
+-------
+
+   This dataset was built by Ignacio Fern�ndez-Tob�as with the collaboration of Iv�n Cantador and Alejandro Bellog�n, 
+   members of the Information Retrieval group at Universidad Autonoma de Madrid (http://ir.ii.uam.es)
+
+-------
+Contact
+-------
+
+   Iv�n Cantador, ivan [dot] cantador [at] uam [dot] es

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TML1M/README.md

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+DESCRIPTION
+================================================================================
+
+Table-Movielens1M extends the original Movielens-1M dataset (https://grouplens.org/datasets/movielens/1m/) with enriched movie data. This new version contains 6040 users, 3883 movies, and 1000209 ratings. Leveraging each MovielensID's corresponding movie page on movielens.org, we further fetch some "time-invariant" metadata for movies, including: Director, Cast, Runtime, Languages, Certificate, Plot, and Url.
+
+We have also prepared embeddings for movies with all-MiniLM-L6-v2 model, see embeddings.npy in zipfile.
+
+The basic task is to classify users' age phase.
+
+ENHANCED MOVIE DATA FORMAT
+================================================================================
+
+movies.csv follows the format outlined below:
+MovielensID,Title,Year,Genre,Director,Cast,Runtime,Languages,Certificate,Plot,Url
+
+- MovielensID: This is the movie's id on the movielens website, inherited from the original Movielens-1M dataset.
+- Title: The title of the movie, sourced from the movielens website.
+- Year: The release year of the movie, sourced from the movielens website.
+- Genre: Movie genres, inherited from the original Movielens-1M dataset.
+- Director: The director's name of the movie, sourced from the movielens website.
+- Cast: The main cast of the movie, sourced from the movielens website.
+- Runtime: The duration of the movie, sourced from the movielens website.
+- Languages: Official language versions of the movie, sourced from the movielens website.
+- Certificate: Movie certificate information, sourced from the movielens website.
+- Plot: A brief summary of the movie's main plot, sourced from the movielens website.
+- Url: The movie's URL on the movielens website.
+
+SOME SPECIAL NOTES
+================================================================================
+
+1. Please note that due to the age of the Movielens-1M dataset, some MovielensID values no longer correspond to actual pages, meaning there is a discrepancy between MovielensID and the actual page ID on the movielens website.
+
+For movies facing this issue, we have extracted information from their actual pages. However, to minimize changes, we have not modified their MovielensID. Here is a list of such movies with their MovielensID and real ID:
+
+   * 3589: Original ID changed to 54605
+   * 557: Original ID changed to 8739
+   * 578: Original ID changed to 6531
+   * 669: Original ID changed to 6918
+   * 863: Original ID changed to 193303
+   * 978: Original ID changed to 4970
+   * 1108: Original ID changed to 6677
+   * 1205: Original ID changed to 4006
+   * 1294: Original ID changed to 5060
+   * 1362: Original ID changed to 4424
+   * 1494: Original ID changed to 6425
+   * 1868: Original ID changed to 6474
+   * 2645: Original ID changed to 5649
+   * 3027: Original ID changed to 203829
+   * 3366: Original ID changed to 6808
+   * 3416: Original ID changed to 6460
+   * 3482: Original ID changed to 72267
+   * 3532: Original ID changed to 5560
+   * 3842: Original ID changed to 5717
+   * 3935: Original ID changed to 6910
+
+1. Additionally, there are some other issues:
+	
+   * Movie with MovielensID 2228 in movielens is missing, so we use information from IMDB.
+   * Movies with MovielensID 1741 and 1758 are duplicates of 1795 and 2563 respectively.
+
+2. Users and Ratings file are followed original format.
+
+
+---------------- The Following is Movielens-1m's original Readme ----------------
+SUMMARY
+================================================================================
+
+These files contain 1,000,209 anonymous ratings of approximately 3,900 movies 
+made by 6,040 MovieLens users who joined MovieLens in 2000.
+
+USAGE LICENSE
+================================================================================
+
+Neither the University of Minnesota nor any of the researchers
+involved can guarantee the correctness of the data, its suitability
+for any particular purpose, or the validity of results based on the
+use of the data set.  The data set may be used for any research
+purposes under the following conditions:
+
+     * The user may not state or imply any endorsement from the
+       University of Minnesota or the GroupLens Research Group.
+
+     * The user must acknowledge the use of the data set in
+       publications resulting from the use of the data set
+       (see below for citation information).
+
+     * The user may not redistribute the data without separate
+       permission.
+
+     * The user may not use this information for any commercial or
+       revenue-bearing purposes without first obtaining permission
+       from a faculty member of the GroupLens Research Project at the
+       University of Minnesota.
+
+If you have any further questions or comments, please contact GroupLens
+<[email protected]>. 
+
+CITATION
+================================================================================
+
+To acknowledge use of the dataset in publications, please cite the following
+paper:
+
+F. Maxwell Harper and Joseph A. Konstan. 2015. The MovieLens Datasets: History
+and Context. ACM Transactions on Interactive Intelligent Systems (TiiS) 5, 4,
+Article 19 (December 2015), 19 pages. DOI=http://dx.doi.org/10.1145/2827872
+
+
+ACKNOWLEDGEMENTS
+================================================================================
+
+Thanks to Shyong Lam and Jon Herlocker for cleaning up and generating the data
+set.
+
+FURTHER INFORMATION ABOUT THE GROUPLENS RESEARCH PROJECT
+================================================================================
+
+The GroupLens Research Project is a research group in the Department of 
+Computer Science and Engineering at the University of Minnesota. Members of 
+the GroupLens Research Project are involved in many research projects related 
+to the fields of information filtering, collaborative filtering, and 
+recommender systems. The project is lead by professors John Riedl and Joseph 
+Konstan. The project began to explore automated collaborative filtering in 
+1992, but is most well known for its world wide trial of an automated 
+collaborative filtering system for Usenet news in 1996. Since then the project 
+has expanded its scope to research overall information filtering solutions, 
+integrating in content-based methods as well as improving current collaborative 
+filtering technology.
+
+Further information on the GroupLens Research project, including research 
+publications, can be found at the following web site:
+        
+        http://www.grouplens.org/
+
+GroupLens Research currently operates a movie recommender based on 
+collaborative filtering:
+
+        http://www.movielens.org/
+
+RATINGS FILE DESCRIPTION
+================================================================================
+
+All ratings are contained in the file "ratings.dat" and are in the
+following format:
+
+UserID::MovieID::Rating::Timestamp
+
+- UserIDs range between 1 and 6040 
+- MovieIDs range between 1 and 3952
+- Ratings are made on a 5-star scale (whole-star ratings only)
+- Timestamp is represented in seconds since the epoch as returned by time(2)
+- Each user has at least 20 ratings
+
+USERS FILE DESCRIPTION
+================================================================================
+
+User information is in the file "users.dat" and is in the following
+format:
+
+UserID::Gender::Age::Occupation::Zip-code
+
+All demographic information is provided voluntarily by the users and is
+not checked for accuracy.  Only users who have provided some demographic
+information are included in this data set.
+
+- Gender is denoted by a "M" for male and "F" for female
+- Age is chosen from the following ranges:
+
+	*  1:  "Under 18"
+	* 18:  "18-24"
+	* 25:  "25-34"
+	* 35:  "35-44"
+	* 45:  "45-49"
+	* 50:  "50-55"
+	* 56:  "56+"
+
+- Occupation is chosen from the following choices:
+
+	*  0:  "other" or not specified
+	*  1:  "academic/educator"
+	*  2:  "artist"
+	*  3:  "clerical/admin"
+	*  4:  "college/grad student"
+	*  5:  "customer service"
+	*  6:  "doctor/health care"
+	*  7:  "executive/managerial"
+	*  8:  "farmer"
+	*  9:  "homemaker"
+	* 10:  "K-12 student"
+	* 11:  "lawyer"
+	* 12:  "programmer"
+	* 13:  "retired"
+	* 14:  "sales/marketing"
+	* 15:  "scientist"
+	* 16:  "self-employed"
+	* 17:  "technician/engineer"
+	* 18:  "tradesman/craftsman"
+	* 19:  "unemployed"
+	* 20:  "writer"
+
+MOVIES FILE DESCRIPTION
+================================================================================
+
+Movie information is in the file "movies.dat" and is in the following
+format:
+
+MovieID::Title::Genres
+
+- Titles are identical to titles provided by the IMDB (including
+year of release)
+- Genres are pipe-separated and are selected from the following genres:
+
+	* Action
+	* Adventure
+	* Animation
+	* Children's
+	* Comedy
+	* Crime
+	* Documentary
+	* Drama
+	* Fantasy
+	* Film-Noir
+	* Horror
+	* Musical
+	* Mystery
+	* Romance
+	* Sci-Fi
+	* Thriller
+	* War
+	* Western
+
+- Some MovieIDs do not correspond to a movie due to accidental duplicate
+entries and/or test entries
+- Movies are mostly entered by hand, so errors and inconsistencies may exist

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