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The dataset generation failed
Error code: DatasetGenerationError
Exception: CastError
Message: Couldn't cast
id: string
authors: string
title: string
categories: string
abstract: string
update_date: timestamp[ns]
tfidf_keywords: list<element: string>
child 0, element: string
metadata_text: string
combined_text: string
-- schema metadata --
pandas: '{"index_columns": [], "column_indexes": [], "columns": [{"name":' + 1156
to
{'id': Value('string'), 'metadata': {'abstract': Value('string'), 'authors': Value('string'), 'categories': Value('string'), 'combined_text': Value('string'), 'id': Value('string'), 'metadata_text': Value('string'), 'tfidf_keywords': List(Value('string')), 'title': Value('string'), 'update_date': Value('timestamp[us]')}}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1404, in compute_config_parquet_and_info_response
fill_builder_info(builder, hf_endpoint=hf_endpoint, hf_token=hf_token, validate=validate)
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 577, in fill_builder_info
) = retry_validate_get_features_num_examples_size_and_compression_ratio(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 496, in retry_validate_get_features_num_examples_size_and_compression_ratio
validate(pf)
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 534, in validate
raise TooBigRowGroupsError(
worker.job_runners.config.parquet_and_info.TooBigRowGroupsError: Parquet file has too big row groups. First row group has 2199879475 which exceeds the limit of 300000000
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1815, in _prepare_split_single
for _, table in generator:
^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 691, in wrapped
for item in generator(*args, **kwargs):
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/parquet/parquet.py", line 106, in _generate_tables
yield f"{file_idx}_{batch_idx}", self._cast_table(pa_table)
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/parquet/parquet.py", line 73, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2272, in table_cast
return cast_table_to_schema(table, schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2218, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
id: string
authors: string
title: string
categories: string
abstract: string
update_date: timestamp[ns]
tfidf_keywords: list<element: string>
child 0, element: string
metadata_text: string
combined_text: string
-- schema metadata --
pandas: '{"index_columns": [], "column_indexes": [], "columns": [{"name":' + 1156
to
{'id': Value('string'), 'metadata': {'abstract': Value('string'), 'authors': Value('string'), 'categories': Value('string'), 'combined_text': Value('string'), 'id': Value('string'), 'metadata_text': Value('string'), 'tfidf_keywords': List(Value('string')), 'title': Value('string'), 'update_date': Value('timestamp[us]')}}
because column names don't match
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1427, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 993, in stream_convert_to_parquet
builder._prepare_split(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1702, in _prepare_split
for job_id, done, content in self._prepare_split_single(
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1858, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
id string | metadata dict |
|---|---|
0704.0047 | {
"abstract": " The intelligent acoustic emission locator is described in Part I, while Part\nII discusses blind source separation, time delay estimation and location of two\nsimultaneously active continuous acoustic emission sources.\n The location of acoustic emission on complicated aircraft frame structures is\n... |
0704.0050 | {
"abstract": " Part I describes an intelligent acoustic emission locator, while Part II\ndiscusses blind source separation, time delay estimation and location of two\ncontinuous acoustic emission sources.\n Acoustic emission (AE) analysis is used for characterization and location of\ndeveloping defects in material... |
0704.0304 | {
"abstract": " This paper discusses the benefits of describing the world as information,\nespecially in the study of the evolution of life and cognition. Traditional\nstudies encounter problems because it is difficult to describe life and\ncognition in terms of matter and energy, since their laws are valid only at ... |
0704.0671 | {
"abstract": " The problem of statistical learning is to construct a predictor of a random\nvariable $Y$ as a function of a related random variable $X$ on the basis of an\ni.i.d. training sample from the joint distribution of $(X,Y)$. Allowable\npredictors are drawn from some specified class, and the goal is to app... |
0704.0954 | {
"abstract": " In a sensor network, in practice, the communication among sensors is subject\nto:(1) errors or failures at random times; (3) costs; and(2) constraints since\nsensors and networks operate under scarce resources, such as power, data rate,\nor communication. The signal-to-noise ratio (SNR) is usually a ... |
0704.0985 | {
"abstract": " Advances in semiconductor technology are contributing to the increasing\ncomplexity in the design of embedded systems. Architectures with novel\ntechniques such as evolvable nature and autonomous behavior have engrossed lot\nof attention. This paper demonstrates conceptually evolvable embedded system... |
0704.1020 | {
"abstract": " The on-line shortest path problem is considered under various models of\npartial monitoring. Given a weighted directed acyclic graph whose edge weights\ncan change in an arbitrary (adversarial) way, a decision maker has to choose in\neach round of a game a path between two distinguished vertices such... |
0704.1028 | {
"abstract": " Ordinal regression is an important type of learning, which has properties of\nboth classification and regression. Here we describe a simple and effective\napproach to adapt a traditional neural network to learn ordinal categories. Our\napproach is a generalization of the perceptron method for ordinal... |
0704.1267 | {
"abstract": " There is a huge amount of historical documents in libraries and in various\nNational Archives that have not been exploited electronically. Although\nautomatic reading of complete pages remains, in most cases, a long-term\nobjective, tasks such as word spotting, text/image alignment, authentication\na... |
0704.1274 | {
"abstract": " This paper uncovers and explores the close relationship between Monte Carlo\nOptimization of a parametrized integral (MCO), Parametric machine-Learning\n(PL), and `blackbox' or `oracle'-based optimization (BO). We make four\ncontributions. First, we prove that MCO is mathematically identical to a bro... |
0704.1394 | {
"abstract": " In these notes we formally describe the functionality of Calculating Valid\nDomains from the BDD representing the solution space of valid configurations.\nThe formalization is largely based on the CLab configuration framework.\n",
"authors": "Tarik Hadzic, Rune Moller Jensen, Henrik Reif Andersen",... |
0704.1409 | {
"abstract": " This paper has been withdrawn by the author. This draft is withdrawn for its\npoor quality in english, unfortunately produced by the author when he was just\nstarting his science route. Look at the ICML version instead:\nhttp://icml2008.cs.helsinki.fi/papers/111.pdf\n",
"authors": "Yao HengShuai",
... |
0704.1675 | {
"abstract": " Information integration applications, such as mediators or mashups, that\nrequire access to information resources currently rely on users manually\ndiscovering and integrating them in the application. Manual resource discovery\nis a slow process, requiring the user to sift through results obtained vi... |
0704.1676 | {
"abstract": " The social media site Flickr allows users to upload their photos, annotate\nthem with tags, submit them to groups, and also to form social networks by\nadding other users as contacts. Flickr offers multiple ways of browsing or\nsearching it. One option is tag search, which returns all images tagged w... |
0704.1783 | {
"abstract": " We present a formal model to represent and solve the unicast/multicast\nrouting problem in networks with Quality of Service (QoS) requirements. To\nattain this, first we translate the network adapting it to a weighted graph\n(unicast) or and-or graph (multicast), where the weight on a connector\ncorr... |
0704.2010 | {
"abstract": " Motivation: Profile hidden Markov Models (pHMMs) are a popular and very\nuseful tool in the detection of the remote homologue protein families.\nUnfortunately, their performance is not always satisfactory when proteins are\nin the 'twilight zone'. We present HMMER-STRUCT, a model construction algorit... |
0704.2083 | {
"abstract": " In this paper Arabic was investigated from the speech recognition problem\npoint of view. We propose a novel approach to build an Arabic Automated Speech\nRecognition System (ASR). This system is based on the open source CMU Sphinx-4,\nfrom the Carnegie Mellon University. CMU Sphinx is a large-vocabu... |
0704.2092 | {
"abstract": " We consider inapproximability of the correlation clustering problem defined\nas follows: Given a graph $G = (V,E)$ where each edge is labeled either \"+\"\n(similar) or \"-\" (dissimilar), correlation clustering seeks to partition the\nvertices into clusters so that the number of pairs correctly (res... |
0704.2201 | {
"abstract": " In this paper we present the creation of an Arabic version of Automated\nSpeech Recognition System (ASR). This system is based on the open source\nSphinx-4, from the Carnegie Mellon University. Which is a speech recognition\nsystem based on discrete hidden Markov models (HMMs). We investigate the\nch... |
0704.2644 | {
"abstract": " The problem of joint universal source coding and modeling, treated in the\ncontext of lossless codes by Rissanen, was recently generalized to fixed-rate\nlossy coding of finitely parametrized continuous-alphabet i.i.d. sources. We\nextend these results to variable-rate lossy block coding of stationar... |
0704.2668 | {
"abstract": " We introduce a framework for filtering features that employs the\nHilbert-Schmidt Independence Criterion (HSIC) as a measure of dependence\nbetween the features and the labels. The key idea is that good features should\nmaximise such dependence. Feature selection for various supervised learning\nprob... |
0704.3157 | {
"abstract": " This paper considers the problem of reasoning on massive amounts of (possibly\ndistributed) data. Presently, existing proposals show some limitations: {\\em\n(i)} the quantity of data that can be handled contemporarily is limited, due to\nthe fact that reasoning is generally carried out in main-memor... |
0704.3359 | {
"abstract": " Web page ranking and collaborative filtering require the optimization of\nsophisticated performance measures. Current Support Vector approaches are\nunable to optimize them directly and focus on pairwise comparisons instead. We\npresent a new approach which allows direct optimization of the relevant ... |
0704.3395 | {
"abstract": " This article presents a model of general-purpose computing on a semantic\nnetwork substrate. The concepts presented are applicable to any semantic\nnetwork representation. However, due to the standards and technological\ninfrastructure devoted to the Semantic Web effort, this article is presented\nfr... |
0704.3433 | {
"abstract": " This paper proposes an approach to training rough set models using Bayesian\nframework trained using Markov Chain Monte Carlo (MCMC) method. The prior\nprobabilities are constructed from the prior knowledge that good rough set\nmodels have fewer rules. Markov Chain Monte Carlo sampling is conducted t... |
0704.3453 | {
"abstract": " One of the major problems in computational biology is the inability of\nexisting classification models to incorporate expanding and new domain\nknowledge. This problem of static classification models is addressed in this\npaper by the introduction of incremental learning for problems in\nbioinformati... |
0704.3515 | {
"abstract": " Noise, corruptions and variations in face images can seriously hurt the\nperformance of face recognition systems. To make such systems robust,\nmulticlass neuralnetwork classifiers capable of learning from noisy data have\nbeen suggested. However on large face data sets such systems cannot provide th... |
0704.3635 | {
"abstract": " Many techniques for handling missing data have been proposed in the\nliterature. Most of these techniques are overly complex. This paper explores an\nimputation technique based on rough set computations. In this paper,\ncharacteristic relations are introduced to describe incompletely specified\ndecis... |
0704.3662 | {
"abstract": " In this paper, we propose an automated evaluation metric for text entry. We\nalso consider possible improvements to existing text entry evaluation metrics,\nsuch as the minimum string distance error rate, keystrokes per character, cost\nper correction, and a unified approach proposed by MacKenzie, so... |
0704.3665 | {
"abstract": " Intelligent Input Methods (IM) are essential for making text entries in many\nEast Asian scripts, but their application to other languages has not been fully\nexplored. This paper discusses how such tools can contribute to the development\nof computer processing of other oriental languages. We propos... |
0704.3708 | {
"abstract": " This paper includes a reflection on the role of networks in the study of\nEnglish language acquisition, as well as a collection of practical criteria to\nannotate free-speech corpora from children utterances. At the theoretical\nlevel, the main claim of this paper is that syntactic networks should be... |
0704.3886 | {
"abstract": " We argue for a compositional semantics grounded in a strongly typed ontology\nthat reflects our commonsense view of the world and the way we talk about it.\nAssuming such a structure we show that the semantics of various natural\nlanguage phenomena may become nearly trivial.\n",
"authors": "Walid S... |
0704.3905 | {
"abstract": " Evolutionary Learning proceeds by evolving a population of classifiers, from\nwhich it generally returns (with some notable exceptions) the single\nbest-of-run classifier as final result. In the meanwhile, Ensemble Learning,\none of the most efficient approaches in supervised Machine Learning for the... |
0705.0025 | {
"abstract": " When will the Internet become aware of itself? In this note the problem is\napproached by asking an alternative question: Can the Internet cope with\nstress? By extrapolating the psychological difference between coping and\ndefense mechanisms a distributed software experiment is outlined which could\... |
0705.0197 | {
"abstract": " Gaussian mixture models (GMM) and support vector machines (SVM) are\nintroduced to classify faults in a population of cylindrical shells. The\nproposed procedures are tested on a population of 20 cylindrical shells and\ntheir performance is compared to the procedure, which uses multi-layer\nperceptro... |
0705.0199 | {
"abstract": " The Parameter-Less Self-Organizing Map (PLSOM) is a new neural network\nalgorithm based on the Self-Organizing Map (SOM). It eliminates the need for a\nlearning rate and annealing schemes for learning rate and neighbourhood size.\nWe discuss the relative performance of the PLSOM and the SOM and demon... |
0705.0214 | {
"abstract": " We present a novel approach for the derivation of PDE modeling\ncurvature-driven flows for matrix-valued data. This approach is based on the\nRiemannian geometry of the manifold of Symmetric Positive Definite Matrices\nPos(n).\n",
"authors": "Mourad Zerai, Maher Moakher",
"categories": "cs.CV",
... |
0705.0449 | {
"abstract": " We propose a new algorithm to the problem of polygonal curve approximation\nbased on a multiresolution approach. This algorithm is suboptimal but still\nmaintains some optimality between successive levels of resolution using dynamic\nprogramming. We show theoretically and experimentally that this alg... |
0705.0462 | {
"abstract": " The description of resources in game semantics has never achieved the\nsimplicity and precision of linear logic, because of a misleading conception:\nthe belief that linear logic is more primitive than game semantics. We advocate\ninstead the contrary: that game semantics is conceptually more primiti... |
0705.0588 | {
"abstract": " One way of getting a better view of data is using frequent patterns. In this\npaper frequent patterns are subsets that occur a minimal number of times in a\nstream of itemsets. However, the discovery of frequent patterns in streams has\nalways been problematic. Because streams are potentially endless... |
0705.0593 | {
"abstract": " Mining frequent subgraphs is an area of research where we have a given set of\ngraphs (each graph can be seen as a transaction), and we search for (connected)\nsubgraphs contained in many of these graphs. In this work we will discuss\ntechniques used in our framework Lattice2SAR for mining and analys... |
0705.0693 | {
"abstract": " The act of bluffing confounds game designers to this day. The very nature of\nbluffing is even open for debate, adding further complication to the process of\ncreating intelligent virtual players that can bluff, and hence play,\nrealistically. Through the use of intelligent, learning agents, and care... |
0705.0734 | {
"abstract": " The semiring-based constraint satisfaction problems (semiring CSPs), proposed\nby Bistarelli, Montanari and Rossi \\cite{BMR97}, is a very general framework of\nsoft constraints. In this paper we propose an abstraction scheme for soft\nconstraints that uses semiring homomorphism. To find optimal solu... |
0705.0760 | {
"abstract": " Max-product belief propagation is a local, iterative algorithm to find the\nmode/MAP estimate of a probability distribution. While it has been successfully\nemployed in a wide variety of applications, there are relatively few\ntheoretical guarantees of convergence and correctness for general loopy gr... |
0705.0761 | {
"abstract": " This paper proposes a neuro-rough model based on multi-layered perceptron and\nrough set. The neuro-rough model is then tested on modelling the risk of HIV\nfrom demographic data. The model is formulated using Bayesian framework and\ntrained using Monte Carlo method and Metropolis criterion. When the... |
0705.0781 | {
"abstract": " This paper presents deformable templates as a tool for segmentation and\nlocalization of biological structures in medical images. Structures are\nrepresented by a prototype template, combined with a parametric warp mapping\nused to deform the original shape. The localization procedure is achieved usi... |
0705.0828 | {
"abstract": " Nuclear medicine (NM) images inherently suffer from large amounts of noise\nand blur. The purpose of this research is to reduce the noise and blur while\nmaintaining image integrity for improved diagnosis. The proposed solution is to\nincrease image quality after the standard pre- and post-processing... |
0705.0952 | {
"abstract": " This paper explores a comparative study of both the linear and kernel\nimplementations of three of the most popular Appearance-based Face Recognition\nprojection classes, these being the methodologies of Principal Component\nAnalysis, Linear Discriminant Analysis and Independent Component Analysis. T... |
0705.0969 | {
"abstract": " Water plays a pivotal role in many physical processes, and most importantly\nin sustaining human life, animal life and plant life. Water supply entities\ntherefore have the responsibility to supply clean and safe water at the rate\nrequired by the consumer. It is therefore necessary to implement mech... |
0705.1031 | {
"abstract": " An ensemble based approach for dealing with missing data, without predicting\nor imputing the missing values is proposed. This technique is suitable for\nonline operations of neural networks and as a result, is used for online\ncondition monitoring. The proposed technique is tested in both classifica... |
0705.1110 | {
"abstract": " In many applications it will be useful to know those patterns that occur with\na balanced interval, e.g., a certain combination of phone numbers are called\nalmost every Friday or a group of products are sold a lot on Tuesday and\nThursday.\n In previous work we proposed a new measure of support (th... |
0705.1161 | {
"abstract": " There have been a number of prior attempts to theoretically justify the\neffectiveness of the inverse document frequency (IDF). Those that take as their\nstarting point Robertson and Sparck Jones's probabilistic model are based on\nstrong or complex assumptions. We show that a more intuitively plausi... |
0705.1209 | {
"abstract": " Militarised conflict is one of the risks that have a significant impact on\nsociety. Militarised Interstate Dispute (MID) is defined as an outcome of\ninterstate interactions, which result on either peace or conflict. Effective\nprediction of the possibility of conflict between states is an important... |
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