ACL-Fig: A Dataset for Scientific Figure Classification
Paper • 2301.12293 • Published
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The dataset generation failed because of a cast error
Error code: DatasetGenerationCastError
Exception: DatasetGenerationCastError
Message: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 5 new columns ({'caption_length', 'image_id', 'arXiv_id', 'caption_id', 'categories'}) and 2 missing columns ({'images', 'annotations'}).
This happened while the json dataset builder was generating data using
hf://datasets/CrowdAILab/scicap/train-metadata.json (at revision 3a6b8bc9d9dd204f453107b1339a6e9dce20b51b)
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback: Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1871, in _prepare_split_single
writer.write_table(table)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 643, in write_table
pa_table = table_cast(pa_table, self._schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2293, in table_cast
return cast_table_to_schema(table, schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2241, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
image_id: int64
caption_id: int64
caption_length: int64
arXiv_id: double
categories: string
-- schema metadata --
pandas: '{"index_columns": [], "column_indexes": [], "columns": [{"name":' + 711
to
{'images': {'figure_type': Value(dtype='string', id=None), 'file_name': Value(dtype='string', id=None), 'id': Value(dtype='int64', id=None), 'ocr': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)}, 'annotations': {'caption': Value(dtype='string', id=None), 'caption_no_index': Value(dtype='string', id=None), 'id': Value(dtype='int64', id=None), 'image_id': Value(dtype='int64', id=None), 'mention': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'paragraph': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)}}
because column names don't match
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1431, 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 992, in stream_convert_to_parquet
builder._prepare_split(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1742, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1873, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 5 new columns ({'caption_length', 'image_id', 'arXiv_id', 'caption_id', 'categories'}) and 2 missing columns ({'images', 'annotations'}).
This happened while the json dataset builder was generating data using
hf://datasets/CrowdAILab/scicap/train-metadata.json (at revision 3a6b8bc9d9dd204f453107b1339a6e9dce20b51b)
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
images dict | annotations dict |
|---|---|
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"caption": "Figure 1: Execution time of LDA, MC and HMM on data sets of different sizes. HMM achieves a smaller execution time than LDA but greater than MC.",
"caption_no_index": "Execution time of LDA, MC and HMM on data sets of different sizes. HMM achieves a smaller execution time than LDA but greater than MC.... |
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"caption": "Figure 6: Illustration of the number of websites, webpages and bitext harvested in an unsupervised manner for English-Hindi. other refers to bitext in a language pair other than English-Hindi and unfertile refers to entry points that did not harvest any bitext.",
"caption_no_index": "Illustration of t... |
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"caption": "Figure 2: Step 2: Cause candidate extraction using QA.",
"caption_no_index": "Step : Cause candidate extraction using QA.",
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[
"We exploited embeddings from multi-qa-mpnetbase-dot-v1 5 , a model designed for semantic search to compute the dot... |
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"caption": "Figure 1: ROC curves",
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[
"Each Figure 1: ROC curves pair of synsets (S t , S h ) is an oriented entailment relation between S t and S h ."
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"caption": "Figure 4: Relation between number of model ensembles and BLEU score on ASPEC En-Ja.",
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"caption": "Figure 2: True intent distribution of the MultiWOZ dataset – Vertical axis denotes percentage of dialogs per intent",
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"caption": "Figure 2: An SFST for a post-pivot T-bounded and Rbounded copy relation.",
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"caption": "Figure 1: The configuration of the conventional CLQA system.",
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"Figure 1 shows the configuration of our previous English-to-Japanese cross-lingual QA system, which h... |
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"Overview of a GBS-QA construction process follows Figure 2.",
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"caption": "Figure 1: Illustration of link relationship of seed websites and related websites, with associated∑ Linkout, ∑ PageRank and ∑ WeightedPageRank in square brackets and with arrows to indicate outgoing links from a seed website to others.",
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"caption": "Figure 1 : Règles générales du modèle et relations temporelles pour le texte (6)",
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"caption": "Figure 5: Mean proportional gaze per referring expression for speaker and listeners. a) The speaker’s gaze during the time of the reference. b) The combined listeners’ gaze during the speaker’s referring expression. c) The speaker’s gaze during the 1 second period before the reference. d) The combined l... |
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"caption": "Figure 5: Decisions of the RL policy (in blue) vs. the baseline policy (in red).",
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"caption": "Figure 2: The overall architecture of our system.",
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"As illustrated in Figure 2, the architecture of our system consists of a candidates generation stage, a weighted merging st... |
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"caption": "Figure 2: Lineplot of GQD against temperature for all the five different language families. The trendlines are drawn using LOESS smoothing.",
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"caption": "Figure 1: Architecture for building Web corpora",
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"caption": "Figure 4: Each line represents experiments with a set number of topics and variable amounts of smoothing on the SEMCOR corpus. The random baseline is at the bottom of the graph, and adding topics improves accuracy. As smoothing increases, the prior (based on token frequency) becomes stronger. Accuracy i... |
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"caption": "Figure 8. Training parameters for TermMine.",
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"We performed some experiments with the different values of various system parameters, the resulting parameters are shown in Figure 8."
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"caption": "Figure 2: The toxicity and induction success rate of different kinds of contexts. profanity/0.78 means the averaged toxicity score for category profanity is 0.78.",
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"caption": "Figure 2: Latent structure underlying the mention ranking and the antecedent tree approach. The black nodes and arcs represent one substructure for the mention ranking approach.",
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"caption": "Figure 7. Preference ratio for the (1) baseline system, (2) MDL parameter optimized speech synthesis system and (3) further backing-off and splitting system.",
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"caption": "Figure 3: A sample sentence annotated according to our hybrid dependency-constituency grammar model. See Figure 6 and Table 2 for its linearization and enhanced UD representation.",
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"caption": "Figure 5: Sample XML output of the POS tagger web service",
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"caption": "Figure 2: Fraction of words less than a given word frequency.",
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"caption": "Figure 2: The morphological analysis stage.",
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"caption": "Figure 2: Comparison between inter-annotator agreement (red line) and three baselines: exact match (blue), fuzzy match (green), and fuzzy + synonyms (orange).",
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"caption": "Figure 3: Distribution of inductively derived impact categories and sub-categories",
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"caption": "Figure 3: Structural features’ performance using the SVM classifier evaluated on the test set.",
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"caption": "Figure 5: Literal RDF translation of a GrAF Propbank annotation representation from (Ide & Suderman 2007)",
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"caption": "Figure 1: Age bands over number of complex words",
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"caption": "Figure 1: The VERBMOBIL Architecture5",
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"caption": "Figure 2: Active learning performance for the comparable corpora classification in Urdu-English language-pair",
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"caption": "Figure 1: Using the vector offset method to solve the analogy task (Mikolov et al., 2013c).",
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"caption": "Figure 2: Data ablation study. Reasonable performance can be obtained with titles only but full articles are crucial for achieving best performance.",
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"caption": "Figure 1: Traditional Pipeline-based Strategy for Heterogeneous POS Tagging",
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"caption": "Figure 9. The syllable accuracy rates of LI system with different maximum number of the Gaussian components in a Gaussian mixture per state using MCS.",
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"caption": "Figure 5: Dendrogram of vectors of measures correlations on a dataset. The height of the bar indicates the distance between vectors or groups of vectors. Postfixes ‘p’ and ‘t’ denote the datasets for PG and TST tasks, respectively.",
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"caption": "Figure 1: Graphical representation of predicate-argument dependencies for the sentence The person who officials say stole millions.",
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"caption": "Figure 3:Extensionalearning curves on as percentage of the training set.",
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"caption": "Figure 3: The decision pipeline for comparative question filtering.",
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"caption": "Figure 1: Length of identity chains and number of their bridging markables with Spearman’s ρ = 0.6595",
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"caption": "Figure 8. Time path of cosine similarities using continuous space model with word pairs (example 1).",
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"caption": "Figure 2: Pseudocode for the generation process. The generated text w is a deterministic function of the decisions.",
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"caption": "Figure 2: GHKM tree equivalent of example translation object. The light gray nodes are rule nodes of the GHKM tree.",
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"caption": "Figure 1: The general TectoMT architecture (from (Popel and Žabokrtský, 2010, :298)).",
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"caption": "Figure 1: PAR-related concepts from C0040426 (Tooth structure). We highlight multiple paths,",
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"caption": "Figure 3: Coordination in TOROT (reproduced with permission from Berdicevskis and Eckhoff 2015)",
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"caption": "Figure 4: Input and output for our automatic headline generation system.",
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"caption": "Figure 3. Relative frequencies of lengths of projective (black) and non-projective (grey) sentences in the Czech treebank.",
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"caption": "Figure 1: Comparison of the time taken to train the topic models of HDP-WSI and HCA-WSI for each lemma in the BNC dataset. For each method, one data point is plotted per lemma.",
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"caption": "Figure 4: Log-likelihood differences (MDIFF) between pairs of sentences for individual samples by five models.",
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"caption": "Figure 1: PRADO Model Architecture",
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"caption": "Figure 4: Shared Concepts in both languages",
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"caption": "Figure 2: The output of the parsing for sentence “Ama hiçbir şey söylemedim ki sizlere.” (in English, “But I did not say anything to you.”)",
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"caption": "Figure 2: An example of labeled sequence linearization.",
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"caption": "Figure 2: Example of TOOLBOX format (Stoll et al., Unpublished)",
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"caption": "Figure 5: Synonymous strategy across one paths, with previously inferred translations",
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"caption": "FIGURE 2: A domain Goal frame from the Iraq question",
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"caption": "Figure 1: The oracle translation for this Arabic VOS sentence would be pruned during search using typical distortion parameters. The Arabic phrases read right-to-left, but we have ordered the sentence from left-to-right in order to clearly illustrate the re-ordering problem.",
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"caption": "Figure 1: Data Processing Scheme",
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"caption": "Figure 1: Class hierarchy of our conceptual ontology for modeling software requirements.",
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"caption": "Figure 2: Understanding Error Rate vs. utterance rejection on the development and test corpora",
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"caption": "Figure 1: Sabarimala Gaussian Curves measuring polarity percentage",
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"mention": [
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"caption": "Figure 2: Differences for alternative unsupervised learners across numbers of clusters.",
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"caption": "Figure 3: Predictive performance (XEC −XEL(T ),C) and average confound correlation (V/rpb) of lexicons generated via our proposed algorithms and a variety of methods in current use. The numbers to the right of each bar indicate the number of winning bootstrap trials.",
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"caption": "Figure 2: Exploration of parser state space using best-first search and error states. States are numbered according to the order in which they become the parser’s current state. The local action classifier is trained with four classes: the three valid actions (represented as Sh for shift, L for reduce-l... |
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"caption": "Figure 9: Texts scored using the two stylistic dimension obtained in our factor analysis",
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"caption": "Figure 7: The acceleration record of LDC term.The LDC term produces different accelerations for different model outputs. The x-axis is pj and the y-axis is the corresponding L ′′ pk value. δ ∈ [0.0, 0.1, ...].",
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"caption": "Figure 2 Results of the best classifier (lex + sem + synt) on different irony types.",
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"caption": "Figure 4: BLEU score on the Finnish Dev set (GBM) with different values for the 1/2σ2 regularization weight. To enable comparable results, the other hyperparameter (length) is kept fixed.",
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"Following Zhang (2004), we have developed a baseline self-bootstrapping procedure, which keeps augmenting the labeled data by employing the mode... |
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"caption": "Figure 5. System Processing Time",
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"caption": "Figure 1: Average scores of accuracy and macro F1 from FixMatch and our method for different sizes of labeled data on the AG News dataset.",
"caption_no_index": "Average scores of accuracy and macro F from FixMatch and our method for different sizes of labeled data on the AG News dataset.",
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"caption": "Figure 3: A final layer averaging 20 model predictions",
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"mention": [
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"This adds a final layer to the model, illustrated for 20 models in Figure 3."
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"caption": "Figure 1: TER distribution in the APE 2022 English-Marathi test set.",
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"mention": [
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"caption": "Fig. 2: The schematic diagram of the proposed Chinese parser.",
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"The block diagram of traditional Chinese parser is shown in Fig. 2."
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"caption": "Figure 2: Example of clinical concept “fever” and its important relations (note the diagram is simplified).",
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"caption": "Figure 7: An LF tree illustrating polarity reversal under→.",
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"Consider the example of Figure 7.",
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"caption": "Figure 4. Intensifiers on average modify semantically more similar adjectives compared to control adverbs.",
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End of preview.
New Released
We have recently released the ground truth for both the public and hidden test sets of the 3rd Scientific Figure Captioning (SciCap) Challenge. Feel free to download them.
The 1st Scientific Figure Captioning (SciCap) Challenge 📖📊
Welcome to the 1st Scientific Figure Captioning (SciCap) Challenge! 🎉 This dataset contains approximately 400,000 scientific figure images sourced from various arXiv papers, along with their captions and relevant paragraphs. The challenge is open to researchers, AI/NLP/CV practitioners, and anyone interested in developing computational models for generating textual descriptions for visuals. 💻
Challenge homepage 🏠
Challenge Overview 🌟
The SciCap Challenge will be hosted at ICCV 2023 in the 5th Workshop on Closing the Loop Between Vision and Language (October 2-3, Paris, France) 🇫🇷. Participants are required to submit the generated captions for a hidden test set for evaluation.
The challenge is divided into two phases:
- Test Phase (2.5 months): Use the provided training set, validation set, and public test set to build and test the models.
- Challenge Phase (2 weeks): Submit results for a hidden test set that will be released before the submission deadline.
Winning teams will be determined based on their results for the hidden test set 🏆. Details of the event's important dates, prizes, and judging criteria are listed on the challenge homepage.
Dataset Overview and Download 📚
The SciCap dataset contains an expanded version of the original SciCap dataset, and includes figures and captions from arXiv papers in eight categories: Computer Science, Economics, Electrical Engineering and Systems Science, Mathematics, Physics, Quantitative Biology, Quantitative Finance, and Statistics 📊. Additionally, it covers data from ACL Anthology papers ACL-Fig.
You can download the dataset using the following command:
from huggingface_hub import snapshot_download
snapshot_download(repo_id="CrowdAILab/scicap", repo_type='dataset')
Merge all image split files into one 🧩
zip -F img-split.zip --out img.zip
The dataset schema is similar to the mscoco dataset:
- images: two separated folders - arXiv and acl figures 📁
- annotations: JSON files containing text information (filename, image id, figure type, OCR, and mapped image id, captions, normalized captions, paragraphs, and mentions) 📝
Evaluation and Submission 📩
You have to submit your generated captions in JSON format as shown below:
[
{
"image_id": int,
"caption": "PREDICTED CAPTION STRING"
},
{
"image_id": int,
"caption": "PREDICTED CAPTION STRING"
}
...
]
Submit your results using this challenge link 🔗. Participants must register on Eval.AI to access the leaderboard and submit results.
Please note: Participants should not use the original captions from the arXiv papers (termed "gold data") as input for their systems ⚠️.
Technical Report Submission 🗒️
All participating teams must submit a 2-4 page technical report detailing their system, adhering to the ICCV 2023 paper template 📄. Teams have the option to submit their reports to either the archival or non-archival tracks of the 5th Workshop on Closing the Loop Between Vision and Language.
Good luck with your participation in the 1st SciCap Challenge! 🍀🎊
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