Below is a list of VELD repositories and relevant metadata. Until a full platform is developed, this README serves as the pragmatic aggregation point.

data velds

code velds

chain velds

topic vocab

content vocab

file_type vocab

• https://github.com/veldhub/veld_data__akp_ner_linkedcat
  • linkedcat/veld.yaml
    • valid: True
    • repo is contained as output veld in:
      • https://github.com/veldhub/veld_chain__akp_ner_inference
    • metadata:
      • description: Prefered dataset is not this one, but linkedcat2! This dataset was created by applying a custom trained SpaCy NER model an APIS / ÖBL data, on data set 'linkedcat2' at our solr index. The csv file is split into id column, character start index of recognized entity, character end index of entity, label of entity type, and a small context window.
      • topic: NLP, Named Entity Recognition
      • file_type: csv
      • content: NER data, inferenced NLP data
  • linkedcat2/veld.yaml
    • valid: True
    • repo is contained as output veld in:
      • https://github.com/veldhub/veld_chain__akp_ner_inference
    • metadata:
      • description: Prefered dataset is this one, not linkedcat! This dataset was created by applying a custom trained SpaCy NER model an APIS / ÖBL data, on data set 'linkedcat2' at our solr index. The csv file is split into id column, character start index of recognized entity, character end index of entity, label of entity type, and a small context window.
      • topic: NLP, Named Entity Recognition
      • file_type: csv
      • content: NER data, inferenced NLP data
• https://github.com/veldhub/veld_data__amc_we_training_data
  • 203_vert_rftt_inhalt_nodup/veld.yaml
    • valid: True
    • repo is contained as input veld in:
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__amc
    • repo is contained as output veld in:
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__amc
    • metadata:
      • description: Entire AMC: no metadata, only text, one sentence per line; Created by Hannes Pirker.
      • topic: NLP
      • file_type: txt
      • content: raw text, newspaper texts
  • 203_vert_rftt_inhalt_nodup__uniq/veld.yaml
    • valid: True
    • repo is contained as input veld in:
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__amc
    • repo is contained as output veld in:
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__amc
    • metadata:
      • description: Entire AMC: no metadata, only text, one sentence per line, each sentence made unique by ordering AMC sentences alphabetically and removing dupcliates; Created by Hannes Pirker.
      • topic: NLP
      • file_type: txt
      • content: raw text, newspaper texts
  • 203_vert_rftt_inhalt_nodup__uniq__stripped/veld.yaml
    • valid: True
    • repo is contained as input veld in:
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__amc
    • repo is contained as output veld in:
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__amc
    • metadata:
      • description: Entire AMC: unique sentence data, cleaned from non-alphanumeric junk before 'A' (anything before line number 54,993) and after 'Z' (anything after line number 521,781,020)
      • topic: NLP
      • file_type: txt
      • content: raw text, newspaper texts
  • 203_vert_rftt_inhalt_nodup__uniq__stripped__lowercased/veld.yaml
    • valid: True
    • repo is contained as input veld in:
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__amc
    • repo is contained as output veld in:
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__amc
    • metadata:
      • description: Entire AMC: unique sentences, stripped from non-alphanumeric lines, lowercased.
      • topic: NLP
      • file_type: txt
      • content: raw text, newspaper texts
  • 203_vert_rftt_inhalt_nodup__uniq__stripped__lowercased__punctuation_removed/veld.yaml
    • valid: True
    • repo is contained as input veld in:
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__amc
    • repo is contained as output veld in:
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__amc
    • metadata:
      • description: Entire AMC: unique sentences, stripped from non-alphanumeric lines, lowercased, punctuation removed.
      • topic: NLP
      • file_type: txt
      • content: raw text, newspaper texts
  • 203_vert_rftt_inhalt_nodup__uniq__stripped__lowercased__punctuation_removed__cleaned/veld.yaml
    • valid: True
    • repo is contained as input veld in:
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__amc
    • repo is contained as output veld in:
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__amc
    • metadata:
      • description: Entire AMC: unique sentences, stripped from non-alphanumeric lines, lowercased, punctuation removed, removed sentences with too many non-alphanumeric characters.
      • topic: NLP
      • file_type: txt
      • content: raw text, newspaper texts
  • 203_vert_rftt_inhalt_nodup__uniq__stripped__sampled/veld.yaml
    • valid: True
    • repo is contained as input veld in:
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__amc
    • repo is contained as output veld in:
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__amc
    • metadata:
      • description: 10% AMC: stripped from non-alphanumeric lines, 10% sampled.
      • topic: NLP
      • file_type: txt
      • content: raw text, newspaper texts
  • 203_vert_rftt_inhalt_nodup__uniq__stripped__sampled__lowercased/veld.yaml
    • valid: True
    • repo is contained as input veld in:
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__amc
    • repo is contained as output veld in:
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__amc
    • metadata:
      • description: 10% AMC: stripped from non-alphanumeric lines, 10% sampled, lowercased.
      • topic: NLP
      • file_type: txt
      • content: raw text, newspaper texts
  • 203_vert_rftt_inhalt_nodup__uniq__stripped__sampled__lowercased__punctuation_removed/veld.yaml
    • valid: True
    • repo is contained as input veld in:
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__amc
    • repo is contained as output veld in:
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__amc
    • metadata:
      • description: 10% AMC: stripped from non-alphanumeric lines, 10% sampled, lowercased, punctuation removed.
      • topic: NLP
      • file_type: txt
      • content: raw text, newspaper texts
  • 203_vert_rftt_inhalt_nodup__uniq__stripped__sampled__lowercased__punctuation_removed__cleaned/veld.yaml
    • valid: True
    • repo is contained as input veld in:
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__amc
    • repo is contained as output veld in:
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__amc
    • metadata:
      • description: 10% AMC: stripped from non-alphanumeric lines, 10% sampled, lowercased, punctuation removed, removed sentences with too many non-alphanumeric characters.
      • topic: NLP
      • file_type: txt
      • content: raw text, newspaper texts
• https://github.com/veldhub/veld_data__apis_oebl__ner_gold
  • data_cleaned/veld.yaml
    • valid: True
    • repo is contained as input veld in:
      • https://github.com/veldhub/veld_chain__train_spacy_apis_ner
    • repo is contained as output veld in:
      • https://github.com/veldhub/veld_chain__apis_ner_transform_to_gold
    • metadata:
      • description: Overlapping entities are removed, index offsets corrected, and duplicates removed. Also texts without any entities are removed too, since it's not known if they don't contain any entities (which often is not true; quite a few of them contain entities) or if the annotators simply didn't go through them (which is more likely, hence they were removed). In the original uncleaned data, some entity types are suffixed with numbers (e.g. PER-1337). These were used for identifying entities in a project context, but are probably of less use for NER NLP training. This dataset keeps the identifiers.
      • topic: NLP, Named Entity Recognition
      • file_type: json
      • content: gold data, NER gold data, NLP gold data
  • data_cleaned_simplified/veld.yaml
    • valid: True
    • repo is contained as input veld in:
      • https://github.com/veldhub/veld_chain__train_spacy_apis_ner
    • repo is contained as output veld in:
      • https://github.com/veldhub/veld_chain__apis_ner_transform_to_gold
    • metadata:
      • description: Same as the cleaned data, but with simplified entities (e.g. PER instead of PER-1337). Probably it's best to use this data set for NER training.
      • topic: NLP, Named Entity Recognition
      • file_type: json
      • content: gold data, NER gold data, NLP gold data
  • data_uncleaned/veld.yaml
    • valid: True
    • repo is contained as input veld in:
      • https://github.com/veldhub/veld_chain__train_spacy_apis_ner
    • repo is contained as output veld in:
      • https://github.com/veldhub/veld_chain__apis_ner_transform_to_gold
    • metadata:
      • description: The original, but united, data coming from APIS / ÖBL.
      • topic: NLP, Named Entity Recognition
      • file_type: json
      • content: gold data, NER gold data, NLP gold data
• https://github.com/veldhub/veld_data__apis_spacy_ner_models
  • m1/model-best/veld.yaml
    • valid: True
    • repo is contained as input veld in:
      • https://github.com/veldhub/veld_chain__akp_ner_inference
      • https://github.com/veldhub/veld_chain__train_spacy_apis_ner
    • repo is contained as output veld in:
      • https://github.com/veldhub/veld_chain__train_spacy_apis_ner
    • metadata:
      • file_type: spaCy model
      • content: spaCy model, NLP model
  • m2/model-best/veld.yaml
    • valid: True
    • repo is contained as input veld in:
      • https://github.com/veldhub/veld_chain__akp_ner_inference
      • https://github.com/veldhub/veld_chain__train_spacy_apis_ner
    • repo is contained as output veld in:
      • https://github.com/veldhub/veld_chain__train_spacy_apis_ner
    • metadata:
      • file_type: spaCy model
      • content: spaCy model, NLP model
• https://github.com/veldhub/veld_data__demo_inference_input_ts-vienna-2024
  • veld.yaml
    • valid: True
    • repo is contained as input veld in:
      • https://github.com/veldhub/veld_chain__demo_udipe_ts-vienna-2024
    • metadata:
      • description: A single txt file, used as inference input to a self-trained updipe model as a demonstration
      • topic: NLP, Universal Dependencies
      • file_type: txt
      • content: raw text
• https://github.com/veldhub/veld_data__demo_train_data_ts-vienna-2024
  • veld.yaml
    • valid: True
    • repo is contained as input veld in:
      • https://github.com/veldhub/veld_chain__demo_udipe_ts-vienna-2024
    • metadata:
      • description: A single conllu file, used to train a updipe model as a demonstration
      • topic: NLP, Universal Dependencies
      • file_type: conllu
      • content: linguistically enriched text, tokenized text, lemmatized text
• https://github.com/veldhub/veld_data__eltec_conllu_stats
  • veld.yaml
    • valid: True
    • repo is contained as output veld in:
      • https://github.com/veldhub/veld_chain__eltec_udpipe_inference
    • metadata:
      • description: eltec analysis on its conllu data inferenced with udpipe
      • topic: NLP, Tokenization, Lemmatization, Part Of Speech, Dependency Parsing, Universal Dependencies, Grammatical Annotation
      • file_type: json
      • content: statistics, linguistic data, annotated literature
• https://github.com/veldhub/veld_data__eltec_original_selection
  • veld.yaml
    • valid: True
    • repo is contained as input veld in:
      • https://github.com/veldhub/veld_chain__eltec_udpipe_inference
    • metadata:
      • description: parent git repo that integrates various ELTeC corpora as submodules for downstream processing.
      • file_type: xml
      • content: TEI, annotated literature
• https://github.com/veldhub/veld_data__fasttext_models
  • m1/veld.yaml
    • valid: True
    • repo is contained as input veld in:
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__amc
    • metadata:
      • description: test training
      • topic: NLP, Word Embeddings
      • file_type: fastText model
  • m3/veld.yaml
    • valid: True
    • repo is contained as input veld in:
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__amc
    • metadata:
      • description: 10% AMC model
      • topic: NLP, Word Embeddings
      • file_type: fastText model
  • m4/veld.yaml
    • valid: True
    • repo is contained as input veld in:
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__amc
    • metadata:
      • description: 10% AMC model
      • topic: NLP, Word Embeddings
      • file_type: fastText model
  • m5/veld.yaml
    • valid: True
    • repo is contained as input veld in:
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__amc
    • metadata:
      • description: 10% AMC model
      • topic: NLP, Word Embeddings
      • file_type: fastText model
  • m6/veld.yaml
    • valid: True
    • repo is contained as input veld in:
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__amc
    • metadata:
      • description: 100% AMC model
      • topic: NLP, Word Embeddings
      • file_type: fastText model
  • m7/veld.yaml
    • valid: True
    • repo is contained as input veld in:
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__amc
    • metadata:
      • description: 100% AMC model
      • topic: NLP, Word Embeddings
      • file_type: fastText model
  • m8/veld.yaml
    • valid: True
    • repo is contained as input veld in:
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__amc
    • metadata:
      • description: 100% AMC model
      • topic: NLP, Word Embeddings
      • file_type: fastText model
  • m9/veld.yaml
    • valid: True
    • repo is contained as input veld in:
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__amc
    • metadata:
      • description: 100% AMC model
      • topic: NLP, Word Embeddings
      • file_type: fastText model
• https://github.com/veldhub/veld_data__glove_models
  • m1/veld.yaml
    • valid: True
    • repo is contained as input veld in:
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__amc
    • repo is contained as output veld in:
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__amc
    • metadata:
      • description: glove test model
      • topic: NLP, Word Embeddings
      • file_type: GloVe model, bin, txt
  • m3/veld.yaml
    • valid: True
    • repo is contained as input veld in:
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__amc
    • repo is contained as output veld in:
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__amc
    • metadata:
      • description: 1% AMC model
      • topic: NLP, Word Embeddings
      • file_type: GloVe model, bin, txt
• https://github.com/veldhub/veld_data__word2vec_models
  • m3/veld.yaml
    • valid: True
    • repo is contained as input veld in:
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__amc
    • repo is contained as output veld in:
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__amc
    • metadata:
      • description: 10% AMC model
      • topic: NLP, Word Embeddings
      • file_type: word2vec model
  • m4/veld.yaml
    • valid: True
    • repo is contained as input veld in:
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__amc
    • repo is contained as output veld in:
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__amc
    • metadata:
      • description: 10% AMC model
      • topic: NLP, Word Embeddings
      • file_type: word2vec model
  • m5/veld.yaml
    • valid: True
    • repo is contained as input veld in:
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__amc
    • repo is contained as output veld in:
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__amc
    • metadata:
      • description: 10% AMC model
      • topic: NLP, Word Embeddings
      • file_type: word2vec model
  • m6/veld.yaml
    • valid: True
    • repo is contained as input veld in:
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__amc
    • repo is contained as output veld in:
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__amc
    • metadata:
      • description: 100% AMC model
      • topic: NLP, Word Embeddings
      • file_type: word2vec model
  • m7/veld.yaml
    • valid: True
    • repo is contained as input veld in:
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__amc
    • repo is contained as output veld in:
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__amc
    • metadata:
      • description: 100% AMC model
      • topic: NLP, Word Embeddings
      • file_type: word2vec model
  • m8/veld.yaml
    • valid: True
    • repo is contained as input veld in:
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__amc
    • repo is contained as output veld in:
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__amc
    • metadata:
      • description: 100% AMC model
      • topic: NLP, Word Embeddings
      • file_type: word2vec model
  • m9/veld.yaml
    • valid: True
    • repo is contained as input veld in:
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__amc
    • repo is contained as output veld in:
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__amc
    • metadata:
      • description: 100% AMC model
      • topic: NLP, Word Embeddings
      • file_type: word2vec model
• https://github.com/veldhub/veld_data__wordembeddings_evaluation
  • evaluation_gold_data/capitalized/veld.yaml
    • valid: True
    • repo is contained as input veld in:
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__amc
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__wikipedia
    • repo is contained as output veld in:
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__amc
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__wikipedia
    • metadata:
      • description: custom evaluation data for evaluating word embeddings models. Words are capitalized.
      • topic: NLP, Word Embeddings
      • file_type: yaml
      • content: evaluation data, NLP gold data
  • evaluation_gold_data/lowercase/veld.yaml
    • valid: True
    • repo is contained as input veld in:
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__amc
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__wikipedia
    • repo is contained as output veld in:
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__amc
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__wikipedia
    • metadata:
      • description: custom evaluation data for evaluating word embeddings models. Words are all lowercase.
      • topic: NLP, Word Embeddings
      • file_type: yaml
      • content: evaluation data, NLP gold data
• https://github.com/veldhub/veld_code__wikipedia_nlp_preprocessing
  • data/wikipedia_json/veld_data_extracted.yaml
    • valid: False, elements not allowed: /x-veld/data/topics,contents
    • repo is contained as code veld in:
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__wikipedia

• https://github.com/veldhub/veld_chain__compare_tokenizations
  • code/veld_code__jupyter_analysis/veld.yaml
    • valid: True
    • metadata:
      • description: template veld code repo for a juptyer notebook
• https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__amc
  • veld_preprocess_clean.yaml
    • valid: True
    • metadata:
      • description: Removes lines that don't reach a threshold regarding the ratio of textual content to non-textual (numbers, special characters) content.
      • topic: NLP, Preprocessing, ETL
• https://github.com/veldhub/veld_code__akp_ner_inference
  • veld_infer.yaml
    • valid: True
    • repo is contained as code veld in:
      • https://github.com/veldhub/veld_chain__akp_ner_inference
    • metadata:
      • description: apply NER models on linkedcat data for usage of the inferenced entites in the AKP project
      • topic: NLP, Machine Learning, Named Entity Recognition
      • input:
        • 1:
          • file_type: spaCy model
          • content: NLP model, NER model
      • output:
        • 1:
          • description: inferenced NLP / NER data.
          • file_type: csv
          • content: inferenced NLP data, NLP data, NER data
• https://github.com/veldhub/veld_code__analyse_conllu
  • veld.yaml
    • valid: True
    • repo is contained as code veld in:
      • https://github.com/veldhub/veld_chain__eltec_udpipe_inference
    • metadata:
      • description: A statistical summary on conllu data, to count linguistic features of a conllu file: - count of total tokens - count of total lemma - count of lemma normalized by token (to put the lemma in relation with token) - count of occurrence of each (Universal Dependencies) part of speech tag Can be adapted to other use cases and made more flexible, but is primarily used in this chain veld: https://github.com/veldhub/veld_chain__eltec_udpipe_inference
      • topic: NLP, Machine Learning, Tokenization, Lemmatization, Part Of Speech, Dependency Parsing, Universal Dependencies, Grammatical Annotation
      • input:
        • 1:
          • file_type: conllu
      • output:
        • 1:
          • file_type: json
          • content: statistics, NLP statistics
• https://github.com/veldhub/veld_code__apache_jena_fuseki
  • veld_export.yaml
    • valid: True
    • repo is contained as code veld in:
      • https://github.com/veldhub/veld_chain__automatic_tei-ification_of_gutenberg
      • https://github.com/veldhub/veld_chain__gutenberg_triplestore
    • metadata:
      • description: Exports data from an Apache Fuseki triplestore given a rq query file, into several serialization formats.
      • topic: ETL, RDF, triplestore
      • input:
        • 1:
          • description: Contains the sparql query that is ran at the triplestore
          • file_type: rq
          • content: sparql query
      • output:
        • 1:
          • description: Output file where the query results should be writte into.
          • file_type: csv, json, tsv, xml
  • veld_import_rdf.yaml
    • valid: True
    • repo is contained as code veld in:
      • https://github.com/veldhub/veld_chain__automatic_tei-ification_of_gutenberg
      • https://github.com/veldhub/veld_chain__gutenberg_triplestore
    • metadata:
      • description: Import script to batch import rdf data from a folder into an apache fuseki triplestore. Note that if this service should connect to a triplestore running in another docker compose service, the relevant networks section might be necessary to set accordingly.
      • topic: ETL, RDF, triplestore, database
      • input:
        • 1:
          • description: A folder containing RDF/XML files. If var in_rdf_file is set, that file is imported; if not, the folder is recursively searched.
          • file_type: xml
          • content: RDF/XML
  • veld_run_server.yaml
    • valid: False, elements not allowed: /x-veld/code/storage
    • repo is contained as code veld in:
      • https://github.com/veldhub/veld_chain__automatic_tei-ification_of_gutenberg
      • https://github.com/veldhub/veld_chain__gutenberg_triplestore
• https://github.com/veldhub/veld_code__bert_embeddings
  • veld_infer_and_create_index.yaml
    • valid: True
• https://github.com/veldhub/veld_code__conllueditor
  • veld.yaml
    • valid: False, elements not allowed: /x-veld/code/storage
    • repo is contained as code veld in:
      • https://github.com/veldhub/veld_chain__demo_conllueditor
      • https://github.com/veldhub/veld_chain__eltec_udpipe_inference
• https://github.com/veldhub/veld_code__downloader
  • veld.yaml
    • valid: True
    • repo is contained as code veld in:
      • https://github.com/veldhub/veld_chain__automatic_tei-ification_of_gutenberg
      • https://github.com/veldhub/veld_chain__compare_tokenizations
      • https://github.com/veldhub/veld_chain__demo_downloader
      • https://github.com/veldhub/veld_chain__demo_wordembeddings_multiarch
      • https://github.com/veldhub/veld_chain__dta_semantic_drift_analysis
      • https://github.com/veldhub/veld_chain__eltec_udpipe_inference
      • https://github.com/veldhub/veld_chain__gutenberg_triplestore
    • metadata:
      • description: A download helper, for simple or batch downloads. Also offers automatic compressed file extraction.
      • topic: ETL
      • input:
        • 1:
          • description: Besides downloading directly from a given url, the downloader may also process a csv file for batch downloading. The first column of the csv must be the url, and the optional second column a designated file name. Column names are ignored by default (by var csv_has_headers being set to true). Note that the var in_csv_file can not be set at the same time as url, since the former designates batch processing while the latter designates a single download.
          • file_type: csv
          • content: download urls and target file names
      • output:
        • 1:
          • description: optional. If out_file is unset, this script will fetch the file name from the resource.
• https://github.com/veldhub/veld_code__fasttext
  • veld_export.yaml
    • valid: True
    • repo is contained as code veld in:
      • https://github.com/veldhub/veld_chain__demo_wordembeddings_multiarch
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__amc
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__wikipedia
    • metadata:
      • description: exports a fasttext model to a pkl file containing a python dict, where the keys are words and its values are the learned vectors, represented as high-dimensional numpy arrays.
      • topic: ETL, NLP, Machine Learning, Word Embeddings
      • input:
        • 1:
          • file_type: fastText model
          • content: Word Embeddings vectors
      • output:
        • 1:
          • file_type: pkl
          • content: Word Embeddings model, Word Embeddings vectors
  • veld_jupyter_notebook.yaml
    • valid: True
    • repo is contained as code veld in:
      • https://github.com/veldhub/veld_chain__demo_wordembeddings_multiarch
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__amc
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__wikipedia
    • metadata:
      • description: a fasttext training and inference jupyter notebook.
      • topic: NLP, Machine Learning, Word Embeddings
  • veld_train.yaml
    • valid: True
    • repo is contained as code veld in:
      • https://github.com/veldhub/veld_chain__demo_wordembeddings_multiarch
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__amc
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__wikipedia
    • metadata:
      • description: A fasttext training setup.
      • topic: NLP, Machine Learning, Word Embeddings
      • input:
        • 1:
          • description: training data must be expressed as one sentence per line.
          • file_type: txt
          • content: raw text
      • output:
        • 1:
          • file_type: fastText model
          • content: Word Embeddings
• https://github.com/veldhub/veld_code__flair
  • veld_infer.yaml
    • valid: False, non-optional value: ' | {}' missing at: /x-veld/code/topic/
  • veld_train.yaml
    • valid: False, non-optional value: ' | {}' missing at: /x-veld/code/topic/
• https://github.com/veldhub/veld_code__glove
  • veld_export.yaml
    • valid: True
    • repo is contained as code veld in:
      • https://github.com/veldhub/veld_chain__demo_wordembeddings_multiarch
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__amc
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__wikipedia
    • metadata:
      • description: exports a glove model to a pkl file containing a python dict, where the keys are words and its values are the learned vectors, represented as high-dimensional numpy arrays.
      • topic: ETL, NLP, Machine Learning, Word Embeddings
      • input:
        • 1:
          • file_type: txt
          • content: GloVe model, Word Embeddings model, Word Embeddings vectors
      • output:
        • 1:
          • file_type: pkl
          • content: Word Embeddings model, Word Embeddings vectors
  • veld_jupyter_notebook.yaml
    • valid: True
    • repo is contained as code veld in:
      • https://github.com/veldhub/veld_chain__demo_wordembeddings_multiarch
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__amc
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__wikipedia
    • metadata:
      • description: A jupyter notebook that loads GloVe vectors and provides some convenient functions to use them.
      • topic: NLP, Machine Learning, Word Embeddings
  • veld_train.yaml
    • valid: True
    • repo is contained as code veld in:
      • https://github.com/veldhub/veld_chain__demo_wordembeddings_multiarch
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__amc
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__wikipedia
    • metadata:
      • description: A GloVe training setup.
      • topic: NLP, Machine Learning, Word Embeddings
      • input:
        • 1:
          • description: In the txt file, each line must be one sentence
          • file_type: txt
          • content: natural text
      • output:
        • 1:
          • file_type: GloVe model
          • content: NLP model, Word Embeddings model
        • 2:
          • file_type: GloVe model
          • content: NLP model, Word Embeddings model
        • 3:
          • file_type: GloVe model
          • content: NLP model, Word Embeddings model
        • 4:
          • file_type: GloVe model
          • content: NLP model, Word Embeddings model
• https://github.com/veldhub/veld_code__inception
  • veld.yaml
    • valid: False, elements not allowed: /x-veld/code/storage
• https://github.com/veldhub/veld_code__jupyter_notebook_base
  • veld.yaml
    • valid: True
    • metadata:
      • description: template veld code repo for a juptyer notebook
• https://github.com/veldhub/veld_code__pypi_publisher
  • veld_publish.yaml
    • valid: False, elements not allowed: /x-veld/code/inputs
    • repo is contained as code veld in:
      • https://github.com/veldhub/veld_chain__demo_pypi_publisher
• https://github.com/veldhub/veld_code__simple_docker_test
  • veld.yaml
    • valid: True
    • metadata:
      • description: prints information about the python intepreter within the docker container.
      • topic: Testing
• https://github.com/veldhub/veld_code__spacy
  • veld_convert.yaml
    • valid: True
    • repo is contained as code veld in:
      • https://github.com/veldhub/veld_chain__train_spacy_apis_ner
    • metadata:
      • description: prepare data for spacy NER training, since spacy expects the entity annotation indices to be precisely at the beginning and end of the words, and also no overlapping entity annotations. Then it converts the data to spaCy docbin, and prepares it for training by splitting it into train, dev, eval subsets, and shuffling them randomly.
      • topic: ETL, NLP, Machine Learning
      • input:
        • 1:
          • description: name of the csv file, containing NER gold data
          • file_type: json
          • content: NER gold data
      • output:
        • 1:
          • description: path to folder where spacy docbin files will be stored with file names train.spacy, dev.spacy, eval.spacy
          • file_type: spaCy docbin
          • content: NER gold data
        • 2:
          • description: log file of conversion
          • file_type: txt
          • content: log
  • veld_create_config.yaml
    • valid: True
    • repo is contained as code veld in:
      • https://github.com/veldhub/veld_chain__train_spacy_apis_ner
    • metadata:
      • description: Creating a spacy config by encapsulating init config ( https://spacy.io/api/cli#init-config ) and init fill-config ( https://spacy.io/api/cli#init-fill-config ) . The output is ai config file used for training; see more here: https://spacy.io/usage/training/#config
      • topic: NLP, Machine Learning
      • output:
        • 1:
          • description: See https://spacy.io/usage/training/#config
          • file_type: cfg
          • content: spacy training config
  • veld_publish_to_hf.yaml
    • valid: True
    • repo is contained as code veld in:
      • https://github.com/veldhub/veld_chain__train_spacy_apis_ner
    • metadata:
      • description: simple service to push spacy models to huggingface. Important: Only works from spacy v3.* onwards!
      • topic: NLP, ETL
      • input:
        • 1:
          • file_type: spaCy model
          • content: NLP model
  • veld_train.yaml
    • valid: True
    • repo is contained as code veld in:
      • https://github.com/veldhub/veld_chain__train_spacy_apis_ner
    • metadata:
      • description: A spacy trainig setup, utilizing spacy v3's config system.
      • topic: NLP, Machine Learning
      • input:
        • 1:
          • file_type: spaCy docbin
          • content: NLP gold data, ML gold data, gold data
        • 2:
          • file_type: spaCy docbin
          • content: NLP gold data, ML gold data, gold data
        • 3:
          • file_type: spaCy docbin
          • content: NLP gold data, ML gold data, gold data
        • 4:
          • description: See https://spacy.io/usage/training/#config
          • file_type: cfg
          • content: spacy training config
      • output:
        • 1:
          • file_type: spaCy model
          • content: NLP model
        • 2:
          • description: training log file
          • file_type: txt
          • content: log
        • 3:
          • description: evaluation log file
          • file_type: txt
          • content: log
• https://github.com/veldhub/veld_code__teitok-tools
  • veld_parseudpipe.yaml
    • valid: True
    • repo is contained as code veld in:
      • https://github.com/veldhub/veld_chain__automatic_tei-ification_of_gutenberg
      • https://github.com/veldhub/veld_chain__compare_tokenizations
      • https://github.com/veldhub/veld_chain__demo_teitok-tools
    • metadata:
      • description: This code veld encapsulates and veldifies the parseudpipe script. All its config here are passed down to the script. For more information on its usage and config, see: https://github.com/ufal/teitok-tools?tab=readme-ov-file#parseudpipe
      • topic: NLP, ETL, Tokenization, Universal Dependencies
      • input:
        • 1:
          • file_type: xml
      • output:
        • 1:
          • file_type: xml
  • veld_udpipe2teitok.yaml
    • valid: True
    • repo is contained as code veld in:
      • https://github.com/veldhub/veld_chain__automatic_tei-ification_of_gutenberg
      • https://github.com/veldhub/veld_chain__compare_tokenizations
      • https://github.com/veldhub/veld_chain__demo_teitok-tools
    • metadata:
      • description: This code veld encapsulates and veldifies the udpipe2teitok script. All its config here are passed down to the script. For more information on its usage and config, see: https://github.com/ufal/teitok-tools?tab=readme-ov-file#udpipe2teitok
      • topic: NLP, Grammatical Annotation, Universal Dependencies, Tokenization, Lemmatization, Part Of Speech, Dependency Parsing
      • input:
        • 1:
          • file_type: txt
      • output:
        • 1:
          • file_type: xml
  • veld_xmltokenize.yaml
    • valid: True
    • repo is contained as code veld in:
      • https://github.com/veldhub/veld_chain__automatic_tei-ification_of_gutenberg
      • https://github.com/veldhub/veld_chain__compare_tokenizations
      • https://github.com/veldhub/veld_chain__demo_teitok-tools
    • metadata:
      • description: This code veld encapsulates and veldifies the xmltokenize script. All its config here are passed down to the script. For more information on its usage and config, see: https://github.com/ufal/teitok-tools?tab=readme-ov-file#xmltokenize
      • topic: NLP, ETL, Tokenization, Universal Dependencies
      • input:
        • 1:
          • description: The xml file to be tokenized
          • file_type: xml
      • output:
        • 1:
          • description: The output tokenized xml
          • file_type: xml
• https://github.com/veldhub/veld_code__udpipe
  • veld_infer.yaml
    • valid: True
    • repo is contained as code veld in:
      • https://github.com/veldhub/veld_chain__demo_udipe_ts-vienna-2024
      • https://github.com/veldhub/veld_chain__eltec_udpipe_inference
    • metadata:
      • description: udpipe inference setup. See https://lindat.mff.cuni.cz/services/udpipe/ for more information on the software encapsulated here.
      • topic: NLP, Machine Learning, Tokenization, Lemmatization, Part Of Speech, Dependency Parsing, Universal Dependencies, Grammatical Annotation
      • input:
        • 1:
          • description: txt files to be inferenced on. Note that the environment var in_txt_file is optional, and if it is not present, the entire input folder will be processed recursively
          • file_type: txt
          • content: raw text
        • 2:
          • file_type: udpipe model
          • content: NLP model, tokenizer, lemmatizer
      • output:
        • 1:
          • description: The file name of the output conllu is created by the corresponding input txt file, since recursive processing requires such automatic logic
          • file_type: conllu, tsv
          • content: inferenced NLP data, tokenized text, lemmatized text, Part Of Speech of text, Universal Dependencies of text, grammatically annotated text, linguistic data
  • veld_train.yaml
    • valid: True
    • repo is contained as code veld in:
      • https://github.com/veldhub/veld_chain__demo_udipe_ts-vienna-2024
      • https://github.com/veldhub/veld_chain__eltec_udpipe_inference
    • metadata:
      • description: udpipe training setup. See https://lindat.mff.cuni.cz/services/udpipe/ for more information on the software encapsulated here.
      • topic: NLP, Machine Learning, Tokenization, Lemmatization, Part Of Speech, Dependency Parsing, Universal Dependencies, Grammatical Annotation
      • input:
        • 1:
          • file_type: conllu
          • content: tokenized text, enriched text, linguistic data
      • output:
        • 1:
          • file_type: udpipe model
          • content: NLP model, tokenizer, lemmatizer
• https://github.com/veldhub/veld_code__wikipedia_nlp_preprocessing
  • veld_download_and_extract.yaml
    • valid: True
    • repo is contained as code veld in:
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__wikipedia
    • metadata:
      • description: downloading wikipedia archive and extracting each article to a json file.
      • topic: NLP, Machine Learning, ETL
      • output:
        • 1:
          • description: a folder containing json files, where each file contains the content of a wikipedia article
          • file_type: json
          • content: NLP training data, raw text
  • veld_transform_wiki_json_to_txt.yaml
    • valid: True
    • repo is contained as code veld in:
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__wikipedia
    • metadata:
      • description: transforming wikipedia raw jsons to a single txt file.
      • topic: NLP, Machine Learning, ETL
      • input:
        • 1:
          • description: a folder containing json files, where each file contains the contents of a wikipedia article
          • file_type: json
          • content: NLP training data, raw text
      • output:
        • 1:
          • description: single txt file, containing only raw content of wikipedia pagaes, split into sentences or per article with a newline each, possibly being only a sampled subset for testing.
          • file_type: txt
          • content: NLP training data, Word Embeddings training data, raw text
• https://github.com/veldhub/veld_code__word2vec
  • veld_export.yaml
    • valid: True
    • repo is contained as code veld in:
      • https://github.com/veldhub/veld_chain__demo_wordembeddings_multiarch
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__amc
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__wikipedia
    • metadata:
      • description: exports a word2vec model to a pkl file containing a python dict, where the keys are words and its values are the learned vectors, represented as high-dimensional numpy arrays.
      • topic: ETL, NLP, Machine Learning, Word Embeddings
      • input:
        • 1:
          • file_type: word2vec model
          • content: Word Embeddings model, Word Embeddings vectors
      • output:
        • 1:
          • file_type: pkl
          • content: Word Embeddings model, Word Embeddings vectors
  • veld_jupyter_notebook.yaml
    • valid: True
    • repo is contained as code veld in:
      • https://github.com/veldhub/veld_chain__demo_wordembeddings_multiarch
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__amc
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__wikipedia
    • metadata:
      • description: a word2vec jupyter notebook, for quick experiments
      • topic: NLP, Machine Learning, Word Embeddings
      • input:
        • 1:
          • description: arbitrary storage for word2vec experiments
          • file_type: word2vec model, txt
          • content: NLP model, Word Embeddings model, model metadata, NLP training data, Word Embeddings training data, raw text
      • output:
        • 1:
          • description: arbitrary storage for word2vec experiments
          • file_type: word2vec model, txt
          • content: NLP model, Word Embeddings model, model metadata, NLP training data, Word Embeddings training data, raw text
  • veld_train.yaml
    • valid: True
    • repo is contained as code veld in:
      • https://github.com/veldhub/veld_chain__demo_wordembeddings_multiarch
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__amc
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__wikipedia
    • metadata:
      • description: A word2vec training setup
      • topic: NLP, Machine Learning, Word Embeddings
      • input:
        • 1:
          • description: training data. Must be one single txt file, one sentence per line.
          • file_type: txt
          • content: NLP training data, Word Embeddings training data, raw text
      • output:
        • 1:
          • description: self trained Word Embeddings word2vec model
          • file_type: word2vec model
          • content: NLP model, Word Embeddings model
• https://github.com/veldhub/veld_code__wordembeddings_evaluation
  • veld_analyse_evaluation.yaml
    • valid: True
    • repo is contained as code veld in:
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__amc
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__wikipedia
    • metadata:
      • description: data visualization of all evaluation data. In a jupyter notebook.
      • topic: NLP, Word Embeddings, Data Visualization
      • input:
        • 1:
          • description: summary of the custom evaluation logic on word embeddings
          • file_type: yaml
          • content: evaluation data
      • output:
        • 1:
          • description: data visualization of all evaluation data, expressed as interactive html
          • file_type: html
          • content: data visualization
        • 2:
          • description: data visualization of all evaluation data, expressed as png
          • file_type: png
          • content: data visualization
  • veld_analyse_evaluation_non_interactive.yaml
    • valid: True
    • repo is contained as code veld in:
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__amc
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__wikipedia
    • metadata:
      • description: data visualization of all evaluation data. non-interactive version of the juypter code.
      • topic: NLP, Word Embeddings, Data Visualization
      • input:
        • 1:
          • description: summary of the custom evaluation logic on word embeddings
          • file_type: yaml
          • content: evaluation data
      • output:
        • 1:
          • description: data visualization of all evaluation data, expressed as interactive html
          • file_type: html
          • content: data visualization
        • 2:
          • description: data visualization of all evaluation data, expressed as png
          • file_type: png
          • content: data visualization
  • veld_eval_fasttext.yaml
    • valid: True
    • repo is contained as code veld in:
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__amc
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__wikipedia
    • metadata:
      • description: custom evaluation logic on fasttext word embeddings.
      • topic: NLP, Machine Learning, Evaluation
      • input:
        • 1:
          • file_type: fastText model
          • content: NLP model, Word Embeddings model
        • 2:
          • file_type: yaml
          • content: metadata
        • 3:
          • file_type: yaml
          • content: NLP gold data
      • output:
        • 1:
          • file_type: yaml
        • 2:
          • file_type: txt
          • content: log
  • veld_eval_glove.yaml
    • valid: True
    • repo is contained as code veld in:
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__amc
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__wikipedia
    • metadata:
      • description: custom evaluation logic on GloVe word embeddings.
      • topic: NLP, Machine Learning, Evaluation
      • input:
        • 1:
          • file_type: GloVe model
          • content: NLP model, Word Embeddings model
        • 2:
          • file_type: yaml
          • content: metadata
        • 3:
          • file_type: yaml
          • content: NLP gold data
      • output:
        • 1:
          • file_type: yaml
        • 2:
          • file_type: txt
          • content: log
  • veld_eval_word2vec.yaml
    • valid: True
    • repo is contained as code veld in:
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__amc
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__wikipedia
    • metadata:
      • description: custom evaluation logic on word2vec word embeddings.
      • topic: NLP, Machine Learning, Evaluation
      • input:
        • 1:
          • description: word2vec model file to be evaluated
          • file_type: word2vec model
          • content: NLP model, Word Embeddings model
        • 2:
          • description: word2vec model metadata
          • file_type: yaml
          • content: metadata
        • 3:
          • file_type: yaml
          • content: NLP gold data
      • output:
        • 1:
          • file_type: yaml
        • 2:
          • file_type: txt
          • content: log
• https://github.com/veldhub/veld_code__wordembeddings_preprocessing
  • veld_preprocess_clean.yaml
    • valid: True
    • repo is contained as code veld in:
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__amc
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__wikipedia
    • metadata:
      • description: Removes lines that don't reach a threshold regarding the ratio of textual content to non-textual (numbers, special characters) content. Splits output into clean and dirty file.
      • topic: NLP, Preprocessing, ETL
      • input:
        • 1:
          • file_type: txt
          • content: raw text
      • output:
        • 1:
          • description: clean lines, where each line's ratio is above the configured threshold
          • file_type: txt
          • content: raw text
        • 2:
          • description: dirty lines, where each line's ratio is below the configured threshold
          • file_type: txt
          • content: raw text
  • veld_preprocess_lowercase.yaml
    • valid: True
    • repo is contained as code veld in:
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__amc
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__wikipedia
    • metadata:
      • description: makes entire text lowercase
      • topic: NLP, Preprocessing, ETL
      • input:
        • 1:
          • file_type: txt
          • content: raw text
      • output:
        • 1:
          • file_type: txt
          • content: raw text
  • veld_preprocess_remove_punctuation.yaml
    • valid: True
    • repo is contained as code veld in:
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__amc
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__wikipedia
    • metadata:
      • description: removes punctuation from text with spaCy pretrained models
      • topic: NLP, Preprocessing, ETL
      • input:
        • 1:
          • file_type: txt
          • content: raw text
      • output:
        • 1:
          • file_type: txt
          • content: raw text
        • 2:
          • file_type: txt
          • content: raw text
  • veld_preprocess_sample.yaml
    • valid: True
    • repo is contained as code veld in:
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__amc
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__wikipedia
    • metadata:
      • description: takes a random sample of lines from a txt file. Randomness can be set with a seed too
      • topic: NLP, Preprocessing, ETL
      • input:
        • 1:
          • file_type: txt
          • content: raw text
      • output:
        • 1:
          • file_type: txt
          • content: raw text
  • veld_preprocess_strip.yaml
    • valid: True
    • repo is contained as code veld in:
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__amc
      • https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__wikipedia
    • metadata:
      • description: removes all lines before and after given line numbers
      • topic: NLP, Preprocessing, ETL
      • input:
        • 1:
          • file_type: txt
          • content: raw text
      • output:
        • 1:
          • file_type: txt
          • content: raw text
• https://github.com/veldhub/veld_code__xmlanntools
  • veld_ann2standoff.yaml
    • valid: True
    • repo is contained as code veld in:
      • https://github.com/veldhub/veld_chain__compare_tokenizations
      • https://github.com/veldhub/veld_chain__demo_xmlanntools
    • metadata:
      • description: A demo code veld, integrating the ann2standoff script. For more documentation, see: https://github.com/czcorpus/xmlanntools?tab=readme-ov-file#ann2standoff
      • topic: NLP, ETL
      • input:
        • 1:
          • file_type: conllu, tsv
        • 2:
          • file_type: txt
        • 3:
          • file_type: ini
      • output:
        • 1:
          • file_type: json
  • veld_standoff2xml.yaml
    • valid: True
    • repo is contained as code veld in:
      • https://github.com/veldhub/veld_chain__compare_tokenizations
      • https://github.com/veldhub/veld_chain__demo_xmlanntools
    • metadata:
      • description: A demo code veld, integrating the standoff2xml script. For more documentation, see: https://github.com/czcorpus/xmlanntools?tab=readme-ov-file#standoff2xml
      • topic: NLP, ETL
      • input:
        • 1:
          • file_type: txt
        • 2:
          • file_type: json
        • 3:
          • file_type: json
      • output:
        • 1:
          • file_type: xml
  • veld_tag_ud.yaml
    • valid: True
    • repo is contained as code veld in:
      • https://github.com/veldhub/veld_chain__compare_tokenizations
      • https://github.com/veldhub/veld_chain__demo_xmlanntools
    • metadata:
      • description: A demo code veld, integrating the tag_ud script. For more documentation, see: https://github.com/czcorpus/xmlanntools?tab=readme-ov-file#tag_ud
      • topic: NLP, ETL
      • input:
        • 1:
          • file_type: txt
      • output:
        • 1:
          • file_type: tsv, conllu
  • veld_xml2standoff.yaml
    • valid: True
    • repo is contained as code veld in:
      • https://github.com/veldhub/veld_chain__compare_tokenizations
      • https://github.com/veldhub/veld_chain__demo_xmlanntools
    • metadata:
      • description: A demo code veld, integrating the xml2standoff script. For more documentation, see: https://github.com/czcorpus/xmlanntools?tab=readme-ov-file#xml2standoff
      • topic: NLP, ETL
      • input:
        • 1:
          • file_type: xml
      • output:
        • 1:
          • file_type: txt
        • 2:
          • file_type: json
  • veld_xml2vrt.yaml
    • valid: True
    • repo is contained as code veld in:
      • https://github.com/veldhub/veld_chain__compare_tokenizations
      • https://github.com/veldhub/veld_chain__demo_xmlanntools
    • metadata:
      • description: A demo code veld, integrating the xml2vrt script. For more documentation, see: https://github.com/czcorpus/xmlanntools?tab=readme-ov-file#xml2vrt
      • topic: NLP, ETL
      • input:
        • 1:
          • file_type: xml
        • 2:
          • file_type: ini
      • output:
        • 1:
          • file_type: xml
• https://github.com/veldhub/veld_code__xml_xslt_transformer
  • veld.yaml
    • valid: True
    • repo is contained as code veld in:
      • https://github.com/veldhub/veld_chain__eltec_udpipe_inference
    • metadata:
      • description: generic xml / xslt transformation setup.
      • topic: ETL, Preprocessing
      • input:
        • 1:
          • description: the input xml file or folder containing xml. Note that if var in_xml_file is set, this script will only transform that file. If it's not set, it will go through the input folder recursively and create an equivalent output data structure.
          • file_type: xml
        • 2:
          • description: the input xsl file or folder containing xsl
          • file_type: xslt
      • output:
        • 1:
          • description: output file or folder for converted txt. Note that the var 'out_txt_file' is only respected, when the input is a single xml file. If the input is a folder, the output will be an equivalent data structure and the var 'out_txt_file' is ignored.
          • file_type: xml, txt

• https://github.com/veldhub/veld_chain__akp_ner_inference
  • veld_infer.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__akp_ner_inference
    • contains input velds:
      • https://github.com/veldhub/veld_data__apis_spacy_ner_models
    • contains output velds:
      • https://github.com/veldhub/veld_data__akp_ner_linkedcat
    • metadata:
      • description: This repo uses self-trained spaCy NER models on the linkedcat dataset to extract entities, which are stored in csv files.
      • topic: NLP, Machine Learning, Named Entity Recognition
• https://github.com/veldhub/veld_chain__apis_ner_evaluate_old_models
  • veld_evaluate.yaml
    • valid: True
    • contains input velds:
      • https://gitlab.oeaw.ac.at/acdh-ch/apis/spacy-ner
    • contains output velds:
      • https://gitlab.oeaw.ac.at/acdh-ch/apis/spacy-ner
    • metadata:
      • description: hard-coded evaluation of several spaCy 2.2.4 models. This chain veld is a wrapper of legacy code.
      • topic: NLP, Machine Learning, Named Entity Recognition
• https://github.com/veldhub/veld_chain__apis_ner_transform_to_gold
  • veld.yaml
    • valid: True
    • contains input velds:
      • https://gitlab.oeaw.ac.at/acdh-ch/apis/spacy-ner
    • contains output velds:
      • https://github.com/veldhub/veld_data__apis_oebl__ner_gold
    • metadata:
      • description: Conversion of apis ner model data to harmonized custom json format.
      • topic: ETL, Data Cleaning
• https://github.com/veldhub/veld_chain__automatic_tei-ification_of_gutenberg
  • veld_step_1_download_gutenberg_metadata.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__downloader
    • metadata:
      • description: Downloads and extracts the project gutenberg metadata.
      • topic: ETL
  • veld_step_2_run_server.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__apache_jena_fuseki
    • metadata:
      • description: An Apache Fuseki instance storing the entire gutenberg metadata.
      • topic: RDF, triplestore, database
  • veld_step_3_import_rdf.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__apache_jena_fuseki
    • metadata:
      • description: imports the gutenberg metadata into the Fuseki triplestore.
      • topic: ETL, RDF, triplestore
  • veld_step_4_query_books_urls.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__apache_jena_fuseki
    • metadata:
      • description: Exports a csv file containing download links and file names of all german books that have no TEI files, but a txt, which will be used for automatic TEI generation further downstream.
      • topic: ETL, RDF, triplestore
  • veld_step_5_download_gutenberg_books.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__downloader
    • metadata:
      • description: Downlaods all german books without TEI, as designated by the previously generated csv.
      • topic: ETL
  • veld_step_6_convert_books_to_teitok.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__teitok-tools
    • metadata:
      • description: Automatic creation of tokenized TEI files of downloaded txt books
      • topic: NLP, Grammatical Annotation, Universal Dependencies, Tokenization, Lemmatization, Part Of Speech, Dependency Parsing
  • veld_step_all.yaml
    • valid: True
• https://github.com/veldhub/veld_chain__compare_tokenizations
  • veld_step_1_download.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__downloader
    • metadata:
      • description: Downloads sample data from the german ELTeC corpus at https://github.com/COST-ELTeC/ELTeC-deu , into two folders, one for xmlanntools and the other for teitok-tools.
      • topic: ETL, Preprocessing
  • veld_step_2_xmlanntools.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__xmlanntools
    • metadata:
      • description: Uses a combination of tools provided mainly by xmlanntools at https://github.com/czcorpus/xmlanntools and veldified as code veld at https://github.com/veldhub/veld_code__xmlanntools . This chain processes an input TEI XML file by tokenizing and enriching it with udpipe and universal dependencies (https://universaldependencies.org/) metadata.
      • topic: NLP, Universal Dependencies, Grammatical Annotation
  • veld_step_3_teitok.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__teitok-tools
    • metadata:
      • description: Uses a combination of tools provided mainly by teitok-tools at https://github.com/ufal/teitok-tools and veldified as code veld at https://github.com/veldhub/veld_code__teitok-tools. This chain processes an input TEI XML file by tokenizing and enriching it with udpipe and universal dependencies (https://universaldependencies.org/) metadata.
      • topic: NLP, Universal Dependencies, Grammatical Annotation
  • veld_step_4_jupyter_analysis.yaml
    • valid: True
    • metadata:
      • description: This chain compares the two enriched TEI XML files which were the output of xmlanntools and teitok-tools. The comparison is done by aggregating and counting the various occurrences of grammatical metadata, structured by universal dependencies. This chain launches an interactive jupyter notebook for easy replication of these count statistics. After exceuting this chain, the notebook can be reached at http://localhost:8888/ .
      • topic: NLP, Universal Dependencies, Grammatical Annotation
  • veld_step_all.yaml
    • valid: True
    • metadata:
      • description: This aggregated chain consists of sub chains and serves the purpose of providing a single execution point for the entire workflow reproduction of this repository. The chain services below simply load their definitions from their respective veld yaml files. See these files for more details on each workflow step. At the end of all this processing, a jupyter notebook is launched, where statistics on the previous workflows are perfmored. This notebook can be reached at http://localhost:8888/ .
      • topic: ETL, Preprocessing, NLP, Universal Dependencies, Grammatical Annotation
• https://github.com/veldhub/veld_chain__demo_conllueditor
  • veld.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__conllueditor
    • metadata:
      • description: an example chain using the veldified version of https://github.com/Orange-OpenSource/conllueditor on sample data.
      • topic: NLP, Universal Dependencies, Tokenization, Lemmatization, Part Of Speech, Dependency Parsing, Grammatical Annotation
• https://github.com/veldhub/veld_chain__demo_downloader
  • veld_demo_01__single_download.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__downloader
    • metadata:
      • description: chain demonstrating download from a single url without any other configuration.
      • topic: demo
  • veld_demo_02__single_download__designate_file_name.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__downloader
    • metadata:
      • description: chain demonstrating download from a single url where a designated output file name is given.
      • topic: demo
  • veld_demo_03__single_download__extract.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__downloader
    • metadata:
      • description: chain demonstrating download from a single url where the downloaded file is an archive which will be automatically extracted.
      • topic: demo
  • veld_demo_04__single_download__designate_file_name__extract.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__downloader
    • metadata:
      • description: chain demonstrating download from a single url where a designated output file name is given, and it is an archive which will be automatically extracted.
      • topic: demo
  • veld_demo_05__csv_bulk_download.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__downloader
    • metadata:
      • description: chain demonstrating bulk download from a csv without any other configuration.
      • topic: demo
  • veld_demo_06__csv_bulk_download__designate_file_name.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__downloader
    • metadata:
      • description: chain demonstrating bulk download from a csv in which respective designated file names are given to each download link.
      • topic: demo
  • veld_demo_07__csv_bulk_download__extract.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__downloader
    • metadata:
      • description: chain demonstrating bulk download from a csv in which each download link is an archive which will be automatically extracted.
      • topic: demo
  • veld_demo_08__csv_bulk_download__csv_no_headers.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__downloader
    • metadata:
      • description: chain demonstrating bulk download from a csv without any headers.
      • topic: demo
  • veld_demo_09__csv_bulk_download__designate_file_name__extract__csv_no_headers.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__downloader
    • metadata:
      • description: chain demonstrating bulk download from a csv in which respective designated file names are given to each download link, where each download link is an archive which will be automatically extracted, and without any headers.
      • topic: demo
  • veld_demo_10__invalid__out_file_and_csv.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__downloader
    • metadata:
      • description: invalid chain demonstrating that giving in_csv_file and out_file at the same time is invalid, since out_file relates to single downloads only and not bulk downloads.
      • topic: demo
  • veld_demo_11__invalid__url_and_csv.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__downloader
    • metadata:
      • description: invalid chain demonstrating that giving in_csv_file and url at the same time is invalid, since url relates to single downloads only and not bulk downloads.
      • topic: demo
• https://github.com/veldhub/veld_chain__demo_pypi_publisher
  • veld_publish.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__pypi_publisher
    • metadata:
      • description: This code veld encapsulates a demo publishing workflow to pypi.org . It uses setuptools and contains a template python module that can be found at ./data/test_python_module/
• https://github.com/veldhub/veld_chain__demo_teitok-tools
  • veld_parseudpipe.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__teitok-tools
    • metadata:
      • description: This chain veld exemplifies usage of the respective code veld. For more information on the underlying tool and its usage, see: https://github.com/ufal/teitok-tools?tab=readme-ov-file#parseudpipe
      • topic: NLP, ETL, Tokenization, Universal Dependencies
  • veld_udpipe2teitok.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__teitok-tools
    • metadata:
      • description: This chain veld exemplifies usage of the respective code veld. For more information on the underlying tool and its usage, see: https://github.com/ufal/teitok-tools?tab=readme-ov-file#udpipe2teitok
      • topic: NLP, ETL, Tokenization, Universal Dependencies
  • veld_xmltokenize.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__teitok-tools
    • metadata:
      • description: This chain veld exemplifies usage of the respective code veld. For more information on the underlying tool and its usage, see: https://github.com/ufal/teitok-tools?tab=readme-ov-file#xmltokenize
      • topic: NLP, ETL, Tokenization, Universal Dependencies
• https://github.com/veldhub/veld_chain__demo_udipe_ts-vienna-2024
  • veld_infer.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__udpipe
    • contains input velds:
      • https://github.com/veldhub/veld_data__demo_inference_input_ts-vienna-2024
    • metadata:
      • description: A demonstration of a VELD chain inferencing on a txt with a self-trained udpipe model
      • topic: NLP, Universal Dependencies
  • veld_train.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__udpipe
    • contains input velds:
      • https://github.com/veldhub/veld_data__demo_train_data_ts-vienna-2024
    • metadata:
      • description: A demonstration of a VELD chain training a udpipe model from scratch
      • topic: NLP, Universal Dependencies
• https://github.com/veldhub/veld_chain__demo_wordembeddings_multiarch
  • veld_step_1_download.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__downloader
    • metadata:
      • description: Downloads the bible
      • topic: ETL, Bible Studies
  • veld_step_2_preprocess.yaml
    • valid: True
    • metadata:
      • description: Preprocesses the bible to be compatible with word embeddings training. This chain does not use a code veld but build context and code that is integrated into the chain repo itself, since the preprocessing is highly specific to this kind of data and the subsequent training.
      • topic: ETL, NLP, Bible Studies
  • veld_step_3_train_fasttext.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__fasttext
    • metadata:
      • description: Trains a fasttext model on the bible and exports its vectors as a dict serialized into a pkl file. The training data is rather small and the hyperparameteres are simplistic, in order to demonstrate the reproducibility of this chain rather than claiming any deeper insight into the data's words context.
      • topic: ETL, NLP, Machine Learning, Word Embeddings, Bible Studies
  • veld_step_4_train_glove.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__glove
    • metadata:
      • description: Trains a glove model on the bible and exports its vectors as a dict serialized into a pkl file. The training data is rather small and the hyperparameteres are simplistic, in order to demonstrate the reproducibility of this chain rather than claiming any deeper insight into the data's words context.
      • topic: ETL, NLP, Machine Learning, Word Embeddings, Bible Studies
  • veld_step_5_train_word2vec.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__word2vec
    • metadata:
      • description: Trains a word2vec model on the bible and exports its vectors as a dict serialized into a pkl file. The training data is rather small and the hyperparameteres are simplistic, in order to demonstrate the reproducibility of this chain rather than claiming any deeper insight into the data's words context.
      • topic: ETL, NLP, Machine Learning, Word Embeddings, Bible Studies
  • veld_step_6_analyse_vectors.yaml
    • valid: True
    • metadata:
      • description: Reads in the trained word vectors from their pkl serializations and tests them for plausability on a few selected words which should show intuitive close or distant relations to each other. This chain does not inherit from a code veld but is defined entirely within its containing chain repository since its code and data are highly context-specific. After reproducing the entire previous sequences yourself and execution of the notebook, feel free to save the notebook and compare the resulting differences with git diff ./code/analyse_vectors/notebooks/analyse_vectors.ipynb, where the reproduced vector similarities will have only slight differences to the record of previously trained ones. This difference is due to randomization within the training, but should be small enough to indicate approximate reproduction.
      • topic: ETL, NLP, Machine Learning, Word Embeddings, Bible Studies
  • veld_step_all.yaml
    • valid: True
    • metadata:
      • description: This is a multi chain that executes all other individual chains in sequential order. Refer to the other veld yaml files for more information on each step.
      • topic: ETL, NLP, Machine Learning, Word Embeddings, Bible Studies
• https://github.com/veldhub/veld_chain__demo_xmlanntools
  • veld_simple_poetry1_all_steps.yaml
    • valid: True
    • metadata:
      • description: a multichain aggregating all individual steps of https://github.com/czcorpus/xmlanntools/tree/main/examples#simple_poetry1 into one single chain
      • topic: NLP, ETL, Tokenization, Universal Dependencies
  • veld_simple_poetry1_step1_xml2standoff.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__xmlanntools
    • metadata:
      • description: A demo chain veld, integrating the xml2standoff script and implementing the first step of the 'Simple_poetry1' example at https://github.com/czcorpus/xmlanntools/tree/main/examples#simple_poetry1
      • topic: NLP, ETL, Tokenization, Universal Dependencies
  • veld_simple_poetry1_step2_tag_ud.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__xmlanntools
    • metadata:
      • description: A demo chain veld, integrating the tag_ud script and implementing the second step of the 'Simple_poetry1' example at https://github.com/czcorpus/xmlanntools/tree/main/examples#simple_poetry1
      • topic: NLP, ETL, Tokenization, Universal Dependencies
  • veld_simple_poetry1_step3_ann2standoff.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__xmlanntools
    • metadata:
      • description: A demo chain veld, integrating the ann2standoff script and implementing the third step of the 'Simple_poetry1' example at https://github.com/czcorpus/xmlanntools/tree/main/examples#simple_poetry1
      • topic: NLP, ETL, Tokenization, Universal Dependencies
  • veld_simple_poetry1_step4_standoff2xml.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__xmlanntools
    • metadata:
      • description: A demo chain veld, integrating the standoff2xml script and implementing the fourth step of the 'Simple_poetry1' example at https://github.com/czcorpus/xmlanntools/tree/main/examples#simple_poetry1
      • topic: NLP, ETL, Tokenization, Universal Dependencies
  • veld_simple_poetry1_step5_xml2vrt.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__xmlanntools
    • metadata:
      • description: A demo chain veld, integrating the xml2vrt script and implementing the fifth step of the 'Simple_poetry1' example at https://github.com/czcorpus/xmlanntools/tree/main/examples#simple_poetry1
      • topic: NLP, ETL, Tokenization, Universal Dependencies
  • veld_tei_example1_all_steps.yaml
    • valid: True
    • metadata:
      • description: a multichain aggregating all individual steps of https://github.com/czcorpus/xmlanntools/tree/main/examples#tei_example1 into one single chain
      • topic: NLP, ETL, Tokenization, Universal Dependencies
  • veld_tei_example1_step1_xml2standoff.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__xmlanntools
    • metadata:
      • description: A demo chain veld, integrating the xml2standoff script and implementing the first step of the 'TEI_example1' at https://github.com/czcorpus/xmlanntools/tree/main/examples#tei_example1
      • topic: NLP, ETL, Tokenization, Universal Dependencies
  • veld_tei_example1_step2_tag_ud.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__xmlanntools
    • metadata:
      • description: A demo chain veld, integrating the tag_ud script and implementing the second step of the 'TEI_example1' at https://github.com/czcorpus/xmlanntools/tree/main/examples#tei_example1
      • topic: NLP, ETL, Tokenization, Universal Dependencies
  • veld_tei_example1_step3_ann2standoff.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__xmlanntools
    • metadata:
      • description: A demo chain veld, integrating the ann2standoff script and implementing the third step of the 'TEI_example1' at https://github.com/czcorpus/xmlanntools/tree/main/examples#tei_example1
      • topic: NLP, ETL, Tokenization, Universal Dependencies
  • veld_tei_example1_step4_standoff2xml.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__xmlanntools
    • metadata:
      • description: A demo chain veld, integrating the standoff2xml script and implementing the fourth step of the 'TEI_example1' at https://github.com/czcorpus/xmlanntools/tree/main/examples#tei_example1
      • topic: NLP, ETL, Tokenization, Universal Dependencies
  • veld_tei_example1_step5_xml2vrt.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__xmlanntools
    • metadata:
      • description: A demo chain veld, integrating the xml2vrt script and implementing the fifth step of the 'TEI_example1' at https://github.com/czcorpus/xmlanntools/tree/main/examples#tei_example1
      • topic: NLP, ETL, Tokenization, Universal Dependencies
• https://github.com/veldhub/veld_chain__dta_semantic_drift_analysis
  • veld_step_1_download.yaml
    • valid: False, non-optional value: ' | {}' missing at: /x-veld/chain/topic/
    • contains code velds:
      • https://github.com/veldhub/veld_code__downloader
• https://github.com/veldhub/veld_chain__eltec_udpipe_inference
  • veld_step_1_preprocess.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__xml_xslt_transformer
    • contains input velds:
      • https://github.com/veldhub/veld_data__eltec_original_selection
    • metadata:
      • description: xml / xslt transformation of ELTeC data
      • topic: ETL
  • veld_step_2_download_models.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__downloader
    • metadata:
      • description: udpipe model download
      • topic: ETL
  • veld_step_3_infer.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__udpipe
    • metadata:
      • description: udpipe inference setup, reading in preprocessed ELTeC data
      • topic: NLP, Machine Learning, Tokenization, Lemmatization, Part Of Speech, Dependency Parsing, Universal Dependencies, Grammatical Annotation
  • veld_step_4_analyse.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__analyse_conllu
    • contains output velds:
      • https://github.com/veldhub/veld_data__eltec_conllu_stats
    • metadata:
      • description: chain to analyse the conllu data which was inferenced by udpipe on several ELTeC corpora.
      • topic: NLP, Machine Learning, Tokenization, Lemmatization, Part Of Speech, Dependency Parsing, Universal Dependencies, Grammatical Annotation
  • veld_step_5_inspect_with_conllueditor.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__conllueditor
    • metadata:
      • description: inspecting the conllu files with conllueditor.
      • topic: NLP, Universal Dependencies, Tokenization, Lemmatization, Part Of Speech, Dependency Parsing, Grammatical Annotation
  • veld_step_all.yaml
    • valid: True
    • metadata:
      • description: An aggregating chain, reusing and referencing all individual chains in a sequential processing
      • topic: ETL, NLP, Machine Learning, Tokenization, Lemmatization, Part Of Speech, Dependency Parsing, Universal Dependencies, Grammatical Annotation
• https://github.com/veldhub/veld_chain__gutenberg_triplestore
  • veld_download_gutenberg_metadata.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__downloader
    • metadata:
      • description: Downloads the entire gutenberg metadata RDF/XML file and extracts it
      • topic: ETL
  • veld_export.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__apache_jena_fuseki
    • metadata:
      • description: Given rq (sparql query) files, this chain exports data from Apache Fuseki triplestore into json files.
      • topic: ETL, RDF, triplestore
  • veld_import_rdf.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__apache_jena_fuseki
    • metadata:
      • description: Imports the Gutenberg RDF/XML data into the Apache Fuseki triplestore
      • topic: ETL, RDF, triplestore
  • veld_run_server.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__apache_jena_fuseki
    • metadata:
      • description: Runs an Apache Fuseki triplestore. Configuration can be adjusted with the respective shiro.ini file, of which there is a default integrated into this chain veld.
      • topic: RDF, triplestore
• https://github.com/veldhub/veld_chain__mara_load_and_publish_models
  • veld_publish_to_hf.yaml
    • valid: True
    • contains input velds:
      • https://gitlab.oeaw.ac.at/acdh-ch/mara/mara-nlp-suite-internal
    • metadata:
      • description: publish SpaCy text classification models trained during the MARA project to huggingface
      • topic: NLP
• https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__amc
  • veld_analyse_evaluation.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__wordembeddings_evaluation
    • contains input velds:
      • https://github.com/veldhub/veld_data__wordembeddings_evaluation
    • contains output velds:
      • https://github.com/veldhub/veld_data__wordembeddings_evaluation
    • metadata:
      • description: data visualization of all evaluation data. In a jupyter notebook.
      • topic: NLP, Word Embeddings, Data Visualization
  • veld_analyse_evaluation_non_interactive.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__wordembeddings_evaluation
    • contains input velds:
      • https://github.com/veldhub/veld_data__wordembeddings_evaluation
    • contains output velds:
      • https://github.com/veldhub/veld_data__wordembeddings_evaluation
    • metadata:
      • description: data visualization of all evaluation data. non-interactive version of the juypter code.
      • topic: NLP, Word Embeddings, Data Visualization
  • veld_eval_fasttext.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__wordembeddings_evaluation
    • contains input velds:
      • https://github.com/veldhub/veld_data__fasttext_models
      • https://github.com/veldhub/veld_data__wordembeddings_evaluation
    • contains output velds:
      • https://github.com/veldhub/veld_data__wordembeddings_evaluation
    • metadata:
      • description: custom evaluation logic on fasttext word embeddings.
      • topic: NLP, Machine Learning, Evaluation
  • veld_eval_glove.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__wordembeddings_evaluation
    • contains input velds:
      • https://github.com/veldhub/veld_data__glove_models
      • https://github.com/veldhub/veld_data__wordembeddings_evaluation
    • contains output velds:
      • https://github.com/veldhub/veld_data__wordembeddings_evaluation
  • veld_eval_word2vec.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__wordembeddings_evaluation
    • contains input velds:
      • https://github.com/veldhub/veld_data__word2vec_models
      • https://github.com/veldhub/veld_data__wordembeddings_evaluation
    • contains output velds:
      • https://github.com/veldhub/veld_data__wordembeddings_evaluation
  • veld_jupyter_notebook_fasttext.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__fasttext
  • veld_jupyter_notebook_glove.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__glove
  • veld_jupyter_notebook_word2vec.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__word2vec
  • veld_preprocess_lowercase.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__wordembeddings_preprocessing
    • contains input velds:
      • https://github.com/veldhub/veld_data__amc_we_training_data
    • contains output velds:
      • https://github.com/veldhub/veld_data__amc_we_training_data
    • metadata:
      • description: makes entire text lowercase
      • topic: NLP, Preprocessing, ETL
  • veld_preprocess_remove_punctuation.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__wordembeddings_preprocessing
    • contains input velds:
      • https://github.com/veldhub/veld_data__amc_we_training_data
    • contains output velds:
      • https://github.com/veldhub/veld_data__amc_we_training_data
    • metadata:
      • description: removes punctuation from text with spaCy pretrained models
      • topic: NLP, Preprocessing, ETL
  • veld_preprocess_sample.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__wordembeddings_preprocessing
    • contains input velds:
      • https://github.com/veldhub/veld_data__amc_we_training_data
    • metadata:
      • description: takes a random sample of lines from a txt file. Randomness can be set with a seed too
      • topic: NLP, Preprocessing, ETL
  • veld_preprocess_strip.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__wordembeddings_preprocessing
    • contains input velds:
      • https://github.com/veldhub/veld_data__amc_we_training_data
    • contains output velds:
      • https://github.com/veldhub/veld_data__amc_we_training_data
    • metadata:
      • description: removes all lines before and after given line numbers
      • topic: NLP, Preprocessing, ETL
  • veld_train_fasttext.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__fasttext
    • metadata:
      • description: A fasttext training setup.
      • topic: NLP, Machine Learning, Word Embeddings
  • veld_train_glove.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__glove
    • contains input velds:
      • https://github.com/veldhub/veld_data__amc_we_training_data
    • contains output velds:
      • https://github.com/veldhub/veld_data__glove_models
    • metadata:
      • description: A GloVe training setup.
      • topic: NLP, Machine Learning, Word Embeddings
  • veld_train_word2vec.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__word2vec
    • contains input velds:
      • https://github.com/veldhub/veld_data__amc_we_training_data
    • contains output velds:
      • https://github.com/veldhub/veld_data__word2vec_models
    • metadata:
      • description: A word2vec training setup
      • topic: NLP, Machine Learning, Word Embeddings
• https://github.com/veldhub/veld_chain__train_infer_wordembeddings_multiple_architectures__wikipedia
  • veld_playground_jupyter_notebook_fasttext.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__fasttext
    • metadata:
      • description: jupyter notebook for playing with fasttext models
      • topic: NLP
  • veld_playground_jupyter_notebook_glove.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__glove
    • metadata:
      • description: jupyter notebook for playing with glove models
      • topic: NLP
  • veld_playground_jupyter_notebook_word2vec.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__word2vec
    • metadata:
      • description: jupyter notebook for playing with word2vec models
      • topic: NLP
  • veld_step_01_preprocess_download_and_extract.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__wikipedia_nlp_preprocessing
    • metadata:
      • description: downloading wikipedia archive and extracting each article to a json file.
      • topic: NLP, ETL
  • veld_step_02_preprocess_transform_wiki_json_to_txt.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__wikipedia_nlp_preprocessing
    • metadata:
      • description: transforming wikipedia jsons to a single txt file.
      • topic: NLP, ETL
  • veld_step_03_preprocess_lowercase.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__wordembeddings_preprocessing
    • metadata:
      • description: preprocessing by making the entire text lowercase.
      • topic: NLP, ETL
  • veld_step_04_preprocess_remove_punctuation.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__wordembeddings_preprocessing
    • metadata:
      • description: preprocessing by removing punctuation of the entire text.
      • topic: NLP, ETL
  • veld_step_05_train_fasttext.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__fasttext
    • metadata:
      • description: training a fasttext model on wikipediaa
      • topic: NLP
  • veld_step_06_train_word2vec.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__word2vec
    • metadata:
      • description: training a word2vec model on wikipediaa
      • topic: NLP
  • veld_step_07_train_glove.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__glove
    • metadata:
      • description: training a glove model on wikipediaa
      • topic: NLP
  • veld_step_08_eval_fasttext.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__wordembeddings_evaluation
    • contains input velds:
      • https://github.com/veldhub/veld_data__wordembeddings_evaluation
    • contains output velds:
      • https://github.com/veldhub/veld_data__wordembeddings_evaluation
    • metadata:
      • description: evaluate fasttext model against evaluation gold data
      • topic: NLP, Evaluation
  • veld_step_09_eval_word2vec.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__wordembeddings_evaluation
    • contains input velds:
      • https://github.com/veldhub/veld_data__wordembeddings_evaluation
    • contains output velds:
      • https://github.com/veldhub/veld_data__wordembeddings_evaluation
    • metadata:
      • description: evaluate word2vec model against evaluation gold data
      • topic: NLP, Evaluation
  • veld_step_10_eval_glove.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__wordembeddings_evaluation
    • contains input velds:
      • https://github.com/veldhub/veld_data__wordembeddings_evaluation
    • contains output velds:
      • https://github.com/veldhub/veld_data__wordembeddings_evaluation
    • metadata:
      • description: evaluate glove model against evaluation gold data
      • topic: NLP, Evaluation
  • veld_step_11_analyse_evaluation.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__wordembeddings_evaluation
    • contains input velds:
      • https://github.com/veldhub/veld_data__wordembeddings_evaluation
    • contains output velds:
      • https://github.com/veldhub/veld_data__wordembeddings_evaluation
    • metadata:
      • description: chain of analysing and evaluating models trained on wikipedia
      • topic: NLP, Evaluation
  • veld_step_all_multi_chain.yaml
    • valid: True
    • metadata:
      • description: An entire multi chain, going through everything (fetching, preprocessing, training, evaluation in one service. This chain is composed of the other chains and is rather meant as a demonstration of the entire setup
      • topic: NLP
• https://github.com/veldhub/veld_chain__train_spacy_apis_ner
  • veld_analysis.yaml
    • valid: True
    • metadata:
      • description: Analyses out-of vocabulary occurrences of training data.
      • topic: NLP, Machine Learning, Named Entity Recognition, Analysis
  • veld_convert.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__spacy
    • contains input velds:
      • https://github.com/veldhub/veld_data__apis_oebl__ner_gold
    • metadata:
      • description: Cleaning and converting json into spaCy docbin
      • topic: ETL, NLP, Machine Learning
  • veld_create_config.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__spacy
    • metadata:
      • description: Creates a spacy training config according to passed arguments. See https://spacy.io/usage/training/#config for the target outcome.
      • topic: NLP, Machine Learning
  • veld_publish_to_hf.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__spacy
    • contains input velds:
      • https://github.com/veldhub/veld_data__apis_spacy_ner_models
    • metadata:
      • description: Pushing spacy model to huggingface.
      • topic: NLP
  • veld_train.yaml
    • valid: True
    • contains code velds:
      • https://github.com/veldhub/veld_code__spacy
    • contains output velds:
      • https://github.com/veldhub/veld_data__apis_spacy_ner_models
    • metadata:
      • description: A NER trainig setup, utilizing spaCy 3's config system.
      • topic: NLP, Machine Learning, Named Entity Recognition

• Analysis
• Bible Studies
• Data Cleaning
• Data Visualization
• database
• demo
• Dependency Parsing
• ETL
• Evaluation
• Grammatical Annotation
• Lemmatization
• Machine Learning
• Named Entity Recognition
• NLP
• Part Of Speech
• Preprocessing
• RDF
• Testing
• Tokenization
• triplestore
• Universal Dependencies
• Word Embeddings

• annotated literature
• data visualization
• download urls and target file names
• enriched text
• evaluation data
• GloVe model
• gold data
• grammatically annotated text
• inferenced NLP data
• lemmatized text
• lemmatizer
• linguistic data
• linguistically enriched text
• log
• metadata
• ML gold data
• model metadata
• natural text
• NER data
• NER gold data
• NER model
• newspaper texts
• NLP data
• NLP gold data
• NLP model
• NLP statistics
• NLP training data
• Part Of Speech of text
• raw text
• RDF/XML
• spaCy model
• spacy training config
• sparql query
• statistics
• TEI
• tokenized text
• tokenizer
• Universal Dependencies of text
• Word Embeddings
• Word Embeddings model
• Word Embeddings training data
• Word Embeddings vectors

• bin
• cfg
• conllu
• csv
• fastText model
• GloVe model
• html
• ini
• json
• pkl
• png
• rq
• spaCy docbin
• spaCy model
• tsv
• txt
• udpipe model
• word2vec model
• xml
• xslt
• yaml