arekit-ss 0.25.0

Low Resource Context Relation Sampler for contexts with relations for fact-checking and fine-tuning your LLM models, powered by AREkit

arekit-ss 0.25.0

📜 List of binded sources

arekit-ss [AREkit double "s"] -- is an object-pair context sampler for datasources, powered by AREkit

NOTE: For custom text sampling, please follow the ARElight project.

Installation

Install dependencies:

pip install git+https://github.com/nicolay-r/arekit-ss.git@0.25.0

Download resources:

python -m arekit_ss.download_data

Usage

Example of composing prompts:

python -m arekit_ss.sample --writer csv --source rusentrel --sampler prompt \
  --prompt "For text: '{text}', the attitude between '{s_val}' and '{t_val}' is: '{label_val}'" \
  --dest_lang en --docs_limit 1

Mind the case (issue #18): switching to another language may affect on amount of extracted data because of terms_per_context parameter that crops context by fixed and predefined amount of words.

Parameters

• source -- source name from the list of the supported sources.
  • terms_per_context -- amount of words (terms) in between SOURCE and TARGET objects.
  • object-source-types -- filter specific source object types
  • object-target-types -- filter specific target object types
  • relation_types -- list of types, in which items separated with | char; all by default
  • splits -- Manual selection of the data-types related splits that should be chosen for the sampling process; types should be separated by ':' sign; for example: 'train:test'
• sampler -- List of the supported samplers:
  • nn -- CNN/LSTM architecture related, including frames annotation from RuSentiFrames.
    • no-vectorize -- flag is applicable only for nn, and denotes no need to generate embeddings for features
  • bert -- BERT-based, single-input sequence.
  • prompt -- prompt-based sampler for LLM systems [prompt engeneering guide]
    • prompt -- text of the prompt which includes the following parameters:
      • {text} is an original text of the sample
      • {s_val} and {t_val} values of the source and target of the pairs respectively
      • {label_val} value of the label
• writer -- the output format of samples:
  • csv -- for AREnets framework;
  • jsonl -- for OpenNRE framework.
  • sqlite -- SQLite-3.0 database.
• mask_entities -- mask entity mode.
• Text translation parameters:
  • src_lang -- original language of the text.
  • dest_lang -- target language of the text.
• output_dir -- target directory for samples storing
• Limiting the amount of documents from source:
  • docs_limit -- amount of documents to be considered for sampling from the whole source.
  • doc_ids -- list of the document IDs.

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• AREkit framework