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This software is used to compute dense vectorisations (sentence embeddings) of sequences of sentences of natural text. It is able to handle multilingual documents until the model used is a multilingual one. This relies on the S-BERT architecture, software and models (https://www.sbert.net/). It computes dense vector representations for tokens, lemmas, entities, etc. of your datasets.

Codes to implement regularized multilevel regression with post-stratification in the context of bycatch estimation

tp vue js pinia

Requests to collect documents relating real-world events (themselves described using wikivents) stored in a global index (provided by database_infrastructure_text_mining).

This software is used to compute TF IDF weighting from texts that are based on the document_tracking_resources format. Vectors and weightings are computed thanks to a resource file that contains a representation of the language used in the same context as the text to weight (news features to weight texts published in the news).

Resources and Python API to manipulate datasets of news documents. It manipulates data in the .pickle format with the help of pandas and numpy. It can perform operations on the datasets.