Update README.md

README.md

@@ -26,18 +26,21 @@ Croissant Aligned Instruct is an instruction-formatted version of the parallel F
 [(subset: aligned_36b)](https://huggingface.co/datasets/croissantllm/croissant_dataset_no_web_data/tree/main/aligned_36b).
 
 ### The original dataset
-The original CroissantAligned dataset contains samples of parallel French/English (or English/French) data from OPUS’13 (99.6% of the data in CroissantAligned), thesis abstracts, and translated [song lyrics](from www.lacoccinelle.net).
+The original CroissantAligned dataset contains samples of parallel French/English (or English/French) data from OPUS’13 (99.6% of the data in CroissantAligned), thesis abstracts, and translated [song lyrics](www.lacoccinelle.net).
 
 Data extracted from OPUS takes the form of sentences pairs, where one sentence is in French and the other is in English. OPUS pairs were passed through a custom pipeline designed to select the highest quality translation examples. The thesis abstract subset contains thesis abstracts paired with translations written by the thesis authors. The song lyrics are translated by contributors to www.lacoccinelle.net. Parallel data are used to
 boost the multilingual capabilities of models trained on them (Faysse et al. (2024). ["CroissantLLM: A Truly Bilingual French-English Language Model"](https://arxiv.org/abs/2402.00786)).
 
 
 ### Preparation of the instruction dataset
-The instruction examples were prepared as follows:
+Croissant Aligned Instruct uses the Croissant Aligned data formatted for the [Lucie Training Dataset](https://huggingface.co/datasets/OpenLLM-France/Lucie-Training-Dataset).
 
-Language separation and tagging: The original text field of the Croissant dataset contains a sentence or passage in French or English immediately followed by its translation without any indication of which passage is in which language. The first step was thus to split each text into separate, monolingual passages and tag each passage with the appropriate language code, identified automatically using the langid Python library. In the Lucie Training Dataset, the extra metadata field for CroissantAligned contains separate keys, text_fr for French and text_en for English, that stores the texts separately.
+Pre-processing of the data for the Lucie Training Dataset proceeded as follows: 
+* Language separation and tagging: The original text field of the Croissant dataset contains a sentence or passage in French or English immediately followed by its translation without any indication of which passage is in which language. The first step was thus to split each text into separate, monolingual passages and tag each passage with the appropriate language code, identified automatically using the langid Python library. In the Lucie Training Dataset, the extra metadata field for CroissantAligned contains separate keys, text_fr for French and text_en for English, that stores the texts separately.
 
-Random combination of texts prefixed by language : To create the text values, each monolingual text was repaired with its translation, but random separators and various methods of prefixing the text with the language (name or code) were added. This was done as a precaution to prevent models trained on this data from switching languages when generating text and can be seen as a very basic instruction to translate the source (first) text into the target (second) text.
+* Random combination of texts prefixed by language : To create the text values, each monolingual text was repaired with its translation, but random separators and various methods of prefixing the text with the language (name or code) were added. This was done as a precaution to prevent models trained on this data from switching languages when generating text and can be seen as a very basic instruction to translate the source (first) text into the target (second) text.
+
+To create the instructions, a number of templates were created for the "user" role, e.g. "Transformez ce texte en Francais: {text}" or "Could you translate the provided text from French to English\t {text}". The first member of a language pair was treated as "text" for the user field and the second was treated as the assistant response. 
 
 ## Citation