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README.md

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 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
 
-It has been developed through further training of a multilingual fine-tuned model, [paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) using NLI data. Specifically, it was trained on two Catalan NLI datasets: [TE-ca](https://huggingface.co/datasets/projecte-aina/teca) and the professional translation of XNLI into Catalan. The training employed the Multiple Negatives Ranking Loss with Hard Negatives, which leverages triplets composed of a premise, an entailed hypothesis, and a contradiction. It is important to note that, given this format, neutral hypotheses from the NLI datasets were not used for training. However, as a form of data augmentation, the model's training set was expanded by duplicating the triplets, wherein the order of the premise and entailed hypothesis was reversed, resulting in a total of 18,928 triplets.
+It has been developed through further training of a multilingual fine-tuned model, [paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) using NLI data. Specifically, it was trained on two Catalan NLI datasets: [TE-ca](https://huggingface.co/datasets/projecte-aina/teca) and the professional translation of XNLI into Catalan. The training employed the Multiple Negatives Ranking Loss with Hard Negatives, which leverages triplets composed of a premise, an entailed hypothesis, and a contradiction. It is important to note that, given this format, neutral hypotheses from the NLI datasets were not used for training. Additionally, as a form of data augmentation, the model's training set was expanded by duplicating the triplets, wherein the order of the premise and entailed hypothesis was reversed, resulting in a total of 18,928 triplets.
 
 ## Usage (Sentence-Transformers)