Presentation We introduce the Bloomz 560m NLI model, fine tuned from the Bloomz 560m chat dpo foundation model. This model is trained on a Natural Language Inference (NLI) task in a language agnostic manner. The NLI task involves determining the semantic relationship between a hypothesis and a set of premises, often expressed as pairs of sentences. The goal is to predict textual entailment (does sentence A imply/contradict/neither sentence B?) and is a classification task (given two sentences, predict one of the three labels). If sentence A is called premise , and sentence B is called hypothesis , then the goal of the modelization is to estimate the following: $$P(premise=c\in\{contradiction, entailment, neutral\}\vert hypothesis)$$ Language agnostic approach It should be noted that hypotheses and premises are randomly chosen between English and French, with each language combination representing a probability of 25%. Performance class precision (%) f1 score (%) support : : : : : : : : global 69.20 68.35 5,010 contradiction 63.66 70.60 1,670 entailment 73.45 73.01 1,670 neutral 70.75 61.45 1,670 Benchmark Here are the performances for both the hypothesis and premise in French: mode…
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