ModernBERT-large-nli

Model · Zero-shot classification

finecat-nli-l

Lee Miller

This model is a fine-tune of the excellent tasksource/ModernBERT-large-nli, trained on the dleemiller/FineCat-NLI dataset—a compilation of several high-quality NLI data sources with quality screening and reduction of easy samples in the training split. The training also incorporates logit distillation from MoritzLaurer/DeBERTa-v3-large-mnli-fever-anli-ling-wanli, a top-performing NLI model, particularly on ANLI benchmarks. \begin{equation} \mathcal{L} = \alpha \cdot \mathcal{L}{\text{CE}}(z^{(s)}, y) + \beta \cdot \mathcal{L}{\text{MSE}}(z^{(s)}, z^{(t)}) \end{equation} where \\(z^{(s)}\\) and \\(z^{(t)}\\) are the student and teacher logits, \\(y\\) are the ground truth labels, and…

Model · Zero-shot classification

bart-large-mnli-yahoo-answers

Joe Davison

This model takes facebook/bart-large-mnli and fine-tunes it on Yahoo Answers topic classification. It can be used to predict whether a topic label can be assigned to a given sequence, whether or not the label has been seen before. You can play with an interactive demo of this zero-shot technique with this model, as well as the non-finetuned facebook/bart-large-mnli, here. This model was fine-tuned on topic classification and will perform best at zero-shot topic classification. Use hypothesistemplate="This text is about {}." as this is the template used during fine-tuning. For settings other than topic classification, you can use any model pre-trained on MNLI such as facebook/bart-large-mnli…

Model · Zero-shot classification

bart-large-mnli

AI at Meta

This is the checkpoint for bart-large after being trained on the MultiNLI (MNLI) dataset. - The bart-large model page - BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension Yin et al. proposed a method for using pre-trained NLI models as a ready-made zero-shot sequence classifiers. The method works by posing the sequence to be classified as the NLI premise and to construct a hypothesis from each candidate label. For example, if we want to evaluate whether a sequence belongs to the class "politics", we could construct a hypothesis of This text is about politics.. The probabilities for entailment and contradiction are then converted…

Model · Zero-shot classification

deberta-v3-large-zeroshot-v2.0

Moritz Borrett-Laurer (formerly Laurer)

Models in this series are designed for efficient zeroshot classification with the Hugging Face pipeline. These models can do classification without training data and run on both GPUs and CPUs. An overview of the latest zeroshot classifiers is available in my Zeroshot Classifier Collection. The main update of this zeroshot-v2.0 series of models is that several models are trained on fully commercially-friendly data for users with strict license requirements. These models can do one universal classification task: determine whether a hypothesis is "true" or "not true" given a text (entailment vs. notentailment). This task format is based on the Natural Language Inference task (NLI). The task is…

Model · Zero-shot classification

deberta-v3-large-zeroshot-v1

Moritz Borrett-Laurer (formerly Laurer)

The model is designed for zero-shot classification with the Hugging Face pipeline. The model should be substantially better at zero-shot classification than my other zero-shot models on the The model can do one universal task: determine whether a hypothesis is true or nottrue given a text (also called entailment vs. notentailment). This task format is based on the Natural Language Inference task (NLI). The task is so universal that any classification task can be reformulated into the task. The model was trained on a mixture of 27 tasks and 310 classes that have been reformatted into this universal format. 1. 26 classification tasks with ~400k texts: 'amazonpolarity', 'imdb', 'appreviews'…

Model · Zero-shot classification

deberta-v3-large-zeroshot-v1.1-all-33

Moritz Borrett-Laurer (formerly Laurer)

The model is designed for zero-shot classification with the Hugging Face pipeline. The model can do one universal classification task: determine whether a hypothesis is "true" or "not true" given a text (entailment vs. notentailment). This task format is based on the Natural Language Inference task (NLI). The task is so universal that any classification task can be reformulated into this task. A detailed description of how the model was trained and how it can be used is available in this paper. The model was trained on a mixture of 33 datasets and 387 classes that have been reformatted into this universal format. 1. Five NLI datasets with ~885k texts: "mnli", "anli", "fever", "wanli"…