Apex-flash

Model · Zero-shot classification

DeBERTa-v3-large-mnli-fever-anli-ling-wanli

Moritz Borrett-Laurer (formerly Laurer)

This model was fine-tuned on the MultiNLI, Fever-NLI, Adversarial-NLI (ANLI), LingNLI and WANLI datasets, which comprise 885 242 NLI hypothesis-premise pairs. This model is the best performing NLI model on the Hugging Face Hub as of 06.06.22 and can be used for zero-shot classification. It significantly outperforms all other large models on the ANLI benchmark. The foundation model is DeBERTa-v3-large from Microsoft. DeBERTa-v3 combines several recent innovations compared to classical Masked Language Models like BERT, RoBERTa etc., see the paper DeBERTa-v3-large-mnli-fever-anli-ling-wanli was trained on the MultiNLI, Fever-NLI, Adversarial-NLI (ANLI), LingNLI and WANLI datasets, which…

Model · Zero-shot classification

nli-deberta-v3-large

Sentence Transformers - Cross-Encoders

This model was trained using SentenceTransformers Cross-Encoder class. This model is based on microsoft/deberta-v3-large The model was trained on the SNLI and MultiNLI datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral. For futher evaluation results, see SBERT.net - Pretrained Cross-Encoder. Pre-trained models can be used like this: You can use the model also directly with Transformers library (without SentenceTransformers library): This model can also be used for zero-shot-classification

Model · Zero-shot classification

deberta-v3-large-tasksource-nli

Damien Sileo

DeBERTa-v3-large fine-tuned with multi-task learning on 600 tasks of the tasksource collection You can further fine-tune this model to use it for any classification or multiple-choice task. This checkpoint has strong zero-shot validation performance on many tasks (e.g. 77% on WNLI). The untuned model CLS embedding also has strong linear probing performance (90% on MNLI), due to the multitask training. This is the shared model with the MNLI classifier on top. Its encoder was trained on many datasets including bigbench, Anthropic rlhf, anli... alongside many NLI and classification tasks with a SequenceClassification heads while using only one shared encoder. Each task had a specific CLS…

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"…