DeBERTa-v3-base-mnli-fever-anli

Maintainer: MoritzLaurer

Total Score

167

Last updated 5/28/2024

⚙️

PropertyValue
Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

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Model overview

The DeBERTa-v3-base-mnli-fever-anli model is a large language model fine-tuned on several natural language inference (NLI) datasets, including MultiNLI, Fever-NLI, and Adversarial-NLI (ANLI). It is based on the DeBERTa-v3-base model from Microsoft, which has been shown to outperform previous versions of DeBERTa on the ANLI benchmark. This model was created and maintained by MoritzLaurer.

Similar models include the mDeBERTa-v3-base-xnli-multilingual-nli-2mil7 model, which is a multilingual version fine-tuned on the XNLI and multilingual-NLI-26lang-2mil7 datasets, and the bert-base-NER model, which is a BERT-base model fine-tuned for named entity recognition.

Model inputs and outputs

Inputs

  • Sequence of text: The model takes a sequence of text as input, which can be a single sentence or a pair of sentences (e.g., a premise and a hypothesis).

Outputs

  • Entailment, neutral, or contradiction probability: The model outputs the probability that the input sequence represents an entailment, neutral, or contradiction relationship between the premise and hypothesis.

Capabilities

The DeBERTa-v3-base-mnli-fever-anli model is capable of performing high-quality natural language inference (NLI) tasks, where the goal is to determine the logical relationship (entailment, contradiction, or neutral) between a premise and a hypothesis. This model outperforms almost all large models on the ANLI benchmark, making it a powerful tool for applications that require robust reasoning about textual relationships.

What can I use it for?

This model can be used for a variety of applications that involve textual reasoning, such as:

  • Question answering: By framing questions as hypotheses and passages as premises, the model can be used to determine the most likely answer.
  • Dialogue systems: The model can be used to understand the intent and logical relationship between utterances in a conversation.
  • Fact-checking: The model can be used to evaluate the veracity of claims by checking if they are entailed by or contradicted by reliable sources.

Things to try

One interesting aspect of this model is its strong performance on the ANLI benchmark, which tests the model's ability to handle adversarial and challenging NLI examples. Researchers could explore using this model as a starting point for further fine-tuning on domain-specific NLI tasks, or investigating the model's reasoning capabilities in greater depth.

Additionally, since the model is based on the DeBERTa-v3 architecture, which has been shown to outperform previous versions of DeBERTa, it could be interesting to compare the performance of this model to other DeBERTa-based models or to explore the impact of the various pre-training and fine-tuning strategies used in its development.



This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

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