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

Maintainer: MoritzLaurer

Total Score

83

Last updated 5/28/2024

🤷

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

Create account to get full access

or

If you already have an account, we'll log you in

Model overview

The DeBERTa-v3-large-mnli-fever-anli-ling-wanli model is a large, high-performing natural language inference (NLI) model. It was fine-tuned on a combination of popular NLI datasets, including MultiNLI, Fever-NLI, ANLI, LingNLI, and WANLI. This model significantly outperforms other large models on the ANLI benchmark and can be used for zero-shot classification.

The foundation model is DeBERTa-v3-large from Microsoft, which combines several recent innovations compared to classical Masked Language Models like BERT and RoBERTa.

Similar models include the DeBERTa-v3-base-mnli-fever-anli and mDeBERTa-v3-base-xnli-multilingual-nli-2mil7 models, which are smaller or multilingual variants of the DeBERTa architecture.

Model inputs and outputs

Inputs

  • Sequence to classify: A piece of text you want to classify
  • Candidate labels: A list of possible labels for the input sequence

Outputs

  • Labels: The predicted label(s) for the input sequence
  • Scores: The probability scores for each predicted label

Capabilities

The DeBERTa-v3-large-mnli-fever-anli-ling-wanli model is highly capable at natural language inference (NLI) tasks. It can determine whether a given hypothesis is entailed by, contradicted by, or neutral with respect to a given premise. For example, given the premise "I first thought that I liked the movie, but upon second thought it was actually disappointing" and the hypothesis "The movie was not good", the model would correctly predict a "contradiction" relationship.

What can I use it for?

This model is well-suited for zero-shot text classification tasks, where you want to classify a piece of text into one or more categories without any labeled training data for that specific task. For instance, you could use it to classify news articles into topics like "politics", "economy", "entertainment", and "environment" without having to annotate a large dataset yourself.

Additionally, the model's strong NLI capabilities make it useful for applications like question answering, entailment-based search, and natural language inference-based reasoning.

Things to try

One interesting thing to try with this model is to experiment with the candidate labels you provide. Since it is a zero-shot classifier, the model can potentially classify the input text into any labels you specify, even if they are not part of the original training data. This allows for a lot of flexibility in terms of the types of classifications you can perform.

You could also try using the model for cross-lingual classification, by providing candidate labels in a different language than the input text. The multilingual DeBERTa-v3 architecture should allow for some degree of cross-lingual transfer, though the performance may not be as high as for the languages included in the fine-tuning data.



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

Related Models

⚙️

DeBERTa-v3-base-mnli-fever-anli

MoritzLaurer

Total Score

167

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.

Read more

Updated Invalid Date

🐍

mDeBERTa-v3-base-mnli-xnli

MoritzLaurer

Total Score

208

The mDeBERTa-v3-base-mnli-xnli is a multilingual model that can perform natural language inference (NLI) on 100 languages. It was pre-trained by Microsoft on the CC100 multilingual dataset and then fine-tuned on the XNLI dataset, which contains hypothesis-premise pairs from 15 languages, as well as the English MNLI dataset. As of December 2021, this model is the best performing multilingual base-sized transformer model, as introduced by Microsoft in this paper. For a smaller, faster (but less performant) model, you can try multilingual-MiniLMv2-L6-mnli-xnli. The maintainer of the mDeBERTa-v3-base-mnli-xnli model is MoritzLaurer. Model inputs and outputs Inputs Text sequences**: The model takes text sequences as input, which can be in any of the 100 languages it was pre-trained on. Outputs Entailment, neutral, or contradiction prediction**: The model outputs a prediction indicating whether the input text sequence entails, contradicts, or is neutral with respect to a provided hypothesis. Probability scores**: The model also outputs probability scores for each of the three possible predictions (entailment, neutral, contradiction). Capabilities The mDeBERTa-v3-base-mnli-xnli model is highly capable at performing natural language inference tasks across a wide range of languages. It can be used for zero-shot classification, where the model is able to classify text without seeing examples of that specific task during training. Some example use cases include: Determining if a given premise entails, contradicts, or is neutral towards a hypothesis, in any of the 100 supported languages. Performing multilingual text classification by framing the task as a natural language inference problem. Building multilingual chatbots or virtual assistants that can handle queries across many languages. What can I use it for? The mDeBERTa-v3-base-mnli-xnli model is well-suited for a variety of natural language processing tasks that require multilingual capabilities, such as: Zero-shot classification: Classify text into pre-defined categories without training on that specific task. Natural language inference: Determine if a given premise entails, contradicts, or is neutral towards a hypothesis. Multilingual question answering Multilingual text summarization Multilingual sentiment analysis Companies working on global products and services could benefit from using this model to handle user interactions and content in multiple languages. Things to try One interesting aspect of the mDeBERTa-v3-base-mnli-xnli model is its ability to perform well on languages it was not fine-tuned on during the NLI task, thanks to the strong cross-lingual transfer capabilities of the underlying mDeBERTa-v3-base model. This means you can use the model to classify text in languages like Bulgarian, Greek, and Thai, which were not included in the XNLI fine-tuning dataset. To explore this, you could try providing the model with input text in a less common language and see how it performs on zero-shot classification or natural language inference tasks. The maintainer notes that performance may be lower than for the fine-tuned languages, but it can still be a useful starting point for multilingual applications.

Read more

Updated Invalid Date

👁️

mDeBERTa-v3-base-xnli-multilingual-nli-2mil7

MoritzLaurer

Total Score

227

mDeBERTa-v3-base-xnli-multilingual-nli-2mil7 is a multilingual model capable of performing natural language inference (NLI) on 100 languages. It was created by MoritzLaurer and is based on the mDeBERTa-v3-base model, which was pre-trained by Microsoft on the CC100 multilingual dataset. The model was then fine-tuned on the XNLI dataset and the multilingual-NLI-26lang-2mil7 dataset, which together contain over 2.7 million hypothesis-premise pairs in 27 languages. As of December 2021, this model is the best performing multilingual base-sized transformer model introduced by Microsoft. Similar models include the xlm-roberta-large-xnli model, which is a fine-tuned XLM-RoBERTa-large model for multilingual NLI, the distilbert-base-multilingual-cased-sentiments-student model, which is a distilled version of a model for multilingual sentiment analysis, and the bert-base-NER model, which is a BERT-based model for named entity recognition. Model inputs and outputs Inputs Premise**: The first part of a natural language inference (NLI) example, which is a natural language statement. Hypothesis**: The second part of an NLI example, which is another natural language statement that may or may not be entailed by the premise. Outputs Label probabilities**: The model outputs the probability of the hypothesis being entailed by the premise, the probability of the hypothesis being neutral with respect to the premise, and the probability of the hypothesis contradicting the premise. Capabilities The mDeBERTa-v3-base-xnli-multilingual-nli-2mil7 model is capable of performing multilingual natural language inference, which means it can determine whether a given hypothesis is entailed by, contradicts, or is neutral with respect to a given premise, across 100 different languages. This makes it useful for applications that require cross-lingual understanding, such as multilingual question answering, content classification, and textual entailment. What can I use it for? The mDeBERTa-v3-base-xnli-multilingual-nli-2mil7 model can be used for a variety of natural language processing tasks that require multilingual understanding, such as: Multilingual zero-shot classification**: The model can be used to classify text in any of the 100 supported languages into predefined categories, without requiring labeled training data for each language. Multilingual question answering**: The model can be used to determine whether a given answer is entailed by, contradicts, or is neutral with respect to a given question, across multiple languages. Multilingual textual entailment**: The model can be used to determine whether one piece of text logically follows from or contradicts another, in a multilingual setting. Things to try One interesting aspect of the mDeBERTa-v3-base-xnli-multilingual-nli-2mil7 model is its ability to perform zero-shot classification across a wide range of languages. This means you can use the model to classify text in languages it was not explicitly trained on, by framing the classification task as a natural language inference problem. For example, you could use the model to classify Romanian text into predefined categories, even though the model was not fine-tuned on Romanian data. Another thing to try would be to use the model for multilingual text generation, by generating hypotheses that are entailed by, contradictory to, or neutral with respect to a given premise, in different languages. This could be useful for applications like multilingual dialogue systems or language learning tools.

Read more

Updated Invalid Date

🤿

xlm-roberta-large-xnli

joeddav

Total Score

178

The xlm-roberta-large-xnli model is based on the XLM-RoBERTa large model and is fine-tuned on a combination of Natural Language Inference (NLI) data in 15 languages. This makes it well-suited for zero-shot text classification tasks, especially in languages other than English. Compared to similar models like bart-large-mnli and bert-base-uncased, the xlm-roberta-large-xnli model leverages multilingual pretraining to extend its capabilities across a broader range of languages. Model Inputs and Outputs Inputs Text sequences**: The model can take in text sequences in any of the 15 languages it was fine-tuned on, including English, French, Spanish, German, and more. Candidate labels**: When using the model for zero-shot classification, you provide a set of candidate labels that the input text should be classified into. Outputs Label probabilities**: The model outputs a probability distribution over the provided candidate labels, indicating the likelihood of the input text belonging to each class. Capabilities The xlm-roberta-large-xnli model is particularly adept at zero-shot text classification tasks, where it can classify text into predefined categories without any specific fine-tuning on that task. This makes it useful for a variety of applications, such as sentiment analysis, topic classification, and intent detection, across a diverse range of languages. What Can I Use It For? You can use the xlm-roberta-large-xnli model for zero-shot text classification in any of the 15 supported languages. This could be helpful for building multilingual applications that need to categorize text, such as customer service chatbots that can understand and respond to queries in multiple languages. The model could also be fine-tuned on domain-specific datasets to create custom classification models for specialized use cases. Things to Try One interesting aspect of the xlm-roberta-large-xnli model is its ability to handle cross-lingual classification, where the input text and candidate labels can be in different languages. You could experiment with this by providing a Russian text sequence and English candidate labels, for example, and see how the model performs. Additionally, you could explore ways to further fine-tune the model on your specific use case to improve its accuracy and effectiveness.

Read more

Updated Invalid Date