Moritzlaurer

Models by this creator

👁️

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.

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Updated 5/28/2024

🐍

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.

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Updated 5/28/2024

⚙️

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.

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Updated 5/28/2024

🤷

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

MoritzLaurer

Total Score

83

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.

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Updated 5/28/2024

🚀

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

MoritzLaurer

Total Score

50

Model description: deberta-v3-large-zeroshot-v1.1-all-33 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. not_entailment). 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. Training data The model was trained on a mixture of 33 datasets and 387 classes that have been reformatted into this universal format. Five NLI datasets with ~885k texts: "mnli", "anli", "fever", "wanli", "ling" 28 classification tasks reformatted into the universal NLI format. ~51k cleaned texts were used to avoid overfitting: 'amazonpolarity', 'imdb', 'appreviews', 'yelpreviews', 'rottentomatoes', 'emotiondair', 'emocontext', 'empathetic', 'financialphrasebank', 'banking77', 'massive', 'wikitoxic\_toxicaggregated', 'wikitoxic\_obscene', 'wikitoxic\_threat', 'wikitoxic\_insult', 'wikitoxic\_identityhate', 'hateoffensive', 'hatexplain', 'biasframes\_offensive', 'biasframes\_sex', 'biasframes\_intent', 'agnews', 'yahootopics', 'trueteacher', 'spam', 'wellformedquery', 'manifesto', 'capsotu'. See details on each dataset here: https://github.com/MoritzLaurer/zeroshot-classifier/blob/main/datasets\_overview.csv Note that compared to other NLI models, this model predicts two classes (entailment vs. not_entailment) as opposed to three classes (entailment/neutral/contradiction) The model was only trained on English data. For multilingual use-cases, I recommend machine translating texts to English with libraries like EasyNMT. English-only models tend to perform better than multilingual models and validation with English data can be easier if you don't speak all languages in your corpus. How to use the model Simple zero-shot classification pipeline #!pip install transformers[sentencepiece] from transformers import pipeline text = "Angela Merkel is a politician in Germany and leader of the CDU" hypothesis_template = "This example is about {}" classes_verbalized = ["politics", "economy", "entertainment", "environment"] zeroshot_classifier = pipeline("zero-shot-classification", model="MoritzLaurer/deberta-v3-large-zeroshot-v1.1-all-33") output = zeroshot_classifier(text, classes_verbalised, hypothesis_template=hypothesis_template, multi_label=False) print(output) Details on data and training The code for preparing the data and training & evaluating the model is fully open-source here: https://github.com/MoritzLaurer/zeroshot-classifier/tree/main Hyperparameters and other details are available in this Weights & Biases repo: https://wandb.ai/moritzlaurer/deberta-v3-large-zeroshot-v1-1-all-33/table?workspace=user- Metrics Balanced accuracy is reported for all datasets. deberta-v3-large-zeroshot-v1.1-all-33 was trained on all datasets, with only maximum 500 texts per class to avoid overfitting. The metrics on these datasets are therefore not strictly zeroshot, as the model has seen some data for each task during training. deberta-v3-large-zeroshot-v1.1-heldout indicates zeroshot performance on the respective dataset. To calculate these zeroshot metrics, the pipeline was run 28 times, each time with one dataset held out from training to simulate a zeroshot setup. figure_large_v1.1 deberta-v3-large-mnli-fever-anli-ling-wanli-binary deberta-v3-large-zeroshot-v1.1-heldout deberta-v3-large-zeroshot-v1.1-all-33 datasets mean (w/o nli) 64.1 73.4 85.2 amazonpolarity (2) 94.7 96.6 96.8 imdb (2) 90.3 95.2 95.5 appreviews (2) 93.6 94.3 94.7 yelpreviews (2) 98.5 98.4 98.9 rottentomatoes (2) 83.9 90.5 90.8 emotiondair (6) 49.2 42.1 72.1 emocontext (4) 57 69.3 82.4 empathetic (32) 42 34.4 58 financialphrasebank (3) 77.4 77.5 91.9 banking77 (72) 29.1 52.8 72.2 massive (59) 47.3 64.7 77.3 wikitoxic\_toxicaggreg (2) 81.6 86.6 91 wikitoxic\_obscene (2) 85.9 91.9 93.1 wikitoxic\_threat (2) 77.9 93.7 97.6 wikitoxic\_insult (2) 77.8 91.1 92.3 wikitoxic\_identityhate (2) 86.4 89.8 95.7 hateoffensive (3) 62.8 66.5 88.4 hatexplain (3) 46.9 61 76.9 biasframes\_offensive (2) 62.5 86.6 89 biasframes\_sex (2) 87.6 89.6 92.6 biasframes\_intent (2) 54.8 88.6 89.9 agnews (4) 81.9 82.8 90.9 yahootopics (10) 37.7 65.6 74.3 trueteacher (2) 51.2 54.9 86.6 spam (2) 52.6 51.8 97.1 wellformedquery (2) 49.9 40.4 82.7 manifesto (56) 10.6 29.4 44.1 capsotu (21) 23.2 69.4 74 mnli\_m (2) 93.1 nan 93.1 mnli\_mm (2) 93.2 nan 93.2 fevernli (2) 89.3 nan 89.5 anli\_r1 (2) 87.9 nan 87.3 anli\_r2 (2) 76.3 nan 78 anli\_r3 (2) 73.6 nan 74.1 wanli (2) 82.8 nan 82.7 lingnli (2) 90.2 nan 89.6 Limitations and bias The model can only do text classification tasks. Please consult the original DeBERTa paper and the papers for the different datasets for potential biases. License The base model (DeBERTa-v3) is published under the MIT license. The datasets the model was fine-tuned on are published under a diverse set of licenses. The following table provides an overview of the non-NLI datasets used for fine-tuning, information on licenses, the underlying papers etc.: https://github.com/MoritzLaurer/zeroshot-classifier/blob/main/datasets\_overview.csv Citation If you use this model academically, please cite: @misc{laurer_building_2023, title = {Building {Efficient} {Universal} {Classifiers} with {Natural} {Language} {Inference}}, url = {http://arxiv.org/abs/2312.17543}, doi = {10.48550/arXiv.2312.17543}, abstract = {Generative Large Language Models (LLMs) have become the mainstream choice for fewshot and zeroshot learning thanks to the universality of text generation. Many users, however, do not need the broad capabilities of generative LLMs when they only want to automate a classification task. Smaller BERT-like models can also learn universal tasks, which allow them to do any text classification task without requiring fine-tuning (zeroshot classification) or to learn new tasks with only a few examples (fewshot), while being significantly more efficient than generative LLMs. This paper (1) explains how Natural Language Inference (NLI) can be used as a universal classification task that follows similar principles as instruction fine-tuning of generative LLMs, (2) provides a step-by-step guide with reusable Jupyter notebooks for building a universal classifier, and (3) shares the resulting universal classifier that is trained on 33 datasets with 389 diverse classes. Parts of the code we share has been used to train our older zeroshot classifiers that have been downloaded more than 55 million times via the Hugging Face Hub as of December 2023. Our new classifier improves zeroshot performance by 9.4\%.}, urldate = {2024-01-05}, publisher = {arXiv}, author = {Laurer, Moritz and van Atteveldt, Wouter and Casas, Andreu and Welbers, Kasper}, month = dec, year = {2023}, note = {arXiv:2312.17543 [cs]}, keywords = {Computer Science - Artificial Intelligence, Computer Science - Computation and Language}, } Ideas for cooperation or questions? If you have questions or ideas for cooperation, contact me at m{dot}laurer{at}vu{dot}nl or LinkedIn Debugging and issues Note that DeBERTa-v3 was released on 06.12.21 and older versions of HF Transformers can have issues running the model (e.g. resulting in an issue with the tokenizer). Using Transformers>=4.13 might solve some issues. Hypotheses used for classification The hypotheses in the tables below were used to fine-tune the model. Inspecting them can help users get a feeling for which type of hypotheses and tasks the model was trained on. You can formulate your own hypotheses by changing the hypothesis_template of the zeroshot pipeline. For example: from transformers import pipeline text = "Angela Merkel is a politician in Germany and leader of the CDU" hypothesis_template = "Merkel is the leader of the party: {}" classes_verbalized = ["CDU", "SPD", "Greens"] zeroshot_classifier = pipeline("zero-shot-classification", model="MoritzLaurer/deberta-v3-large-zeroshot-v1.1-all-33") output = zeroshot_classifier(text, classes_verbalized, hypothesis_template=hypothesis_template, multi_label=False) print(output) Note that a few rows in the massive and banking77 datasets contain nan because some classes were so ambiguous/unclear that I excluded them from the data. wellformedquery label hypothesis not\_well\_formed This example is not a well formed Google query well\_formed This example is a well formed Google query. biasframes\_sex label hypothesis not\_sex This example does not contain allusions to sexual content. sex This example contains allusions to sexual content. biasframes\_intent label hypothesis intent The intent of this example is to be offensive/disrespectful. not\_intent The intent of this example is not to be offensive/disrespectful. biasframes\_offensive label hypothesis not\_offensive This example could not be considered offensive, disrespectful, or toxic. offensive This example could be considered offensive, disrespectful, or toxic. financialphrasebank label hypothesis negative The sentiment in this example is negative from an investor's perspective. neutral The sentiment in this example is neutral from an investor's perspective. positive The sentiment in this example is positive from an investor's perspective. rottentomatoes label hypothesis negative The sentiment in this example rotten tomatoes movie review is negative positive The sentiment in this example rotten tomatoes movie review is positive amazonpolarity label hypothesis negative The sentiment in this example amazon product review is negative positive The sentiment in this example amazon product review is positive imdb label hypothesis negative The sentiment in this example imdb movie review is negative positive The sentiment in this example imdb movie review is positive appreviews label hypothesis negative The sentiment in this example app review is negative. positive The sentiment in this example app review is positive. yelpreviews label hypothesis negative The sentiment in this example yelp review is negative. positive The sentiment in this example yelp review is positive. wikitoxic\_toxicaggregated label hypothesis not\_toxicaggregated This example wikipedia comment does not contain toxic language. toxicaggregated This example wikipedia comment contains toxic language. wikitoxic\_obscene label hypothesis not\_obscene This example wikipedia comment does not contain obscene language. obscene This example wikipedia comment contains obscene language. wikitoxic\_threat label hypothesis not\_threat This example wikipedia comment does not contain a threat. threat This example wikipedia comment contains a threat. wikitoxic\_insult label hypothesis insult This example wikipedia comment contains an insult. not\_insult This example wikipedia comment does not contain an insult. wikitoxic\_identityhate label hypothesis identityhate This example wikipedia comment contains identity hate. not\_identityhate This example wikipedia comment does not contain identity hate. hateoffensive label hypothesis hate\_speech This example tweet contains hate speech. neither This example tweet contains neither offensive language nor hate speech. offensive This example tweet contains offensive language without hate speech. hatexplain label hypothesis hate\_speech This example text from twitter or gab contains hate speech. neither This example text from twitter or gab contains neither offensive language nor hate speech. offensive This example text from twitter or gab contains offensive language without hate speech. spam label hypothesis not\_spam This example sms is not spam. spam This example sms is spam. emotiondair label hypothesis anger This example tweet expresses the emotion: anger fear This example tweet expresses the emotion: fear joy This example tweet expresses the emotion: joy love This example tweet expresses the emotion: love sadness This example tweet expresses the emotion: sadness surprise This example tweet expresses the emotion: surprise emocontext label hypothesis angry This example tweet expresses the emotion: anger happy This example tweet expresses the emotion: happiness others This example tweet does not express any of the emotions: anger, sadness, or happiness sad This example tweet expresses the emotion: sadness empathetic label hypothesis afraid The main emotion of this example dialogue is: afraid angry The main emotion of this example dialogue is: angry annoyed The main emotion of this example dialogue is: annoyed anticipating The main emotion of this example dialogue is: anticipating anxious The main emotion of this example dialogue is: anxious apprehensive The main emotion of this example dialogue is: apprehensive ashamed The main emotion of this example dialogue is: ashamed caring The main emotion of this example dialogue is: caring confident The main emotion of this example dialogue is: confident content The main emotion of this example dialogue is: content devastated The main emotion of this example dialogue is: devastated disappointed The main emotion of this example dialogue is: disappointed disgusted The main emotion of this example dialogue is: disgusted embarrassed The main emotion of this example dialogue is: embarrassed excited The main emotion of this example dialogue is: excited faithful The main emotion of this example dialogue is: faithful furious The main emotion of this example dialogue is: furious grateful The main emotion of this example dialogue is: grateful guilty The main emotion of this example dialogue is: guilty hopeful The main emotion of this example dialogue is: hopeful impressed The main emotion of this example dialogue is: impressed jealous The main emotion of this example dialogue is: jealous joyful The main emotion of this example dialogue is: joyful lonely The main emotion of this example dialogue is: lonely nostalgic The main emotion of this example dialogue is: nostalgic prepared The main emotion of this example dialogue is: prepared proud The main emotion of this example dialogue is: proud sad The main emotion of this example dialogue is: sad sentimental The main emotion of this example dialogue is: sentimental surprised The main emotion of this example dialogue is: surprised terrified The main emotion of this example dialogue is: terrified trusting The main emotion of this example dialogue is: trusting agnews label hypothesis Business This example news text is about business news Sci/Tech This example news text is about science and technology Sports This example news text is about sports World This example news text is about world news yahootopics label hypothesis Business & Finance This example question from the Yahoo Q&A forum is categorized in the topic: Business & Finance Computers & Internet This example question from the Yahoo Q&A forum is categorized in the topic: Computers & Internet Education & Reference This example question from the Yahoo Q&A forum is categorized in the topic: Education & Reference Entertainment & Music This example question from the Yahoo Q&A forum is categorized in the topic: Entertainment & Music Family & Relationships This example question from the Yahoo Q&A forum is categorized in the topic: Family & Relationships Health This example question from the Yahoo Q&A forum is categorized in the topic: Health Politics & Government This example question from the Yahoo Q&A forum is categorized in the topic: Politics & Government Science & Mathematics This example question from the Yahoo Q&A forum is categorized in the topic: Science & Mathematics Society & Culture This example question from the Yahoo Q&A forum is categorized in the topic: Society & Culture Sports This example question from the Yahoo Q&A forum is categorized in the topic: Sports massive label hypothesis alarm\_query The example utterance is a query about alarms. alarm\_remove The intent of this example utterance is to remove an alarm. alarm\_set The intent of the example utterance is to set an alarm. audio\_volume\_down The intent of the example utterance is to lower the volume. audio\_volume\_mute The intent of this example utterance is to mute the volume. audio\_volume\_other The example utterance is related to audio volume. audio\_volume\_up The intent of this example utterance is turning the audio volume up. calendar\_query The example utterance is a query about a calendar. calendar\_remove The intent of the example utterance is to remove something from a calendar. calendar\_set The intent of this example utterance is to set something in a calendar. cooking\_query The example utterance is a query about cooking. cooking\_recipe This example utterance is about cooking recipies. datetime\_convert The example utterance is related to date time changes or conversion. datetime\_query The intent of this example utterance is a datetime query. email\_addcontact The intent of this example utterance is adding an email address to contacts. email\_query The example utterance is a query about emails. email\_querycontact The intent of this example utterance is to query contact details. email\_sendemail The intent of the example utterance is to send an email. general\_greet This example utterance is a general greet. general\_joke The intent of the example utterance is to hear a joke. general\_quirky nan iot\_cleaning The intent of the example utterance is for an IoT device to start cleaning. iot\_coffee The intent of this example utterance is for an IoT device to make coffee. iot\_hue\_lightchange The intent of this example utterance is changing the light. iot\_hue\_lightdim The intent of the example utterance is to dim the lights. iot\_hue\_lightoff The example utterance is related to turning the lights off. iot\_hue\_lighton The example utterance is related to turning the lights on. iot\_hue\_lightup The intent of this example utterance is to brighten lights. iot\_wemo\_off The intent of this example utterance is turning an IoT device off. iot\_wemo\_on The intent of the example utterance is to turn an IoT device on. lists\_createoradd The example utterance is related to creating or adding to lists. lists\_query The example utterance is a query about a list. lists\_remove The intent of this example utterance is to remove a list or remove something from a list. music\_dislikeness The intent of this example utterance is signalling music dislike. music\_likeness The example utterance is related to liking music. music\_query The example utterance is a query about music. music\_settings The intent of the example utterance is to change music settings. news\_query The example utterance is a query about the news. play\_audiobook The example utterance is related to playing audiobooks. play\_game The intent of this example utterance is to start playing a game. play\_music The intent of this example utterance is for an IoT device to play music. play\_podcasts The example utterance is related to playing podcasts. play\_radio The intent of the example utterance is to play something on the radio. qa\_currency This example utteranceis about currencies. qa\_definition The example utterance is a query about a definition. qa\_factoid The example utterance is a factoid question. qa\_maths The example utterance is a question about maths. qa\_stock This example utterance is about stocks. recommendation\_events This example utterance is about event recommendations. recommendation\_locations The intent of this example utterance is receiving recommendations for good locations. recommendation\_movies This example utterance is about movie recommendations. social\_post The example utterance is about social media posts. social\_query The example utterance is a query about a social network. takeaway\_order The intent of this example utterance is to order takeaway food. takeaway\_query This example utterance is about takeaway food. transport\_query The example utterance is a query about transport or travels. transport\_taxi The intent of this example utterance is to get a taxi. transport\_ticket This example utterance is about transport tickets. transport\_traffic This example utterance is about transport or traffic. weather\_query This example utterance is a query about the wheather. banking77 label hypothesis Refund\_not\_showing\_up This customer example message is about a refund not showing up. activate\_my\_card This banking customer example message is about activating a card. age\_limit This banking customer example message is related to age limits. apple\_pay\_or\_google\_pay This banking customer example message is about apple pay or google pay atm\_support This banking customer example message requests ATM support. automatic\_top\_up This banking customer example message is about automatic top up. balance\_not\_updated\_after\_bank\_transfer This banking customer example message is about a balance not updated after a transfer. balance\_not\_updated\_after\_cheque\_or\_cash\_deposit This banking customer example message is about a balance not updated after a cheque or cash deposit. beneficiary\_not\_allowed This banking customer example message is related to a beneficiary not being allowed or a failed transfer. cancel\_transfer This banking customer example message is related to the cancellation of a transfer. card\_about\_to\_expire This banking customer example message is related to the expiration of a card. card\_acceptance This banking customer example message is related to the scope of acceptance of a card. card\_arrival This banking customer example message is about the arrival of a card. card\_delivery\_estimate This banking customer example message is about a card delivery estimate or timing. card\_linking nan card\_not\_working This banking customer example message is about a card not working. card\_payment\_fee\_charged This banking customer example message is about a card payment fee. card\_payment\_not\_recognised This banking customer example message is about a payment the customer does not recognise. card\_payment\_wrong\_exchange\_rate This banking customer example message is about a wrong exchange rate. card\_swallowed This banking customer example message is about a card swallowed by a machine. cash\_withdrawal\_charge This banking customer example message is about a cash withdrawal charge. cash\_withdrawal\_not\_recognised This banking customer example message is about an unrecognised cash withdrawal. change\_pin This banking customer example message is about changing a pin code. compromised\_card This banking customer example message is about a compromised card. contactless\_not\_working This banking customer example message is about contactless not working country\_support This banking customer example message is about country-specific support. declined\_card\_payment This banking customer example message is about a declined card payment. declined\_cash\_withdrawal This banking customer example message is about a declined cash withdrawal. declined\_transfer This banking customer example message is about a declined transfer. direct\_debit\_payment\_not\_recognised This banking customer example message is about an unrecognised direct debit payment. disposable\_card\_limits This banking customer example message is about the limits of disposable cards. edit\_personal\_details This banking customer example message is about editing personal details. exchange\_charge This banking customer example message is about exchange rate charges. exchange\_rate This banking customer example message is about exchange rates. exchange\_via\_app nan extra\_charge\_on\_statement This banking customer example message is about an extra charge. failed\_transfer This banking customer example message is about a failed transfer. fiat\_currency\_support This banking customer example message is about fiat currency support get\_disposable\_virtual\_card This banking customer example message is about getting a disposable virtual card. get\_physical\_card nan getting\_spare\_card This banking customer example message is about getting a spare card. getting\_virtual\_card This banking customer example message is about getting a virtual card. lost\_or\_stolen\_card This banking customer example message is about a lost or stolen card. lost\_or\_stolen\_phone This banking customer example message is about a lost or stolen phone. order\_physical\_card This banking customer example message is about ordering a card. passcode\_forgotten This banking customer example message is about a forgotten passcode. pending\_card\_payment This banking customer example message is about a pending card payment. pending\_cash\_withdrawal This banking customer example message is about a pending cash withdrawal. pending\_top\_up This banking customer example message is about a pending top up. pending\_transfer This banking customer example message is about a pending transfer. pin\_blocked This banking customer example message is about a blocked pin. receiving\_money This banking customer example message is about receiving money. request\_refund This banking customer example message is about a refund request. reverted\_card\_payment? This banking customer example message is about reverting a card payment. supported\_cards\_and\_currencies nan terminate\_account This banking customer example message is about terminating an account. top\_up\_by\_bank\_transfer\_charge nan top\_up\_by\_card\_charge This banking customer example message is about the charge for topping up by card. top\_up\_by\_cash\_or\_cheque This banking customer example message is about topping up by cash or cheque. top\_up\_failed This banking customer example message is about top up issues or failures. top\_up\_limits This banking customer example message is about top up limitations. top\_up\_reverted This banking customer example message is about issues with topping up. topping\_up\_by\_card This banking customer example message is about topping up by card. transaction\_charged\_twice This banking customer example message is about a transaction charged twice. transfer\_fee\_charged This banking customer example message is about an issue with a transfer fee charge. transfer\_into\_account This banking customer example message is about transfers into the customer's own account. transfer\_not\_received\_by\_recipient This banking customer example message is about a transfer that has not arrived yet. transfer\_timing This banking customer example message is about transfer timing. unable\_to\_verify\_identity This banking customer example message is about an issue with identity verification. verify\_my\_identity This banking customer example message is about identity verification. verify\_source\_of\_funds This banking customer example message is about the source of funds. verify\_top\_up This banking customer example message is about verification and top ups virtual\_card\_not\_working This banking customer example message is about a virtual card not working visa\_or\_mastercard This banking customer example message is about types of bank cards. why\_verify\_identity This banking customer example message questions why identity verification is necessary. wrong\_amount\_of\_cash\_received This banking customer example message is about a wrong amount of cash received. wrong\_exchange\_rate\_for\_cash\_withdrawal This banking customer example message is about a wrong exchange rate for a cash withdrawal. trueteacher label hypothesis factually\_consistent The example summary is factually consistent with the full article. factually\_inconsistent The example summary is factually inconsistent with the full article. capsotu label hypothesis Agriculture This example text from a US presidential speech is about agriculture Civil Rights This example text from a US presidential speech is about civil rights or minorities or civil liberties Culture This example text from a US presidential speech is about cultural policy Defense This example text from a US presidential speech is about defense or military Domestic Commerce This example text from a US presidential speech is about banking or finance or commerce Education This example text from a US presidential speech is about education Energy This example text from a US presidential speech is about energy or electricity or fossil fuels Environment This example text from a US presidential speech is about the environment or water or waste or pollution Foreign Trade This example text from a US presidential speech is about foreign trade Government Operations This example text from a US presidential speech is about government operations or administration Health This example text from a US presidential speech is about health Housing This example text from a US presidential speech is about community development or housing issues Immigration This example text from a US presidential speech is about migration International Affairs This example text from a US presidential speech is about international affairs or foreign aid Labor This example text from a US presidential speech is about employment or labour Law and Crime This example text from a US presidential speech is about law, crime or family issues Macroeconomics This example text from a US presidential speech is about macroeconomics Public Lands This example text from a US presidential speech is about public lands or water management Social Welfare This example text from a US presidential speech is about social welfare Technology This example text from a US presidential speech is about space or science or technology or communications Transportation This example text from a US presidential speech is about transportation manifesto label hypothesis Agriculture and Farmers: Positive This example text from a political party manifesto is positive towards policies for agriculture and farmers Anti-Growth Economy: Positive This example text from a political party manifesto is in favour of anti-growth politics Anti-Imperialism This example text from a political party manifesto is anti-imperialistic, for example against controlling other countries and for greater self-government of colonies Centralisation This example text from a political party manifesto is in favour of political centralisation Civic Mindedness: Positive This example text from a political party manifesto is positive towards national solidarity, civil society or appeals for public spiritedness or against anti-social attitudes Constitutionalism: Negative This example text from a political party manifesto is positive towards constitutionalism Constitutionalism: Positive This example text from a political party manifesto is positive towards constitutionalism and the status quo of the constitution Controlled Economy This example text from a political party manifesto is supportive of direct government control of the economy, e.g. price control or minimum wages Corporatism/Mixed Economy This example text from a political party manifesto is positive towards cooperation of government, employers, and trade unions simultaneously Culture: Positive This example text from a political party manifesto is in favour of cultural policies or leisure facilities, for example museus, libraries or public sport clubs Decentralization This example text from a political party manifesto is for decentralisation or federalism Democracy This example text from a political party manifesto favourably mentions democracy or democratic procedures or institutions Economic Goals This example text from a political party manifesto is a broad/general statement on economic goals without specifics Economic Growth: Positive This example text from a political party manifesto is supportive of economic growth, for example facilitation of more production or government aid for growth Economic Orthodoxy This example text from a political party manifesto is for economic orthodoxy, for example reduction of budget deficits, thrift or a strong currency Economic Planning This example text from a political party manifesto is positive towards government economic planning, e.g. policy plans or strategies Education Expansion This example text from a political party manifesto is about the need to expand/improve policy on education Education Limitation This example text from a political party manifesto is sceptical towards state expenditure on education, for example in favour of study fees or private schools Environmental Protection This example text from a political party manifesto is in favour of environmental protection, e.g. fighting climate change or 'green' policies or preservation of natural resources or animal rights Equality: Positive This example text from a political party manifesto is positive towards equality or social justice, e.g. protection of underprivileged groups or fair distribution of resources European Community/Union: Negative This example text from a political party manifesto negatively mentions the EU or European Community European Community/Union: Positive This example text from a political party manifesto is positive towards the EU or European Community, for example EU expansion and integration Foreign Special Relationships: Negative This example text from a political party manifesto is negative towards particular countries Foreign Special Relationships: Positive This example text from a political party manifesto is positive towards particular countries Free Market Economy This example text from a political party manifesto is in favour of a free market economy and capitalism Freedom and Human Rights This example text from a political party manifesto is in favour of freedom and human rights, for example freedom of speech, assembly or against state coercion or for individualism Governmental and Administrative Efficiency This example text from a political party manifesto is in favour of efficiency in government/administration, for example by restructuring civil service or improving bureaucracy Incentives: Positive This example text from a political party manifesto is favourable towards supply side economic policies supporting businesses, for example for incentives like subsidies or tax breaks Internationalism: Negative This example text from a political party manifesto is sceptical of internationalism, for example negative towards international cooperation, in favour of national sovereignty and unilaterialism Internationalism: Positive This example text from a political party manifesto is in favour of international cooperation with other countries, for example mentions the need for aid to developing countries, or global governance Keynesian Demand Management This example text from a political party manifesto is for keynesian demand management and demand side economic policies Labour Groups: Negative This example text from a political party manifesto is negative towards labour groups and unions Labour Groups: Positive This example text from a political party manifesto is positive towards labour groups, for example for good working conditions, fair wages or unions Law and Order: Positive This example text from a political party manifesto is positive towards law and order and strict law enforcement Market Regulation This example text from a political party manifesto is supports market regulation for a fair and open market, for example for consumer protection or for increased competition or for social market economy Marxist Analysis This example text from a political party manifesto is positive towards Marxist-Leninist ideas or uses specific Marxist terminology Middle Class and Professional Groups This example text from a political party manifesto favourably references the middle class, e.g. white colar groups or the service sector Military: Negative This example text from a political party manifesto is negative towards the military, for example for decreasing military spending or disarmament Military: Positive This example text from a political party manifesto is positive towards the military, for example for military spending or rearmament or military treaty obligations Multiculturalism: Negative This example text from a political party manifesto is sceptical towards multiculturalism, or for cultural integration or appeals to cultural homogeneity in society Multiculturalism: Positive This example text from a political party manifesto favourably mentions cultural diversity, for example for freedom of religion or linguistic heritages National Way of Life: Negative This example text from a political party manifesto unfavourably mentions a country's nation and history, for example sceptical towards patriotism or national pride National Way of Life: Positive This example text from a political party manifesto is positive towards the national way of life and history, for example pride of citizenship or appeals to patriotism Nationalisation This example text from a political party manifesto is positive towards government ownership of industries or land or for economic nationalisation Non-economic Demographic Groups This example text from a political party manifesto favourably mentions non-economic demographic groups like women, students or specific age groups Peace This example text from a political party manifesto is positive towards peace and peaceful means of solving crises, for example in favour of negotiations and ending wars Political Authority This example text from a political party manifesto mentions the speaker's competence to govern or other party's lack of such competence, or favourably mentions a strong/stable government Political Corruption This example text from a political party manifesto is negative towards political corruption or abuse of political/bureaucratic power Protectionism: Negative This example text from a political party manifesto is negative towards protectionism, in favour of free trade Protectionism: Positive This example text from a political party manifesto is in favour of protectionism, for example tariffs, export subsidies Technology and Infrastructure: Positive This example text from a political party manifesto is about technology and infrastructure, e.g. the importance of modernisation of industry, or supportive of public spending on infrastructure/tech Traditional Morality: Negative This example text from a political party manifesto is negative towards traditional morality, for example against religious moral values, for divorce or abortion, for modern families or separation of church and state Traditional Morality: Positive This example text from a political party manifesto is favourable towards traditional or religious values, for example for censorship of immoral behavour, for traditional family values or religious institutions Underprivileged Minority Groups This example text from a political party manifesto favourably mentions underprivileged minorities, for example handicapped, homosexuals or immigrants Welfare State Expansion This example text from a political party manifesto is positive towards the welfare state, e.g. health care, pensions or social housing Welfare State Limitation This example text from a political party manifesto is for limiting the welfare state, for example public funding for social services or social security, e.g. private care before state care

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Updated 5/30/2024