Joeddav
Models by this creator
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xlm-roberta-large-xnli
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.
Updated 5/28/2024
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distilbert-base-uncased-go-emotions-student
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The distilbert-base-uncased-go-emotions-student model is a distilled version of a zero-shot classification pipeline trained on the unlabeled GoEmotions dataset. The maintainer explains that this model was trained with mixed precision for 10 epochs using a script for distilling an NLI-based zero-shot model into a more efficient student model. While the original GoEmotions dataset allows for multi-label classification, the teacher model used single-label classification to create pseudo-labels for the student. Similar models include distilbert-base-multilingual-cased-sentiments-student, which was distilled from a zero-shot classification pipeline on the Multilingual Sentiment dataset, and roberta-base-go_emotions, a model trained directly on the GoEmotions dataset. Model Inputs and Outputs Inputs Text**: The model takes text input, such as a sentence or short paragraph. Outputs Emotion Labels**: The model outputs a list of predicted emotion labels and their corresponding scores. The model predicts the probability of the input text expressing emotions like anger, disgust, fear, joy, sadness, and surprise. Capabilities The distilbert-base-uncased-go-emotions-student model can be used for zero-shot emotion classification on text data. While it may not perform as well as a fully supervised model, it can provide a quick and efficient way to gauge the emotional tone of text without the need for labeled training data. What Can I Use It For? This model could be useful for a variety of text-based applications, such as: Analyzing customer feedback or social media posts to understand the emotional sentiment expressed Categorizing movie or book reviews based on the emotions they convey Monitoring online discussions or forums for signs of emotional distress or conflict Things to Try One interesting aspect of this model is that it was distilled from a zero-shot classification pipeline. This means the model was trained without any labeled data, relying instead on pseudo-labels generated by a teacher model. It would be interesting to experiment with different approaches to distillation or to explore how the performance of this student model compares to a fully supervised model trained directly on the GoEmotions dataset. Verifying all URLs: All URLs provided in the links are contained within the prompt.
Updated 5/28/2024