distilbert-base-multilingual-cased-sentiments-student

Maintainer: lxyuan

Total Score

204

Last updated 5/21/2024

🚀

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

Get summaries of the top AI models delivered straight to your inbox:

Model Overview

distilbert-base-multilingual-cased-sentiments-student is a distilled version of a zero-shot classification pipeline on the Multilingual Sentiment dataset. It was created by lxyuan using a process of knowledge distillation, where a larger "teacher" model (in this case, MoritzLaurer/mDeBERTa-v3-base-mnli-xnli) is used to train a smaller "student" model (in this case, distilbert-base-multilingual-cased). This allows the student model to achieve high performance while being more efficient and lightweight.

The model is capable of performing zero-shot sentiment classification on multilingual text, determining whether a given piece of text has a positive, negative, or neutral sentiment. It can handle text in English, Malay, Japanese, and potentially other languages. This makes it useful for applications that require sentiment analysis across multiple languages, without the need for language-specific training data.

Model Inputs and Outputs

Inputs

  • Text: A piece of text, in any of the supported languages (English, Malay, Japanese, etc.), to be classified for sentiment.

Outputs

  • Sentiment scores: A list of three dictionaries, each containing the following keys:
    • label: The sentiment label ('positive', 'neutral', or 'negative')
    • score: The probability of the corresponding sentiment label

Capabilities

The distilbert-base-multilingual-cased-sentiments-student model can perform zero-shot sentiment classification on multilingual text. For example:

from transformers import pipeline

distilled_student_sentiment_classifier = pipeline(
    model="lxyuan/distilbert-base-multilingual-cased-sentiments-student", 
    return_all_scores=True
)

# English
distilled_student_sentiment_classifier("I love this movie and i would watch it again and again!")
# Output: [[{'label': 'positive', 'score': 0.9731044769287109},
#           {'label': 'neutral', 'score': 0.016910076141357422},
#           {'label': 'negative', 'score': 0.009985478594899178}]]

# Malay
distilled_student_sentiment_classifier("Saya suka filem ini dan saya akan menontonnya lagi dan lagi!")
# Output: [[{'label': 'positive', 'score': 0.9760093688964844},
#           {'label': 'neutral', 'score': 0.01804516464471817},
#           {'label': 'negative', 'score': 0.005945465061813593}]]

# Japanese
distilled_student_sentiment_classifier("")
# Output: [[{'label': 'positive', 'score': 0.9342429041862488},
#           {'label': 'neutral', 'score': 0.040193185210227966},
#           {'label': 'negative', 'score': 0.025563929229974747}]]

What Can I Use It For?

The distilbert-base-multilingual-cased-sentiments-student model can be used in a variety of applications that require multilingual sentiment analysis, such as:

  • Social media monitoring: Analyzing customer sentiment across multiple languages on social media platforms.
  • Product reviews: Aggregating and analyzing product reviews from customers in different countries and languages.
  • Market research: Gauging public opinion on various topics or events in a global context.
  • Customer service: Automatically detecting the sentiment of customer inquiries or feedback in different languages.

By using this distilled and efficient model, you can build sentiment analysis pipelines that are fast, scalable, and capable of handling text in multiple languages.

Things to Try

One interesting aspect of this model is that it was trained using a process of knowledge distillation, where a larger "teacher" model was used to train a smaller "student" model. This allows the student model to achieve high performance while being more efficient and lightweight.

You could try experimenting with the model's performance and compare it to the original teacher model, MoritzLaurer/mDeBERTa-v3-base-mnli-xnli, to see how much the distillation process has impacted the model's accuracy and speed.

Additionally, you could explore using this model as a starting point for further fine-tuning on domain-specific sentiment analysis tasks, potentially leading to even better performance for your particular use case.



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

🔎

distilbert-base-uncased-go-emotions-student

joeddav

Total Score

64

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.

Read more

Updated Invalid Date

👁️

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

MoritzLaurer

Total Score

223

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

🐍

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

🤿

twitter-xlm-roberta-base-sentiment

cardiffnlp

Total Score

169

The twitter-xlm-roberta-base-sentiment model is a multilingual XLM-roBERTa-base model trained on ~198M tweets and fine-tuned for sentiment analysis. The model supports sentiment analysis in 8 languages (Arabic, English, French, German, Hindi, Italian, Spanish, and Portuguese), but can potentially be used for more languages as well. This model was developed by cardiffnlp. Similar models include the xlm-roberta-base-language-detection model, which is a fine-tuned version of the XLM-RoBERTa base model for language identification, and the xlm-roberta-large and xlm-roberta-base models, which are the base and large versions of the multilingual XLM-RoBERTa model. Model inputs and outputs Inputs Text sequences for sentiment analysis Outputs A label indicating the predicted sentiment (Positive, Negative, or Neutral) A score representing the confidence of the prediction Capabilities The twitter-xlm-roberta-base-sentiment model can perform sentiment analysis on text in 8 languages: Arabic, English, French, German, Hindi, Italian, Spanish, and Portuguese. It was trained on a large corpus of tweets, giving it the ability to analyze the sentiment of short, informal text. What can I use it for? This model can be used for a variety of applications that require multilingual sentiment analysis, such as social media monitoring, customer service analysis, and market research. By leveraging the model's ability to analyze sentiment in multiple languages, developers can build applications that can process text from a wide range of sources and users. Things to try One interesting thing to try with this model is to experiment with the different languages it supports. Since the model was trained on a diverse dataset of tweets, it may be able to capture nuances in sentiment that are specific to certain cultures or languages. Developers could try using the model to analyze sentiment in languages beyond the 8 it was specifically fine-tuned on, to see how it performs. Another idea is to compare the performance of this model to other sentiment analysis models, such as the bart-large-mnli or valhalla models, to see how it fares on different types of text and tasks.

Read more

Updated Invalid Date