mdeberta-v3-base
Maintainer: microsoft
128
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
mdeberta-v3-base is a multilingual version of the DeBERTa language model developed by Microsoft. DeBERTa improves upon the BERT and RoBERTa models by using disentangled attention and an enhanced mask decoder, allowing it to outperform RoBERTa on a majority of natural language understanding (NLU) tasks with 80GB of training data.
The multilingual mDeBERTa-v3-base model was trained on the CC100 multilingual dataset and has 12 layers with a hidden size of 768, resulting in 86M backbone parameters and a vocabulary of 250K tokens. Compared to the original DeBERTa model, the V3 version significantly improves performance on downstream tasks by using ELECTRA-style pre-training with gradient-disentangled embedding sharing.
Model inputs and outputs
Inputs
- Natural language text in a variety of languages, including over 100 supported by the multilingual model.
- Input sequences can be up to 8192 tokens long.
Outputs
- Contextual token embeddings that can be used for a variety of natural language processing tasks.
- Zero-shot cross-lingual classification outputs on the XNLI dataset.
Capabilities
mDeBERTa-v3-base excels at multilingual natural language understanding, demonstrating strong zero-shot cross-lingual transfer capabilities on the XNLI dataset. Compared to the XLM-RoBERTa base model, mDeBERTa-v3-base achieves a significantly higher average accuracy of 79.8% across 15 languages, outperforming XLM-RoBERTa by over 3 percentage points.
What can I use it for?
The multilingual capabilities of mDeBERTa-v3-base make it well-suited for a variety of NLP tasks that require understanding text in multiple languages, such as:
-
Zero-shot cross-lingual classification: By leveraging the strong transfer learning performance of
mDeBERTa-v3-base, you can build multilingual classification models without needing to annotate data in each target language. -
Multilingual question answering and information retrieval: The model's ability to encode text in over 100 languages allows it to power cross-lingual search and question answering applications.
-
Multilingual text generation and data augmentation: The broad language coverage of
mDeBERTa-v3-basemakes it useful for generating synthetic text in multiple languages to augment training data.
Things to try
One interesting aspect of mDeBERTa-v3-base is its ability to process input sequences up to 8192 tokens long. This makes it well-suited for tasks involving long-form text, such as document retrieval and summarization. You could experiment with using the model's multi-granularity capabilities to improve the performance of your long document understanding applications.
Additionally, the model's support for hybrid retrieval techniques, combining both dense and sparse representations, presents opportunities to leverage its strengths in both embedding-based and lexical matching approaches. Exploring ways to effectively combine these complementary retrieval signals could lead to performance gains in your information retrieval workflows.
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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deberta-base is a text-to-text AI model developed by Microsoft that improves upon the BERT and RoBERTa models using disentangled attention and an enhanced mask decoder. According to the maintainer's description, deberta-base outperforms BERT and RoBERTa on the majority of natural language understanding (NLU) tasks with 80GB of training data. Similar models like mdeberta-v3-base, deberta-v3-base, and deberta-v3-large further improve upon DeBERTa using ELECTRA-style pre-training and gradient-disentangled embedding sharing. These models demonstrate significantly better performance on downstream NLU tasks compared to the original DeBERTa. Model inputs and outputs Inputs Text data in natural language, such as sentences or paragraphs Outputs Predictions or representations for various natural language processing tasks, such as: Text classification Question answering Sentiment analysis Named entity recognition Capabilities The deberta-base model can be fine-tuned on a variety of natural language understanding tasks and has shown strong performance, outperforming BERT and RoBERTa models on tasks like SQuAD 1.1, SQuAD 2.0, and MNLI. The improved disentangled attention and mask decoder allow it to better capture contextual relationships in text. What can I use it for? You can use deberta-base for a wide range of natural language processing applications, such as: Question answering**: Fine-tune the model on a question-answering dataset like SQuAD to build a system that can answer questions based on given context. Text classification**: Use the model's representations as features for training a classifier on tasks like sentiment analysis, topic classification, or intent detection. Named entity recognition**: Fine-tune the model to identify and extract named entities (e.g., people, organizations, locations) from text. Things to try One interesting aspect of the deberta-base model is its enhanced mask decoder, which allows it to better capture contextual relationships in text compared to previous BERT-based models. You could experiment with using the model's representations for tasks that require deep language understanding, such as commonsense reasoning or reading comprehension. Additionally, you could try fine-tuning the model on specialized domains or languages to see if the disentangled attention mechanism provides benefits for those use cases.
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