distilbert-base-multilingual-cased
Maintainer: distilbert
119
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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
The distilbert-base-multilingual-cased is a distilled version of the BERT base multilingual model. It was developed by the Hugging Face team and is a smaller, faster, and lighter version of the original BERT multilingual model. Compared to the BERT base multilingual model, this model has 6 layers, 768 dimensions, and 12 heads, totaling 134M parameters (versus 177M for the original BERT multilingual model). On average, this DistilBERT model is twice as fast as the original BERT multilingual model.
Similar models include the distilbert-base-uncased model, which is a distilled version of the BERT base uncased model, and the bert-base-cased and bert-base-uncased BERT base models.
Model inputs and outputs
Inputs
- Text: The model takes in text as input, which can be in one of 104 different languages supported by the model.
Outputs
- Token-level predictions: The model can output token-level predictions, such as for masked language modeling tasks.
- Sequence-level predictions: The model can also output sequence-level predictions, such as for next sentence prediction tasks.
Capabilities
The distilbert-base-multilingual-cased model is capable of performing a variety of natural language processing tasks, including text classification, named entity recognition, and question answering. The model has been shown to perform well on multilingual tasks, making it useful for applications that need to handle text in multiple languages.
What can I use it for?
The distilbert-base-multilingual-cased model can be used for a variety of downstream tasks, such as:
- Text classification: The model can be fine-tuned on a labeled dataset to perform tasks like sentiment analysis, topic classification, or intent detection.
- Named entity recognition: The model can be used to identify and extract named entities (e.g., people, organizations, locations) from text.
- Question answering: The model can be fine-tuned on a question answering dataset to answer questions based on a given context.
Additionally, the smaller size and faster inference speed of the distilbert-base-multilingual-cased model make it a good choice for applications with resource-constrained environments, such as mobile or edge devices.
Things to try
One interesting thing to try with the distilbert-base-multilingual-cased model is to explore its multilingual capabilities. Since the model was trained on 104 different languages, you can experiment with inputting text in various languages and see how the model performs. You can also try fine-tuning the model on a multilingual dataset to see if it can improve performance on cross-lingual tasks.
Another interesting experiment would be to compare the performance of the distilbert-base-multilingual-cased model to the original BERT base multilingual model, both in terms of accuracy and inference speed. This could help you determine the tradeoffs between model size, speed, and performance for your specific 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!
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