Multilingual-MiniLM-L12-H384
Maintainer: microsoft
62
🗣️
| 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 Multilingual-MiniLM-L12-H384 is a 12-layer, 384-hidden, 12-head Transformer model from Microsoft that uses the same tokenizer as XLM-RoBERTa but the same Transformer architecture as BERT. It was distilled from a larger model using the techniques described in the "MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers" paper. This model has 21M Transformer parameters and 96M embedding parameters, making it a relatively small and fast multilingual language model.
Model inputs and outputs
The Multilingual-MiniLM-L12-H384 model takes text as input and can be used for various natural language processing tasks such as text classification, question answering, and language generation. The model outputs representations of the input text that can then be used for downstream applications.
Inputs
- Text in one of the 100 languages supported by the model
Outputs
- Contextualized embeddings for the input text
- Logits for various downstream tasks (e.g. classification, generation)
Capabilities
The Multilingual-MiniLM-L12-H384 model has been evaluated on cross-lingual natural language inference (XNLI) and cross-lingual question answering (MLQA) benchmarks. It achieves competitive performance compared to larger models like mBERT and XLM-RoBERTa, demonstrating strong multilingual capabilities despite its small size.
What can I use it for?
The Multilingual-MiniLM-L12-H384 model can be fine-tuned on a variety of downstream tasks, such as text classification, question answering, and language generation. Its small size and fast inference make it a good choice for applications that require efficient multilingual language understanding, such as chatbots, virtual assistants, and content recommendation systems.
Things to try
Given the model's strong multilingual performance, you could try fine-tuning it on tasks that require cross-lingual transfer, such as multilingual sentiment analysis or cross-lingual document retrieval. The model's efficient design also makes it a good candidate for deployment on resource-constrained devices like smartphones.
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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