MiniLM-L12-H384-uncased
Maintainer: microsoft
63
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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 MiniLM-L12-H384-uncased model is a small and fast pre-trained Transformer model developed by Microsoft. It is a distilled version of the UniLM v2 model, with 12 layers, 384 hidden size, and 33M parameters, making it 2.7x faster than the BERT-Base model. Similar models include the Multilingual-MiniLM-L12-H384 and DistilBERT models, which are also smaller and faster versions of larger language models.
Model inputs and outputs
Inputs
- Text: The
MiniLM-L12-H384-uncasedmodel takes raw text as input, which is preprocessed and tokenized. The maximum sequence length is 128 tokens.
Outputs
- Token embeddings: The model outputs token-level embeddings that capture the semantic meaning of the input text. These embeddings can be used as features for downstream natural language understanding tasks.
Capabilities
The MiniLM-L12-H384-uncased model is capable of language understanding and generation tasks. It can be fine-tuned on a variety of natural language processing (NLP) tasks, such as question answering, text classification, and natural language inference. For example, the model achieves competitive results on the SQuAD 2.0 and GLUE benchmark tasks compared to the larger BERT-Base model.
What can I use it for?
The MiniLM-L12-H384-uncased model can be used for a wide range of NLP applications, such as semantic search, text classification, and question answering. Its small size and fast inference make it well-suited for deployment on edge devices or in low-latency applications. You can fine-tune the model on your own dataset to adapt it to your specific use case.
Things to try
One interesting thing to try with the MiniLM-L12-H384-uncased model is to compare its performance to the larger BERT-Base model on your specific task. The model's smaller size and faster inference could make it a more practical choice for your application, while still maintaining competitive performance. You can also experiment with different fine-tuning approaches, such as using different datasets or hyperparameter settings, to further optimize the model's performance.
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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