AI model creator details for nreimers

mMiniLMv2-L12-H384-distilled-from-XLMR-Large

The mMiniLMv2-L12-H384-distilled-from-XLMR-Large model is a masked language model that has been trained to fill in missing words or phrases in a given text. It is a smaller and distilled version of the XLMR-Large model, which means it has undergone a compression process to reduce its size while still preserving its language understanding capabilities. This model can be used for various natural language processing tasks such as text generation, question answering, and language translation.

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47.6K

Huggingface

MiniLMv2-L6-H384-distilled-from-RoBERTa-Large

nreimers

MiniLMv2-L6-H384-distilled-from-RoBERTa-Large is a language model that has been distilled from RoBERTa-Large and is capable of performing fill-mask tasks. It has a smaller architecture compared to RoBERTa-Large, consisting of 6 layers and a hidden size of 384. It has been trained on a large amount of diverse text data, allowing it to generate accurate and contextually relevant predictions for fill-mask tasks.

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22.6K

Huggingface

MiniLMv2-L12-H384-distilled-from-RoBERTa-Large

nreimers

MiniLMv2-L12-H384-distilled-from-RoBERTa-Large is a language model that has been distilled from RoBERTa-Large. It is a variant of the MiniLMv2 model and has been trained on a large corpus of data. This model is designed for the fill-mask task, where it is given a sentence with a blank (mask) and is tasked with predicting the most appropriate word or phrase to fill in the blank. It has been trained to understand and generate coherent and contextually appropriate text.

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7.4K

Huggingface

MiniLM-L6-H384-uncased

nreimers

Platform did not provide a description for this model.

$-/run

4.6K

Huggingface

MiniLMv2-L6-H384-distilled-from-BERT-Base