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This model is a fine-tuned version of bert-base-uncased on the **GLUE MRPC dataset**. The GLUE MRPC dataset, from The Microsoft Research Paraphrase Corpus (Dolan & Brockett, 2005) is a corpus of sentence pairs automatically extracted from online news sources, with human annotations for whether the sentences in the pair are semantically equivalent. It is a pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in this paper BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. This model, bert-base-uncased-mrpc, is uncased: it does not make a difference between **"english"** and **"English"**. Masked language modeling predicts a masked token in a sequence, and the model can attend to tokens bidirectionally. This means the model has full access to the tokens on the left and right. Masked language modeling is great for tasks that require a good contextual understanding of an entire sequence. BERT is an example of a masked language model. For this model, you don’t need labels (also known as an unsupervised task) because the next word (MLM) is the label BERT base model (uncased) It provides: - Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally masks the future tokens. It allows the model to learn a bidirectional representation of the sentence. - Next sentence prediction (NSP): the models concatenates two masked sentences as inputs during pretraining. Sometimes they correspond to sentences that were next to each other in the original text, sometimes not. The model then has to predict if the two sentences were following each other or not.

Text analysis
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Dense Prediction Transformer (DPT) model trained on 1.4 million images for monocular depth estimation. It was introduced in the paper Vision Transformers for Dense Prediction by Ranftl et al. (2021) and first released in this repository. DPT uses the Vision Transformer (ViT) as backbone and adds a neck + head on top for monocular depth estimation. This repository hosts the "hybrid" version of the model as stated in the paper. DPT-Hybrid diverges from DPT by using ViT-hybrid as a backbone and taking some activations from the backbone. The model card has been written in combination by the Hugging Face team and Intel.

Depth estimation
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Dense Prediction Transformer (DPT) model trained on 1.4 million images for monocular depth estimation. It was introduced in the paper Vision Transformers for Dense Prediction by Ranftl et al. (2021) and first released in this repository. DPT uses the Vision Transformer (ViT) as backbone and adds a neck + head on top for monocular depth estimation. The model card has been written in combination by the Hugging Face team and Intel. In case you want to implement the entire logic yourself, here's how to do that for zero-shot depth estimation on an image:

Depth estimation
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基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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This model is an int4 model with group_size 128 and symmetric quantization of google/gemma-4-31B-it generated by intel/auto-round. Please follow the license of the original model. A youtube video by Fahd Mirza demonstrates how to use this model. MODEL_ID = "Intel/gemma-4-31B-it-int4-AutoRound" processor = AutoProcessor.from_pretrained(MODEL_ID) model = AutoModelForCausalLM.from_pretrained( MODEL_ID, dtype="auto", device_map="auto" )

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