Delete microsoft

microsoft/git-base/README.md

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----
-language: en
-license: mit
-tags:
-- vision
-- image-to-text
-- image-captioning
-model_name: microsoft/git-base
-pipeline_tag: image-to-text
----
-
-# GIT (GenerativeImage2Text), base-sized
-
-GIT (short for GenerativeImage2Text) model, base-sized version. It was introduced in the paper [GIT: A Generative Image-to-text Transformer for Vision and Language](https://arxiv.org/abs/2205.14100) by Wang et al. and first released in [this repository](https://github.com/microsoft/GenerativeImage2Text).
-
-Disclaimer: The team releasing GIT did not write a model card for this model so this model card has been written by the Hugging Face team.
-
-## Model description
-
-GIT is a Transformer decoder conditioned on both CLIP image tokens and text tokens. The model is trained using "teacher forcing" on a lot of (image, text) pairs.
-
-The goal for the model is simply to predict the next text token, giving the image tokens and previous text tokens.
-
-The model has full access to (i.e. a bidirectional attention mask is used for) the image patch tokens, but only has access to the previous text tokens (i.e. a causal attention mask is used for the text tokens) when predicting the next text token.
-
-![GIT architecture](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/git_architecture.jpg)
-
-This allows the model to be used for tasks like:
-
-- image and video captioning
-- visual question answering (VQA) on images and videos
-- even image classification (by simply conditioning the model on the image and asking it to generate a class for it in text).
-
-## Intended uses & limitations
-
-You can use the raw model for image captioning. See the [model hub](https://huggingface.co/models?search=microsoft/git) to look for
-fine-tuned versions on a task that interests you.
-
-### How to use
-
-For code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/main/model_doc/git#transformers.GitForCausalLM.forward.example).
-
-## Training data
-
-From the paper:
-
-> We collect 0.8B image-text pairs for pre-training, which include COCO (Lin et al., 2014), Conceptual Captions
-(CC3M) (Sharma et al., 2018), SBU (Ordonez et al., 2011), Visual Genome (VG) (Krishna et al., 2016),
-Conceptual Captions (CC12M) (Changpinyo et al., 2021), ALT200M (Hu et al., 2021a), and an extra 0.6B
-data following a similar collection procedure in Hu et al. (2021a).
-
-=> however this is for the model referred to as "GIT" in the paper, which is not open-sourced.
-
-This checkpoint is "GIT-base", which is a smaller variant of GIT trained on 10 million image-text pairs.
-
-See table 11 in the [paper](https://arxiv.org/abs/2205.14100) for more details.
-
-### Preprocessing
-
-We refer to the original repo regarding details for preprocessing during training.
-
-During validation, one resizes the shorter edge of each image, after which center cropping is performed to a fixed-size resolution. Next, frames are normalized across the RGB channels with the ImageNet mean and standard deviation.
-
-## Evaluation results
-
-For evaluation results, we refer readers to the [paper](https://arxiv.org/abs/2205.14100).

microsoft/git-base/config.json

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-{
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-  "architectures": [
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-  "model_type": "git",
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-    "typical_p": 1.0,
-    "use_bfloat16": false
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-  "vocab_size": 30522
-}

microsoft/git-base/generation_config.json

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-{
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microsoft/git-base/gitattributes

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microsoft/git-base/model.safetensors

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microsoft/git-base/preprocessor_config.json

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-{
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microsoft/git-base/special_tokens_map.json

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microsoft/git-base/tokenizer_config.json

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-{
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