Update README.md

Huggingface -> Hugging Face

README.md

@@ -23,7 +23,7 @@ This source code is licensed under the Apache 2.0 license found in the LICENSE f
 
 [colab]: <https://colab.research.google.com/assets/colab-badge.svg>
 
-OFA is a unified sequence-to-sequence pretrained model (support **English** and **Chinese**) that unifies modalities (i.e., cross-modality, vision, language) and tasks (**finetuning** and **prompt tuning** are supported): image captioning (1st at the [MSCOCO Leaderboard](https://competitions.codalab.org/competitions/3221#results)), VQA ([link](https://eval.ai/web/challenges/challenge-page/830/leaderboard/2278)), visual grounding, text-to-image generation, text classification, text generation, image classification, etc. We provide **step-by-step** instructions for pretraining and finetuning and corresponding checkpoints (check official ckpt \[[EN](checkpoints.md)|[CN](checkpoints_cn.md)\] or [huggingface ckpt](https://huggingface.co/OFA-Sys)).
+OFA is a unified sequence-to-sequence pretrained model (support **English** and **Chinese**) that unifies modalities (i.e., cross-modality, vision, language) and tasks (**finetuning** and **prompt tuning** are supported): image captioning (1st at the [MSCOCO Leaderboard](https://competitions.codalab.org/competitions/3221#results)), VQA ([link](https://eval.ai/web/challenges/challenge-page/830/leaderboard/2278)), visual grounding, text-to-image generation, text classification, text generation, image classification, etc. We provide **step-by-step** instructions for pretraining and finetuning and corresponding checkpoints (check official ckpt \[[EN](checkpoints.md)|[CN](checkpoints_cn.md)\] or [Hugging Face ckpt](https://huggingface.co/OFA-Sys)).
 
 We sincerely welcome contributions to our project. Feel free to contact us or send us issues / PRs!
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@@ -42,8 +42,8 @@ We provide online demo via Hugging Face Spaces for you to interact with our pret
 Also we provide Colab notebooks for you to better perceive the procedures. Click [here](colab.md) to check them out!
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-# Use in Huggingface Transformers
-We support the inference of OFA in Huggingface Transformers. Check the [README](transformers.md) and [Colab Notebook](https://colab.research.google.com/drive/1Ho81RBV8jysZ7e0FhsSCk_v938QeDuy3?usp=sharing) for more information. Codes are released in this branch https://github.com/OFA-Sys/OFA/tree/feature/add_transformers
+# Use in Hugging Face Transformers
+We support the inference of OFA in Hugging Face Transformers. Check the [README](transformers.md) and [Colab Notebook](https://colab.research.google.com/drive/1Ho81RBV8jysZ7e0FhsSCk_v938QeDuy3?usp=sharing) for more information. Codes are released in this branch https://github.com/OFA-Sys/OFA/tree/feature/add_transformers
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@@ -52,10 +52,10 @@ We support the inference of OFA in Huggingface Transformers. Check the [README](
 * 2022.12.7: Released the MMSpeech an ASR pre-training method based on OFA. Check our paper [here](https://arxiv.org/abs/2212.00500)! Please see the [README_mmspeech.md](README_mmspeech.md) for further details.
 * 2022.8.16: Released the **Chinese** version of OFA. **OFA-CN** needs only switching to `bpe_dir=../../utils/BERT_CN_dict` and `bpe=bert` and using our provided Chinese checkpoints in [checkpoints_cn.md](checkpoints_cn.md). Temporarily, we only provide base-size and large-size pretrained checkpoints and finetuned checkpoints on [MUGE Caption](https://tianchi.aliyun.com/muge) and the Chinese version of RefCOCO(-/+/g) (to release soon). 
 * 2022.8.5: Released support of **prompt tuning** for OFA. Check our paper [here](https://arxiv.org/abs/2208.02532)! Please see the [prompt_tuning.md](prompt_tuning.md) for further details.
-* 2022.7.7: Updated support of OFA on **huggingface transformers** (fixed bugs in forward, add sequence generator from Fairseq to ensure performance, etc.). Refer to the doc [transformers.md](transformers.md) and the branch `feature/add_transformers`. 
+* 2022.7.7: Updated support of OFA on **Hugging Face transformers** (fixed bugs in forward, add sequence generator from Fairseq to ensure performance, etc.). Refer to the doc [transformers.md](transformers.md) and the branch `feature/add_transformers`. 
 * 2022.6.17: Released the pretrained checkpoint of **OFA-Huge**. To use it, set `--arch=ofa_huge` in the script.
 * 2022.5.15: OFA was accepted by **ICML 2022**
-* 2022.4.28: Add support of inference on **huggingface transformers**. For how to use it, please refer to the doc [transformers.md](transformers.md) and our [huggingface models](https://huggingface.co/OFA-Sys). 
+* 2022.4.28: Add support of inference on **Hugging Face transformers**. For how to use it, please refer to the doc [transformers.md](transformers.md) and our [Hugging Face models](https://huggingface.co/OFA-Sys). 
 * 2022.4.16: Released lightweight pretrained models **OFA-Medium** (~93M params) and **OFA-Tiny** (~33M params) in [checkpoints.md](checkpoints.md). To use them, you just need to load the corresponding checkpoint and set `--arch=ofa_medium` or `--arch=ofa_tiny` in the scripts.
 
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