Repository: huggingface/transformers
Pull request: Standardize BARTpho model card: badges, new examples, fixed broken im…

Updated bartpho.md

# What does this PR do?

Fixes # (issue)
Standardize BARTpho model card


## Before submitting
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Please review this @stevhliu



Changed files:
filename: docs/source/en/model_doc/bartpho.md
patch: @@ -14,79 +14,98 @@ rendered properly in your Markdown viewer.
 
 -->
 
-# BARTpho
-
-<div class="flex flex-wrap space-x-1">
-<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
-<img alt="TensorFlow" src="https://img.shields.io/badge/TensorFlow-FF6F00?style=flat&logo=tensorflow&logoColor=white">
-<img alt="Flax" src="https://img.shields.io/badge/Flax-29a79b.svg?style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAC0AAAAtCAMAAAANxBKoAAAC7lBMVEUAAADg5vYHPVgAoJH+/v76+v39/f9JbLP///9+AIgAnY3///+mcqzt8fXy9fgkXa3Ax9709fr+///9/f8qXq49qp5AaLGMwrv8/P0eW60VWawxYq8yqJzG2dytt9Wyu9elzci519Lf3O3S2efY3OrY0+Xp7PT///////+dqNCexMc6Z7AGpJeGvbenstPZ5ejQ1OfJzOLa7ejh4+/r8fT29vpccbklWK8PVa0AS6ghW63O498vYa+lsdKz1NDRt9Kw1c672tbD3tnAxt7R6OHp5vDe7OrDyuDn6vLl6/EAQKak0MgATakkppo3ZK/Bz9y8w9yzu9jey97axdvHzeG21NHH4trTwthKZrVGZLSUSpuPQJiGAI+GAI8SWKydycLL4d7f2OTi1+S9xNzL0ePT6OLGzeEAo5U0qJw/aLEAo5JFa7JBabEAp5Y4qZ2QxLyKmsm3kL2xoMOehrRNb7RIbbOZgrGre68AUqwAqZqNN5aKJ5N/lMq+qsd8kMa4pcWzh7muhLMEV69juq2kbKqgUaOTR5uMMZWLLZSGAI5VAIdEAH+ovNDHuNCnxcy3qcaYx8K8msGplrx+wLahjbYdXrV6vbMvYK9DrZ8QrZ8tqJuFms+Sos6sw8ecy8RffsNVeMCvmb43aLltv7Q4Y7EZWK4QWa1gt6meZKUdr6GOAZVeA4xPAISyveLUwtivxtKTpNJ2jcqfvcltiMiwwcfAoMVxhL+Kx7xjdrqTe60tsaNQs6KaRKACrJ6UTZwkqpqTL5pkHY4AloSgsd2ptNXPvNOOncuxxsqFl8lmg8apt8FJcr9EbryGxLqlkrkrY7dRa7ZGZLQ5t6iXUZ6PPpgVpZeJCJFKAIGareTa0+KJod3H0deY2M+esM25usmYu8d2zsJOdcBVvrCLbqcAOaaHaKQAMaScWqKBXqCXMJ2RHpiLF5NmJZAdAHN2kta11dKu1M+DkcZLdb+Mcql3TppyRJdzQ5ZtNZNlIY+DF4+voCOQAAAAZ3RSTlMABAT+MEEJ/RH+/TP+Zlv+pUo6Ifz8+fco/fz6+evr39S9nJmOilQaF/7+/f38+smmoYp6b1T+/v7++vj189zU0tDJxsGzsrKSfv34+Pf27dDOysG9t6+n/vv6+vr59uzr1tG+tZ6Qg9Ym3QAABR5JREFUSMeNlVVUG1EQhpcuxEspXqS0SKEtxQp1d3d332STTRpIQhIISQgJhODu7lAoDoUCpe7u7u7+1puGpqnCPOyZvffbOXPm/PsP9JfQgyCC+tmTABTOcbxDz/heENS7/1F+9nhvkHePG0wNDLbGWwdXL+rbLWvpmZHXD8+gMfBjTh+aSe6Gnn7lwQIOTR0c8wfX3PWgv7avbdKwf/ZoBp1Gp/PvuvXW3vw5ib7emnTW4OR+3D4jB9vjNJ/7gNvfWWeH/TO/JyYrsiKCRjVEZA3UB+96kON+DxOQ/NLE8PE5iUYgIXjFnCOlxEQMaSGVxjg4gxOnEycGz8bptuNjVx08LscIgrzH3umcn+KKtiBIyvzOO2O99aAdR8cF19oZalnCtvREUw79tCd5sow1g1UKM6kXqUx4T8wsi3sTjJ3yzDmmhenLXLpo8u45eG5y4Vvbk6kkC4LLtJMowkSQxmk4ggVJEG+7c6QpHT8vvW9X7/o7+3ELmiJi2mEzZJiz8cT6TBlanBk70cB5GGIGC1gRDdZ00yADLW1FL6gqhtvNXNG5S9gdSrk4M1qu7JAsmYshzDS4peoMrU/gT7qQdqYGZaYhxZmVbGJAm/CS/HloWyhRUlknQ9KYcExTwS80d3VNOxUZJpITYyspl0LbhArhpZCD9cRWEQuhYkNGMHToQ/2Cs6swJlb39CsllxdXX6IUKh/H5jbnSsPKjgmoaFQ1f8wRLR0UnGE/RcDEjj2jXG1WVTwUs8+zxfcrVO+vSsuOpVKxCfYZiQ0/aPKuxQbQ8lIz+DClxC8u+snlcJ7Yr1z1JPqUH0V+GDXbOwAib931Y4Imaq0NTIXPXY+N5L18GJ37SVWu+hwXff8l72Ds9XuwYIBaXPq6Shm4l+Vl/5QiOlV+uTk6YR9PxKsI9xNJny31ygK1e+nIRC1N97EGkFPI+jCpiHe5PCEy7oWqWSwRrpOvhFzcbTWMbm3ZJAOn1rUKpYIt/lDhW/5RHHteeWFN60qo98YJuoq1nK3uW5AabyspC1BcIEpOhft+SZAShYoLSvnmSfnYADUERP5jJn2h5XtsgCRuhYQqAvwTwn33+YWEKUI72HX5AtfSAZDe8F2DtPPm77afhl0EkthzuCQU0BWApgQIH9+KB0JhopMM7bJrdTRoleM2JAVNMyPF+wdoaz+XJpGoVAQ7WXUkcV7gT3oUZyi/ISIJAVKhgNp+4b4veCFhYVJw4locdSjZCp9cPUhLF9EZ3KKzURepMEtCDPP3VcWFx4UIiZIklIpFNfHpdEafIF2aRmOcrUmjohbT2WUllbmRvgfbythbQO3222fpDJoufaQPncYYuqoGtUEsCJZL6/3PR5b4syeSjZMQG/T2maGANlXT2v8S4AULWaUkCxfLyW8iW4kdka+nEMjxpL2NCwsYNBp+Q61PF43zyDg9Bm9+3NNySn78jMZUUkumqE4Gp7JmFOdP1vc8PpRrzj9+wPinCy8K1PiJ4aYbnTYpCCbDkBSbzhu2QJ1Gd82t8jI8TH51+OzvXoWbnXUOBkNW+0mWFwGcGOUVpU81/n3TOHb5oMt2FgYGjzau0Nif0Ss7Q3XB33hjjQHjHA5E5aOyIQc8CBrLdQSs3j92VG+3nNEjbkbdbBr9zm04ruvw37vh0QKOdeGIkckc80fX3KH/h7PT4BOjgCty8VZ5ux1MoO5Cf5naca2LAsEgehI+drX8o/0Nu+W0m6K/I9gGPd/dfx/EN/wN62AhsBWuAAAAAElFTkSuQmCC
-">
+<div style="float: right;">
+   <div class="flex flex-wrap space-x-1">
+      <img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
+      <img alt="TensorFlow" src="https://img.shields.io/badge/TensorFlow-FF6F00?style=flat&logo=tensorflow&logoColor=white">
+      <img alt="Flax" src="https://img.shields.io/badge/Flax-29a79b.svg?style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAC0AAAAtCAMAAAANxBKoAAAC7lBMVEUAAADg5vYHPVgAoJH+/v76+v39/f9JbLP///9+AIgAnY3///+mcqzt8fXy9fgkXa3Ax9709fr+///9/f8qXq49qp5AaLGMwrv8/P0eW60VWawxYq8yqJzG2dytt9Wyu9elzci519Lf3O3S2efY3OrY0+Xp7PT///////+dqNCexMc6Z7AGpJeGvbenstPZ5ejQ1OfJzOLa7ejh4+/r8fT29vpccbklWK8PVa0AS6ghW63O498vYa+lsdKz1NDRt9Kw1c672tbD3tnAxt7R6OHp5vDe7OrDyuDn6vLl6/EAQKak0MgATakkppo3ZK/Bz9y8w9yzu9jey97axdvHzeG21NHH4trTwthKZrVGZLSUSpuPQJiGAI+GAI8SWKydycLL4d7f2OTi1+S9xNzL0ePT6OLGzeEAo5U0qJw/aLEAo5JFa7JBabEAp5Y4qZ2QxLyKmsm3kL2xoMOehrRNb7RIbbOZgrGre68AUqwAqZqNN5aKJ5N/lMq+qsd8kMa4pcWzh7muhLMEV69juq2kbKqgUaOTR5uMMZWLLZSGAI5VAIdEAH+ovNDHuNCnxcy3qcaYx8K8msGplrx+wLahjbYdXrV6vbMvYK9DrZ8QrZ8tqJuFms+Sos6sw8ecy8RffsNVeMCvmb43aLltv7Q4Y7EZWK4QWa1gt6meZKUdr6GOAZVeA4xPAISyveLUwtivxtKTpNJ2jcqfvcltiMiwwcfAoMVxhL+Kx7xjdrqTe60tsaNQs6KaRKACrJ6UTZwkqpqTL5pkHY4AloSgsd2ptNXPvNOOncuxxsqFl8lmg8apt8FJcr9EbryGxLqlkrkrY7dRa7ZGZLQ5t6iXUZ6PPpgVpZeJCJFKAIGareTa0+KJod3H0deY2M+esM25usmYu8d2zsJOdcBVvrCLbqcAOaaHaKQAMaScWqKBXqCXMJ2RHpiLF5NmJZAdAHN2kta11dKu1M+DkcZLdb+Mcql3TppyRJdzQ5ZtNZNlIY+DF4+voCOQAAAAZ3RSTlMABAT+MEEJ/RH+/TP+Zlv+pUo6Ifz8+fco/fz6+evr39S9nJmOilQaF/7+/f38+smmoYp6b1T+/v7++vj189zU0tDJxsGzsrKSfv34+Pf27dDOysG9t6+n/vv6+vr59uzr1tG+tZ6Qg9Ym3QAABR5JREFUSMeNlVVUG1EQhpcuxEspXqS0SKEtxQp1d3d332STTRpIQhIISQgJhODu7lAoDoUCpe7u7u7+1puGpqnCPOyZvffbOXPm/PsP9JfQgyCC+tmTABTOcbxDz/heENS7/1F+9nhvkHePG0wNDLbGWwdXL+rbLWvpmZHXD8+gMfBjTh+aSe6Gnn7lwQIOTR0c8wfX3PWgv7avbdKwf/ZoBp1Gp/PvuvXW3vw5ib7emnTW4OR+3D4jB9vjNJ/7gNvfWWeH/TO/JyYrsiKCRjVEZA3UB+96kON+DxOQ/NLE8PE5iUYgIXjFnCOlxEQMaSGVxjg4gxOnEycGz8bptuNjVx08LscIgrzH3umcn+KKtiBIyvzOO2O99aAdR8cF19oZalnCtvREUw79tCd5sow1g1UKM6kXqUx4T8wsi3sTjJ3yzDmmhenLXLpo8u45eG5y4Vvbk6kkC4LLtJMowkSQxmk4ggVJEG+7c6QpHT8vvW9X7/o7+3ELmiJi2mEzZJiz8cT6TBlanBk70cB5GGIGC1gRDdZ00yADLW1FL6gqhtvNXNG5S9gdSrk4M1qu7JAsmYshzDS4peoMrU/gT7qQdqYGZaYhxZmVbGJAm/CS/HloWyhRUlknQ9KYcExTwS80d3VNOxUZJpITYyspl0LbhArhpZCD9cRWEQuhYkNGMHToQ/2Cs6swJlb39CsllxdXX6IUKh/H5jbnSsPKjgmoaFQ1f8wRLR0UnGE/RcDEjj2jXG1WVTwUs8+zxfcrVO+vSsuOpVKxCfYZiQ0/aPKuxQbQ8lIz+DClxC8u+snlcJ7Yr1z1JPqUH0V+GDXbOwAib931Y4Imaq0NTIXPXY+N5L18GJ37SVWu+hwXff8l72Ds9XuwYIBaXPq6Shm4l+Vl/5QiOlV+uTk6YR9PxKsI9xNJny31ygK1e+nIRC1N97EGkFPI+jCpiHe5PCEy7oWqWSwRrpOvhFzcbTWMbm3ZJAOn1rUKpYIt/lDhW/5RHHteeWFN60qo98YJuoq1nK3uW5AabyspC1BcIEpOhft+SZAShYoLSvnmSfnYADUERP5jJn2h5XtsgCRuhYQqAvwTwn33+YWEKUI72HX5AtfSAZDe8F2DtPPm77afhl0EkthzuCQU0BWApgQIH9+KB0JhopMM7bJrdTRoleM2JAVNMyPF+wdoaz+XJpGoVAQ7WXUkcV7gT3oUZyi/ISIJAVKhgNp+4b4veCFhYVJw4locdSjZCp9cPUhLF9EZ3KKzURepMEtCDPP3VcWFx4UIiZIklIpFNfHpdEafIF2aRmOcrUmjohbT2WUllbmRvgfbythbQO3222fpDJoufaQPncYYuqoGtUEsCJZL6/3PR5b4syeSjZMQG/T2maGANlXT2v8S4AULWaUkCxfLyW8iW4kdka+nEMjxpL2NCwsYNBp+Q61PF43zyDg9Bm9+3NNySn78jMZUUkumqE4Gp7JmFOdP1vc8PpRrzj9+wPinCy8K1PiJ4aYbnTYpCCbDkBSbzhu2QJ1Gd82t8jI8TH51+OzvXoWbnXUOBkNW+0mWFwGcGOUVpU81/n3TOHb5oMt2FgYGjzau0Nif0Ss7Q3XB33hjjQHjHA5E5aOyIQc8CBrLdQSs3j92VG+3nNEjbkbdbBr9zm04ruvw37vh0QKOdeGIkckc80fX3KH/h7PT4BOjgCty8VZ5ux1MoO5Cf5naca2LAsEgehI+drX8o/0Nu+W0m6K/I9gGPd/dfx/EN/wN62AhsBWuAAAAAElFTkSuQmCC
+      ">
+   </div>
 </div>
 
-## Overview
+# BARTpho
 
-The BARTpho model was proposed in [BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese](https://huggingface.co/papers/2109.09701) by Nguyen Luong Tran, Duong Minh Le and Dat Quoc Nguyen.
+[BARTpho](https://huggingface.co/papers/2109.09701) is a large-scale Vietnamese sequence-to-sequence model. It offers a word-based and syllable-based version. This model is built on the [BART](./bart) large architecture with its denoising pretraining.
 
-The abstract from the paper is the following:
+You can find all the original checkpoints under the [VinAI](https://huggingface.co/vinai/models?search=bartpho) organization.
 
-*We present BARTpho with two versions -- BARTpho_word and BARTpho_syllable -- the first public large-scale monolingual
-sequence-to-sequence models pre-trained for Vietnamese. Our BARTpho uses the "large" architecture and pre-training
-scheme of the sequence-to-sequence denoising model BART, thus especially suitable for generative NLP tasks. Experiments
-on a downstream task of Vietnamese text summarization show that in both automatic and human evaluations, our BARTpho
-outperforms the strong baseline mBART and improves the state-of-the-art. We release BARTpho to facilitate future
-research and applications of generative Vietnamese NLP tasks.*
+> [!TIP]
+> This model was contributed by [dqnguyen](https://huggingface.co/dqnguyen).
+> Check out the right sidebar for examples of how to apply BARTpho to different language tasks.
 
-This model was contributed by [dqnguyen](https://huggingface.co/dqnguyen). The original code can be found [here](https://github.com/VinAIResearch/BARTpho).
+The example below demonstrates how to summarize text with [`Pipeline`] or the [`AutoModel`] class.
 
-## Usage example
 
-```python
->>> import torch
->>> from transformers import AutoModel, AutoTokenizer
+<hfoptions id="usage">
+<hfoption id="Pipeline">
 
->>> bartpho = AutoModel.from_pretrained("vinai/bartpho-syllable")
 
->>> tokenizer = AutoTokenizer.from_pretrained("vinai/bartpho-syllable")
 
->>> line = "Chúng tôi là những nghiên cứu viên."
+```python
+import torch
+from transformers import pipeline
+
+pipeline = pipeline(
+   task="summarization",
+   model="vinai/bartpho-word",
+   torch_dtype=torch.float16,
+   device=0
+)
+
+text = """
+Quang tổng hợp hay gọi tắt là quang hợp là quá trình thu nhận và chuyển hóa năng lượng ánh sáng Mặt trời của thực vật, 
+tảo và một số vi khuẩn để tạo ra hợp chất hữu cơ phục vụ bản thân cũng như làm nguồn thức ăn cho hầu hết các sinh vật 
+trên Trái Đất. Quang hợp trong thực vật thường liên quan đến chất tố diệp lục màu xanh lá cây và tạo ra oxy như một sản phẩm phụ
+"""
+pipeline(text)
+```
 
->>> input_ids = tokenizer(line, return_tensors="pt")
+</hfoption>
+<hfoption id="AutoModel">
 
->>> with torch.no_grad():
-...     features = bartpho(**input_ids)  # Models outputs are now tuples
+```python
+import torch
+from transformers import BartForConditionalGeneration, AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained(
+    "vinai/bartpho-word",
+)
+model = BartForConditionalGeneration.from_pretrained(
+    "vinai/bartpho-word",
+    torch_dtype=torch.float16,
+    device_map="auto",
+)
+
+text = """
+Quang tổng hợp hay gọi tắt là quang hợp là quá trình thu nhận và chuyển hóa năng lượng ánh sáng Mặt trời của thực vật, 
+tảo và một số vi khuẩn để tạo ra hợp chất hữu cơ phục vụ bản thân cũng như làm nguồn thức ăn cho hầu hết các sinh vật 
+trên Trái Đất. Quang hợp trong thực vật thường liên quan đến chất tố diệp lục màu xanh lá cây và tạo ra oxy như một sản phẩm phụ
+"""
+inputs = tokenizer(text, return_tensors="pt").to("cuda")
+
+outputs = model.generate(inputs["input_ids"], num_beams=2, min_length=0, max_length=20)
+tokenizer.batch_decode(outputs, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+```
 
->>> # With TensorFlow 2.0+:
->>> from transformers import TFAutoModel
+</hfoption>
+<hfoption id="transformers CLI">
 
->>> bartpho = TFAutoModel.from_pretrained("vinai/bartpho-syllable")
->>> input_ids = tokenizer(line, return_tensors="tf")
->>> features = bartpho(**input_ids)
+```bash
+echo -e "Quang tổng hợp hay gọi tắt là quang hợp là quá trình thu nhận và chuyển hóa năng lượng ánh sáng Mặt trời của thực vật, 
+tảo và một số vi khuẩn để tạo ra hợp chất hữu cơ phục vụ bản thân cũng như làm nguồn thức ăn cho hầu hết các sinh vật 
+trên Trái Đất. Quang hợp trong thực vật thường liên quan đến chất tố diệp lục màu xanh lá cây và tạo ra oxy như một sản phẩm phụ" | \
+transformers run --task summarization --model vinai/bartpho-word --device 0
 ```
 
-## Usage tips
+</hfoption>
+</hfoptions>
 
-- Following mBART, BARTpho uses the "large" architecture of BART with an additional layer-normalization layer on top of
-  both the encoder and decoder. Thus, usage examples in the [documentation of BART](bart), when adapting to use
-  with BARTpho, should be adjusted by replacing the BART-specialized classes with the mBART-specialized counterparts.
-  For example:
 
-```python
->>> from transformers import MBartForConditionalGeneration
-
->>> bartpho = MBartForConditionalGeneration.from_pretrained("vinai/bartpho-syllable")
->>> TXT = "Chúng tôi là <mask> nghiên cứu viên."
->>> input_ids = tokenizer([TXT], return_tensors="pt")["input_ids"]
->>> logits = bartpho(input_ids).logits
->>> masked_index = (input_ids[0] == tokenizer.mask_token_id).nonzero().item()
->>> probs = logits[0, masked_index].softmax(dim=0)
->>> values, predictions = probs.topk(5)
->>> print(tokenizer.decode(predictions).split())
-```
 
-- This implementation is only for tokenization: "monolingual_vocab_file" consists of Vietnamese-specialized types
-  extracted from the pre-trained SentencePiece model "vocab_file" that is available from the multilingual XLM-RoBERTa.
-  Other languages, if employing this pre-trained multilingual SentencePiece model "vocab_file" for subword
-  segmentation, can reuse BartphoTokenizer with their own language-specialized "monolingual_vocab_file".
+## Notes
+
+- BARTpho uses the large architecture of BART with an additional layer-normalization layer on top of the encoder and decoder. The BART-specific classes should be replaced with the mBART-specific classes.
+- This implementation only handles tokenization through the `monolingual_vocab_file` file. This is a Vietnamese-specific subset of token types taken from that multilingual vocabulary. If you want to use this tokenizer for another language, replace the `monolingual_vocab_file` with one specialized for your target language.
 
 ## BartphoTokenizer