Fixed Majority of the Typos in transformers[en] Documentation (#33350)

* Fixed typo: insted to instead

* Fixed typo: relase to release

* Fixed typo: nighlty to nightly

* Fixed typos: versatible, benchamarks, becnhmark to versatile, benchmark, benchmarks

* Fixed typo in comment: quantizd to quantized

* Fixed typo: architecutre to architecture

* Fixed typo: contibution to contribution

* Fixed typo: Presequities to Prerequisites

* Fixed typo: faste to faster

* Fixed typo: extendeding to extending

* Fixed typo: segmetantion_maps to segmentation_maps

* Fixed typo: Alternativelly to Alternatively

* Fixed incorrectly defined variable: output to output_disabled

* Fixed typo in library name: tranformers.onnx to transformers.onnx

* Fixed missing import: import tensorflow as tf

* Fixed incorrectly defined variable: token_tensor to tokens_tensor

* Fixed missing import: import torch

* Fixed incorrectly defined variable and typo: uromaize to uromanize

* Fixed incorrectly defined variable and typo: uromaize to uromanize

* Fixed typo in function args: numpy.ndarry to numpy.ndarray

* Fixed Inconsistent Library Name: Torchscript to TorchScript

* Fixed Inconsistent Class Name: OneformerProcessor to OneFormerProcessor

* Fixed Inconsistent Class Named Typo: TFLNetForMultipleChoice to TFXLNetForMultipleChoice

* Fixed Inconsistent Library Name Typo: Pytorch to PyTorch

* Fixed Inconsistent Function Name Typo: captureWarning to captureWarnings

* Fixed Inconsistent Library Name Typo: Pytorch to PyTorch

* Fixed Inconsistent Class Name Typo: TrainingArgument to TrainingArguments

* Fixed Inconsistent Model Name Typo: Swin2R to Swin2SR

* Fixed Inconsistent Model Name Typo: EART to BERT

* Fixed Inconsistent Library Name Typo: TensorFLow to TensorFlow

* Fixed Broken Link for Speech Emotion Classification with Wav2Vec2

* Fixed minor missing word Typo

* Fixed minor missing word Typo

* Fixed minor missing word Typo

* Fixed minor missing word Typo

* Fixed minor missing word Typo

* Fixed minor missing word Typo

* Fixed minor missing word Typo

* Fixed minor missing word Typo

* Fixed Punctuation: Two commas

* Fixed Punctuation: No Space between XLM-R and is

* Fixed Punctuation: No Space between [~accelerate.Accelerator.backward] and method

* Added backticks to display model.fit() in codeblock

* Added backticks to display openai-community/gpt2 in codeblock

* Fixed Minor Typo: will to with

* Fixed Minor Typo: is to are

* Fixed Minor Typo: in to on

* Fixed Minor Typo: inhibits to exhibits

* Fixed Minor Typo: they need to it needs

* Fixed Minor Typo: cast the load the checkpoints To load the checkpoints

* Fixed Inconsistent Class Name Typo: TFCamembertForCasualLM to TFCamembertForCausalLM

* Fixed typo in attribute name: outputs.last_hidden_states to outputs.last_hidden_state

* Added missing verbosity level: fatal

* Fixed Minor Typo: take To takes

* Fixed Minor Typo: heuristic To heuristics

* Fixed Minor Typo: setting To settings

* Fixed Minor Typo: Content To Contents

* Fixed Minor Typo: millions To million

* Fixed Minor Typo: difference To differences

* Fixed Minor Typo: while extract To which extracts

* Fixed Minor Typo: Hereby To Here

* Fixed Minor Typo: addition To additional

* Fixed Minor Typo: supports To supported

* Fixed Minor Typo: so that benchmark results TO as a consequence, benchmark

* Fixed Minor Typo: a To an

* Fixed Minor Typo: a To an

* Fixed Minor Typo: Chain-of-though To Chain-of-thought

docs/source/en/model_doc/camembert.md

@@ -106,7 +106,7 @@ as the information relative to the inputs and outputs.
 
 [[autodoc]] TFCamembertModel
 
-## TFCamembertForCasualLM
+## TFCamembertForCausalLM
 
 [[autodoc]] TFCamembertForCausalLM
 

docs/source/en/model_doc/clipseg.md

@@ -19,7 +19,7 @@ rendered properly in your Markdown viewer.
 ## Overview
 
 The CLIPSeg model was proposed in [Image Segmentation Using Text and Image Prompts](https://arxiv.org/abs/2112.10003) by Timo Lüddecke
-and Alexander Ecker. CLIPSeg adds a minimal decoder on top of a frozen [CLIP](clip) model for zero- and one-shot image segmentation.
+and Alexander Ecker. CLIPSeg adds a minimal decoder on top of a frozen [CLIP](clip) model for zero-shot and one-shot image segmentation.
 
 The abstract from the paper is the following:
 

docs/source/en/model_doc/code_llama.md

@@ -34,7 +34,7 @@ This model was contributed by [ArthurZucker](https://huggingface.co/ArthurZ). Th
 
 The `Llama2` family models, on which Code Llama is based, were trained using `bfloat16`, but the original inference uses `float16`. Let's look at the different precisions:
 
-* `float32`: PyTorch convention on model initialization is to load models in `float32`, no matter with which `dtype` the model weights were stored. `transformers` also follows this convention for consistency with PyTorch. This will be picked by default. If you want the `AutoModel` API to cast the load the checkpoints with the storage weights type, you must specify `torch_dtype="auto"`, e.g. `model = AutoModelForCausalLM.from_pretrained("path", torch_dtype = "auto")`.
+* `float32`: PyTorch convention on model initialization is to load models in `float32`, no matter with which `dtype` the model weights were stored. `transformers` also follows this convention for consistency with PyTorch. This will be picked by default. If you want the `AutoModel` API to load the checkpoints with the storage weights type, you must specify `torch_dtype="auto"`, e.g. `model = AutoModelForCausalLM.from_pretrained("path", torch_dtype = "auto")`.
 * `bfloat16`: Code Llama was trained with this precision, so we recommend using it for further training or fine-tuning.
 * `float16`: We recommend running inference using this precision, as it's usually faster than `bfloat16`, and evaluation metrics show no discernible degradation with respect to `bfloat16`. You can also run inference using `bfloat16`, and we recommend you check inference results with both `float16` and `bfloat16` after fine-tuning.
 

docs/source/en/model_doc/falcon_mamba.md

@@ -27,7 +27,7 @@ Due to its architecture, FalconMamba is significantly faster at inference and re
 
 Tips:
 
-- FalconMamba is mostly based on Mamba architecutre, the same [tips and best practices](./mamba) would be relevant here.
+- FalconMamba is mostly based on Mamba architecture, the same [tips and best practices](./mamba) would be relevant here.
 
 The model has been trained on approximtely 6T tokens consisting a mixture of many data sources such as RefineWeb, Cosmopedia and Math data.
 

docs/source/en/model_doc/hiera.md

@@ -31,7 +31,7 @@ alt="drawing" width="600"/>
 
 <small> Hiera architecture. Taken from the <a href="https://arxiv.org/abs/2306.00989">original paper.</a> </small>
 
-This model was a joint contibution by [EduardoPacheco](https://huggingface.co/EduardoPacheco) and [namangarg110](https://huggingface.co/namangarg110). The original code can be found [here] (https://github.com/facebookresearch/hiera).
+This model was a joint contribution by [EduardoPacheco](https://huggingface.co/EduardoPacheco) and [namangarg110](https://huggingface.co/namangarg110). The original code can be found [here] (https://github.com/facebookresearch/hiera).
 
 ## Resources
 

docs/source/en/model_doc/jamba.md

@@ -33,7 +33,7 @@ alt="drawing" width="600"/>
 
 ## Usage
 
-### Presequities
+### Prerequisites
 
 Jamba requires you use `transformers` version 4.39.0 or higher:
 ```bash

docs/source/en/model_doc/matcha.md

@@ -61,7 +61,7 @@ print(processor.decode(predictions[0], skip_special_tokens=True))
 
 ## Fine-tuning
 
-To fine-tune MatCha, refer to the pix2struct [fine-tuning notebook](https://github.com/huggingface/notebooks/blob/main/examples/image_captioning_pix2struct.ipynb). For `Pix2Struct` models, we have found out that fine-tuning the model with Adafactor and cosine learning rate scheduler leads to faste convergence:
+To fine-tune MatCha, refer to the pix2struct [fine-tuning notebook](https://github.com/huggingface/notebooks/blob/main/examples/image_captioning_pix2struct.ipynb). For `Pix2Struct` models, we have found out that fine-tuning the model with Adafactor and cosine learning rate scheduler leads to faster convergence:
 ```python
 from transformers.optimization import Adafactor, get_cosine_schedule_with_warmup
 

docs/source/en/model_doc/mbart.md

@@ -83,7 +83,7 @@ keyword, and target text format passed with the `text_label` keyword argument.
 ## Overview of MBart-50
 
 MBart-50 was introduced in the [Multilingual Translation with Extensible Multilingual Pretraining and Finetuning](https://arxiv.org/abs/2008.00401) paper by Yuqing Tang, Chau Tran, Xian Li, Peng-Jen Chen, Naman Goyal, Vishrav
-Chaudhary, Jiatao Gu, Angela Fan. MBart-50 is created using the original *mbart-large-cc25* checkpoint by extendeding
+Chaudhary, Jiatao Gu, Angela Fan. MBart-50 is created using the original *mbart-large-cc25* checkpoint by extending
 its embedding layers with randomly initialized vectors for an extra set of 25 language tokens and then pretrained on 50
 languages.
 

docs/source/en/model_doc/mixtral.md

@@ -31,7 +31,7 @@ Mixtral-8x7B is the second large language model (LLM) released by [mistral.ai](h
 Mixtral-8x7B is a decoder-only Transformer with the following architectural choices:
 
 - Mixtral is a Mixture of Experts (MoE) model with 8 experts per MLP, with a total of 45 billion parameters. To learn more about mixture-of-experts, refer to the [blog post](https://huggingface.co/blog/moe).
-- Despite the model having 45 billion parameters,, the compute required for a single forward pass is the same as that of a 14 billion parameter model. This is because even though each of the experts have to be loaded in RAM (70B like ram requirement) each token from the hidden states are dispatched twice (top 2 routing) and thus the compute (the operation required at each forward computation) is just 2 X sequence_length. 
+- Despite the model having 45 billion parameters, the compute required for a single forward pass is the same as that of a 14 billion parameter model. This is because even though each of the experts have to be loaded in RAM (70B like ram requirement) each token from the hidden states are dispatched twice (top 2 routing) and thus the compute (the operation required at each forward computation) is just 2 X sequence_length. 
 
 The following implementation details are shared with Mistral AI's first model [Mistral-7B](mistral):
 - Sliding Window Attention - Trained with 8k context length and fixed cache size, with a theoretical attention span of 128K tokens

docs/source/en/model_doc/mms.md

@@ -242,7 +242,7 @@ export UROMAN=$(pwd)
 ```
 
 You can then pre-process the text input using the following code snippet. You can either rely on using the bash variable 
-`UROMAN` to point to the uroman repository, or you can pass the uroman directory as an argument to the `uromaize` function:
+`UROMAN` to point to the uroman repository, or you can pass the uroman directory as an argument to the `uromanize` function:
 
 ```python
 import torch
@@ -270,9 +270,9 @@ def uromanize(input_string, uroman_path):
     return stdout.decode()[:-1]
 
 text = "이봐 무슨 일이야"
-uromaized_text = uromanize(text, uroman_path=os.environ["UROMAN"])
+uromanized_text = uromanize(text, uroman_path=os.environ["UROMAN"])
 
-inputs = tokenizer(text=uromaized_text, return_tensors="pt")
+inputs = tokenizer(text=uromanized_text, return_tensors="pt")
 
 set_seed(555)  # make deterministic
 with torch.no_grad():

docs/source/en/model_doc/mpt.md

@@ -18,7 +18,7 @@ rendered properly in your Markdown viewer.
 
 ## Overview
 
-The MPT model was proposed by the [MosaicML](https://www.mosaicml.com/) team and released with multiple sizes and finetuned variants. The MPT models is a series of open source and commercially usable LLMs pre-trained on 1T tokens. 
+The MPT model was proposed by the [MosaicML](https://www.mosaicml.com/) team and released with multiple sizes and finetuned variants. The MPT models are a series of open source and commercially usable LLMs pre-trained on 1T tokens. 
 
 MPT models are GPT-style decoder-only transformers with several improvements: performance-optimized layer implementations, architecture changes that provide greater training stability, and the elimination of context length limits by replacing positional embeddings with ALiBi. 
 

docs/source/en/model_doc/oneformer.md

@@ -39,7 +39,7 @@ This model was contributed by [Jitesh Jain](https://huggingface.co/praeclarumjj3
 - If you want to train the model in a distributed environment across multiple nodes, then one should update the
   `get_num_masks` function inside in the `OneFormerLoss` class of `modeling_oneformer.py`. When training on multiple nodes, this should be
   set to the average number of target masks across all nodes, as can be seen in the original implementation [here](https://github.com/SHI-Labs/OneFormer/blob/33ebb56ed34f970a30ae103e786c0cb64c653d9a/oneformer/modeling/criterion.py#L287).
-- One can use [`OneFormerProcessor`] to prepare input images and task inputs for the model and optional targets for the model. [`OneformerProcessor`] wraps [`OneFormerImageProcessor`] and [`CLIPTokenizer`] into a single instance to both prepare the images and encode the task inputs.
+- One can use [`OneFormerProcessor`] to prepare input images and task inputs for the model and optional targets for the model. [`OneFormerProcessor`] wraps [`OneFormerImageProcessor`] and [`CLIPTokenizer`] into a single instance to both prepare the images and encode the task inputs.
 - To get the final segmentation, depending on the task, you can call [`~OneFormerProcessor.post_process_semantic_segmentation`] or [`~OneFormerImageProcessor.post_process_instance_segmentation`] or [`~OneFormerImageProcessor.post_process_panoptic_segmentation`]. All three tasks can be solved using [`OneFormerForUniversalSegmentation`] output, panoptic segmentation accepts an optional `label_ids_to_fuse` argument to fuse instances of the target object/s (e.g. sky) together.
 
 ## Resources

docs/source/en/model_doc/openai-gpt.md

@@ -29,7 +29,7 @@ rendered properly in your Markdown viewer.
 
 OpenAI GPT model was proposed in [Improving Language Understanding by Generative Pre-Training](https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf)
 by Alec Radford, Karthik Narasimhan, Tim Salimans and Ilya Sutskever. It's a causal (unidirectional) transformer
-pre-trained using language modeling on a large corpus will long range dependencies, the Toronto Book Corpus.
+pre-trained using language modeling on a large corpus with long range dependencies, the Toronto Book Corpus.
 
 The abstract from the paper is the following:
 

docs/source/en/model_doc/phobert.md

@@ -54,7 +54,7 @@ This model was contributed by [dqnguyen](https://huggingface.co/dqnguyen). The o
 
 <Tip> 
 
-PhoBERT implementation is the same as BERT, except for tokenization. Refer to [EART documentation](bert) for information on 
+PhoBERT implementation is the same as BERT, except for tokenization. Refer to [BERT documentation](bert) for information on 
 configuration classes and their parameters. PhoBERT-specific tokenizer is documented below.  
 
 </Tip>

docs/source/en/model_doc/seggpt.md

@@ -27,7 +27,7 @@ The abstract from the paper is the following:
 Tips:
 - One can use [`SegGptImageProcessor`] to prepare image input, prompt and mask to the model.
 - One can either use segmentation maps or RGB images as prompt masks. If using the latter make sure to set `do_convert_rgb=False` in the `preprocess` method.
-- It's highly advisable to pass `num_labels` when using `segmetantion_maps` (not considering background) during preprocessing and postprocessing with [`SegGptImageProcessor`] for your use case.
+- It's highly advisable to pass `num_labels` when using `segmentation_maps` (not considering background) during preprocessing and postprocessing with [`SegGptImageProcessor`] for your use case.
 - When doing inference with [`SegGptForImageSegmentation`] if your `batch_size` is greater than 1 you can use feature ensemble across your images by passing `feature_ensemble=True` in the forward method.
 
 Here's how to use the model for one-shot semantic segmentation:

docs/source/en/model_doc/swin2sr.md

@@ -19,7 +19,7 @@ rendered properly in your Markdown viewer.
 ## Overview
 
 The Swin2SR model was proposed in [Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration](https://arxiv.org/abs/2209.11345) by Marcos V. Conde, Ui-Jin Choi, Maxime Burchi, Radu Timofte.
-Swin2R improves the [SwinIR](https://github.com/JingyunLiang/SwinIR/) model by incorporating [Swin Transformer v2](swinv2) layers which mitigates issues such as training instability, resolution gaps between pre-training
+Swin2SR improves the [SwinIR](https://github.com/JingyunLiang/SwinIR/) model by incorporating [Swin Transformer v2](swinv2) layers which mitigates issues such as training instability, resolution gaps between pre-training
 and fine-tuning, and hunger on data.
 
 The abstract from the paper is the following:

docs/source/en/model_doc/vits.md

@@ -127,7 +127,7 @@ export UROMAN=$(pwd)
 ```
 
 You can then pre-process the text input using the following code snippet. You can either rely on using the bash variable 
-`UROMAN` to point to the uroman repository, or you can pass the uroman directory as an argument to the `uromaize` function:
+`UROMAN` to point to the uroman repository, or you can pass the uroman directory as an argument to the `uromanize` function:
 
 ```python
 import torch
@@ -155,9 +155,9 @@ def uromanize(input_string, uroman_path):
     return stdout.decode()[:-1]
 
 text = "이봐 무슨 일이야"
-uromaized_text = uromanize(text, uroman_path=os.environ["UROMAN"])
+uromanized_text = uromanize(text, uroman_path=os.environ["UROMAN"])
 
-inputs = tokenizer(text=uromaized_text, return_tensors="pt")
+inputs = tokenizer(text=uromanized_text, return_tensors="pt")
 
 set_seed(555)  # make deterministic
 with torch.no_grad():

docs/source/en/model_doc/xlm-roberta.md

@@ -43,7 +43,7 @@ low-resource languages, improving 11.8% in XNLI accuracy for Swahili and 9.2% fo
 also present a detailed empirical evaluation of the key factors that are required to achieve these gains, including the
 trade-offs between (1) positive transfer and capacity dilution and (2) the performance of high and low resource
 languages at scale. Finally, we show, for the first time, the possibility of multilingual modeling without sacrificing
-per-language performance; XLM-Ris very competitive with strong monolingual models on the GLUE and XNLI benchmarks. We
+per-language performance; XLM-R is very competitive with strong monolingual models on the GLUE and XNLI benchmarks. We
 will make XLM-R code, data, and models publicly available.*
 
 This model was contributed by [stefan-it](https://huggingface.co/stefan-it). The original code can be found [here](https://github.com/pytorch/fairseq/tree/master/examples/xlmr).

docs/source/en/model_doc/xlnet.md

@@ -166,7 +166,7 @@ This model was contributed by [thomwolf](https://huggingface.co/thomwolf). The o
 [[autodoc]] TFXLNetForSequenceClassification
     - call
 
-## TFLNetForMultipleChoice
+## TFXLNetForMultipleChoice
 
 [[autodoc]] TFXLNetForMultipleChoice
     - call