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

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* 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
This commit is contained in:
Nilay Bhatnagar
2024-09-09 14:17:24 +05:30
committed by GitHub
parent 489cbfd6d3
commit eedd21b9e7
61 changed files with 74 additions and 71 deletions
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docs/source/en/model_doc/oneformer.md
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@@ -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