remove duplicate words

awesome-transformers.md

@@ -596,7 +596,7 @@ Keywords: Data-Centric AI, Data Quality, Noisy Labels, Outlier Detection, Active
 
 ## [BentoML](https://github.com/bentoml/BentoML)
 
-[BentoML](https://github.com/bentoml) is the unified framework for for building, shipping, and scaling production-ready AI applications incorporating traditional ML, pre-trained AI models, Generative and Large Language Models. 
+[BentoML](https://github.com/bentoml) is the unified framework for building, shipping, and scaling production-ready AI applications incorporating traditional ML, pre-trained AI models, Generative and Large Language Models. 
 All Hugging Face models and pipelines can be seamlessly integrated into BentoML applications, enabling the running of models on the most suitable hardware and independent scaling based on usage.
 
 Keywords: BentoML, Framework, Deployment, AI Applications

docs/source/en/model_doc/prophetnet.md

@@ -51,7 +51,7 @@ The Authors' code can be found [here](https://github.com/microsoft/ProphetNet).
 
 - ProphetNet is a model with absolute position embeddings so it's usually advised to pad the inputs on the right rather than
   the left.
-- The model architecture is based on the original Transformer, but replaces the “standard” self-attention mechanism in the decoder by a a main self-attention mechanism and a self and n-stream (predict) self-attention mechanism.
+- The model architecture is based on the original Transformer, but replaces the “standard” self-attention mechanism in the decoder by a main self-attention mechanism and a self and n-stream (predict) self-attention mechanism.
 
 ## Resources
 

docs/source/en/preprocessing.md

@@ -471,7 +471,7 @@ from [`DetrImageProcessor`] and define a custom `collate_fn` to batch images tog
 
 ## Multimodal
 
-For tasks involving multimodal inputs, you'll need a [processor](main_classes/processors) to prepare your dataset for the model. A processor couples together two processing objects such as as tokenizer and feature extractor.
+For tasks involving multimodal inputs, you'll need a [processor](main_classes/processors) to prepare your dataset for the model. A processor couples together two processing objects such as tokenizer and feature extractor.
 
 Load the [LJ Speech](https://huggingface.co/datasets/lj_speech) dataset (see the 🤗 [Datasets tutorial](https://huggingface.co/docs/datasets/load_hub) for more details on how to load a dataset) to see how you can use a processor for automatic speech recognition (ASR):
 

docs/source/en/testing.md

@@ -1011,7 +1011,7 @@ slow models to do qualitative testing. To see the use of these simply look for *
 grep tiny tests examples
 ```
 
-Here is a an example of a [script](https://github.com/huggingface/transformers/tree/main/scripts/fsmt/fsmt-make-tiny-model.py) that created the tiny model
+Here is an example of a [script](https://github.com/huggingface/transformers/tree/main/scripts/fsmt/fsmt-make-tiny-model.py) that created the tiny model
 [stas/tiny-wmt19-en-de](https://huggingface.co/stas/tiny-wmt19-en-de). You can easily adjust it to your specific
 model's architecture.