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/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/preprocessing.md | https://huggingface.co/docs/transformers/en/preprocessing/ | .md | <!--Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
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Unless required by applicable law or agr... | 0_0_0 |
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⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/preprocessing.md | https://huggingface.co/docs/transformers/en/preprocessing/#preprocess | .md | [[open-in-colab]]
Before you can train a model on a dataset, it needs to be preprocessed into the expected model input format. Whether your data is text, images, or audio, it needs to be converted and assembled into batches of tensors. 🤗 Transformers provides a set of preprocessing classes to help prepare your data ... | 0_1_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/preprocessing.md | https://huggingface.co/docs/transformers/en/preprocessing/#preprocess | .md | * Text, use a [Tokenizer](./main_classes/tokenizer) to convert text into a sequence of tokens, create a numerical representation of the tokens, and assemble them into tensors.
* Speech and audio, use a [Feature extractor](./main_classes/feature_extractor) to extract sequential features from audio waveforms and convert ... | 0_1_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/preprocessing.md | https://huggingface.co/docs/transformers/en/preprocessing/#preprocess | .md | * Image inputs use a [ImageProcessor](./main_classes/image_processor) to convert images into tensors.
* Multimodal inputs, use a [Processor](./main_classes/processors) to combine a tokenizer and a feature extractor or image processor.
<Tip>
`AutoProcessor` **always** works and automatically chooses the correct clas... | 0_1_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/preprocessing.md | https://huggingface.co/docs/transformers/en/preprocessing/#preprocess | .md | </Tip>
Before you begin, install 🤗 Datasets so you can load some datasets to experiment with:
```bash
pip install datasets
``` | 0_1_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/preprocessing.md | https://huggingface.co/docs/transformers/en/preprocessing/#natural-language-processing | .md | <Youtube id="Yffk5aydLzg"/>
The main tool for preprocessing textual data is a [tokenizer](main_classes/tokenizer). A tokenizer splits text into *tokens* according to a set of rules. The tokens are converted into numbers and then tensors, which become the model inputs. Any additional inputs required by the model are a... | 0_2_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/preprocessing.md | https://huggingface.co/docs/transformers/en/preprocessing/#natural-language-processing | .md | <Tip>
If you plan on using a pretrained model, it's important to use the associated pretrained tokenizer. This ensures the text is split the same way as the pretraining corpus, and uses the same corresponding tokens-to-index (usually referred to as the *vocab*) during pretraining.
</Tip>
Get started by loading a ... | 0_2_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/preprocessing.md | https://huggingface.co/docs/transformers/en/preprocessing/#natural-language-processing | .md | >>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-cased")
```
Then pass your text to the tokenizer:
```py
>>> encoded_input = tokenizer("Do not meddle in the affairs of wizards, for they are subtle and quick to anger.")
>>> print(encoded_input)
{'input_ids': [101, 2079, 2025, 19960, 10362, 1999, ... | 0_2_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/preprocessing.md | https://huggingface.co/docs/transformers/en/preprocessing/#natural-language-processing | .md | 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}
```
The tokenizer returns a dictionary with three important items:
* [input_ids](glossary#input-ids) are the indices corresponding to each token in th... | 0_2_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/preprocessing.md | https://huggingface.co/docs/transformers/en/preprocessing/#natural-language-processing | .md | * [attention_mask](glossary#attention-mask) indicates whether a token should be attended to or not.
* [token_type_ids](glossary#token-type-ids) identifies which sequence a token belongs to when there is more than one sequence.
Return your input by decoding the `input_ids`:
```py
>>> tokenizer.decode(encoded_input["... | 0_2_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/preprocessing.md | https://huggingface.co/docs/transformers/en/preprocessing/#natural-language-processing | .md | '[CLS] Do not meddle in the affairs of wizards, for they are subtle and quick to anger. [SEP]'
```
As you can see, the tokenizer added two special tokens - `CLS` and `SEP` (classifier and separator) - to the sentence. Not all models need
special tokens, but if they do, the tokenizer automatically adds them for you. ... | 0_2_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/preprocessing.md | https://huggingface.co/docs/transformers/en/preprocessing/#natural-language-processing | .md | ```py
>>> batch_sentences = [
... "But what about second breakfast?",
... "Don't think he knows about second breakfast, Pip.",
... "What about elevensies?",
... ]
>>> encoded_inputs = tokenizer(batch_sentences)
>>> print(encoded_inputs)
{'input_ids': [[101, 1252, 1184, 1164, 1248, 6462, 136, 102],
[101, 179... | 0_2_6 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/preprocessing.md | https://huggingface.co/docs/transformers/en/preprocessing/#natural-language-processing | .md | [101, 1327, 1164, 5450, 23434, 136, 102]],
'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0]],
'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1]]}
``` | 0_2_7 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/preprocessing.md | https://huggingface.co/docs/transformers/en/preprocessing/#pad | .md | Sentences aren't always the same length which can be an issue because tensors, the model inputs, need to have a uniform shape. Padding is a strategy for ensuring tensors are rectangular by adding a special *padding token* to shorter sentences.
Set the `padding` parameter to `True` to pad the shorter sequences in the ... | 0_3_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/preprocessing.md | https://huggingface.co/docs/transformers/en/preprocessing/#pad | .md | ... "But what about second breakfast?",
... "Don't think he knows about second breakfast, Pip.",
... "What about elevensies?",
... ]
>>> encoded_input = tokenizer(batch_sentences, padding=True)
>>> print(encoded_input)
{'input_ids': [[101, 1252, 1184, 1164, 1248, 6462, 136, 102, 0, 0, 0, 0, 0, 0, 0],
[101, ... | 0_3_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/preprocessing.md | https://huggingface.co/docs/transformers/en/preprocessing/#pad | .md | [101, 1327, 1164, 5450, 23434, 136, 102, 0, 0, 0, 0, 0, 0, 0, 0]],
'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],
'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1, 1, 1, 1... | 0_3_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/preprocessing.md | https://huggingface.co/docs/transformers/en/preprocessing/#truncation | .md | On the other end of the spectrum, sometimes a sequence may be too long for a model to handle. In this case, you'll need to truncate the sequence to a shorter length.
Set the `truncation` parameter to `True` to truncate a sequence to the maximum length accepted by the model:
```py
>>> batch_sentences = [
... "Bu... | 0_4_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/preprocessing.md | https://huggingface.co/docs/transformers/en/preprocessing/#truncation | .md | ... "Don't think he knows about second breakfast, Pip.",
... "What about elevensies?",
... ]
>>> encoded_input = tokenizer(batch_sentences, padding=True, truncation=True)
>>> print(encoded_input)
{'input_ids': [[101, 1252, 1184, 1164, 1248, 6462, 136, 102, 0, 0, 0, 0, 0, 0, 0],
[101, 1790, 112, 189, 1341, 1119,... | 0_4_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/preprocessing.md | https://huggingface.co/docs/transformers/en/preprocessing/#truncation | .md | 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],
'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0]]... | 0_4_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/preprocessing.md | https://huggingface.co/docs/transformers/en/preprocessing/#build-tensors | .md | Finally, you want the tokenizer to return the actual tensors that get fed to the model.
Set the `return_tensors` parameter to either `pt` for PyTorch, or `tf` for TensorFlow:
<frameworkcontent>
<pt>
```py
>>> batch_sentences = [
... "But what about second breakfast?",
... "Don't think he knows about secon... | 0_5_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/preprocessing.md | https://huggingface.co/docs/transformers/en/preprocessing/#build-tensors | .md | >>> encoded_input = tokenizer(batch_sentences, padding=True, truncation=True, return_tensors="pt")
>>> print(encoded_input)
{'input_ids': tensor([[101, 1252, 1184, 1164, 1248, 6462, 136, 102, 0, 0, 0, 0, 0, 0, 0],
[101, 1790, 112, 189, 1341, 1119, 3520, 1164, 1248, 6462, 117, 21902, 1643, 119, 102],
[101, 1327, 1164, 5... | 0_5_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/preprocessing.md | https://huggingface.co/docs/transformers/en/preprocessing/#build-tensors | .md | 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]),
'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 0, 0, 0, ... | 0_5_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/preprocessing.md | https://huggingface.co/docs/transformers/en/preprocessing/#build-tensors | .md | ... "But what about second breakfast?",
... "Don't think he knows about second breakfast, Pip.",
... "What about elevensies?",
... ]
>>> encoded_input = tokenizer(batch_sentences, padding=True, truncation=True, return_tensors="tf")
>>> print(encoded_input)
{'input_ids': <tf.Tensor: shape=(2, 9), dtype=int32... | 0_5_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/preprocessing.md | https://huggingface.co/docs/transformers/en/preprocessing/#build-tensors | .md | [101, 1790, 112, 189, 1341, 1119, 3520, 1164, 1248, 6462, 117, 21902, 1643, 119, 102],
[101, 1327, 1164, 5450, 23434, 136, 102, 0, 0, 0, 0, 0, 0, 0, 0]],
dtype=int32)>,
'token_type_ids': <tf.Tensor: shape=(2, 9), dtype=int32, numpy=
array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,... | 0_5_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/preprocessing.md | https://huggingface.co/docs/transformers/en/preprocessing/#build-tensors | .md | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=int32)>,
'attention_mask': <tf.Tensor: shape=(2, 9), dtype=int32, numpy=
array([[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=int32)>}
```
</tf>
</frameworkcontent>... | 0_5_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/preprocessing.md | https://huggingface.co/docs/transformers/en/preprocessing/#build-tensors | .md | only `truncation`. `text-generation` pipelines support `max_length`, `truncation`, `padding` and `add_special_tokens`.
In `fill-mask` pipelines, tokenizer arguments can be passed in the `tokenizer_kwargs` argument (dictionary).
</Tip> | 0_5_6 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/preprocessing.md | https://huggingface.co/docs/transformers/en/preprocessing/#audio | .md | For audio tasks, you'll need a [feature extractor](main_classes/feature_extractor) to prepare your dataset for the model. The feature extractor is designed to extract features from raw audio data, and convert them into tensors.
Load the [MInDS-14](https://huggingface.co/datasets/PolyAI/minds14) dataset (see the 🤗 [D... | 0_6_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/preprocessing.md | https://huggingface.co/docs/transformers/en/preprocessing/#audio | .md | ```py
>>> from datasets import load_dataset, Audio | 0_6_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/preprocessing.md | https://huggingface.co/docs/transformers/en/preprocessing/#audio | .md | >>> dataset = load_dataset("PolyAI/minds14", name="en-US", split="train")
```
Access the first element of the `audio` column to take a look at the input. Calling the `audio` column automatically loads and resamples the audio file:
```py
>>> dataset[0]["audio"]
{'array': array([ 0. , 0.00024414, -0.00024414,... | 0_6_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/preprocessing.md | https://huggingface.co/docs/transformers/en/preprocessing/#audio | .md | {'array': array([ 0. , 0.00024414, -0.00024414, ..., -0.00024414,
0. , 0. ], dtype=float32),
'path': '/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~JOINT_ACCOUNT/602ba55abb1e6d0fbce92065.wav',
'sampling_rate': 8000}
``... | 0_6_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/preprocessing.md | https://huggingface.co/docs/transformers/en/preprocessing/#audio | .md | * `path` points to the location of the audio file.
* `sampling_rate` refers to how many data points in the speech signal are measured per second. | 0_6_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/preprocessing.md | https://huggingface.co/docs/transformers/en/preprocessing/#audio | .md | * `sampling_rate` refers to how many data points in the speech signal are measured per second.
For this tutorial, you'll use the [Wav2Vec2](https://huggingface.co/facebook/wav2vec2-base) model. Take a look at the model card, and you'll learn Wav2Vec2 is pretrained on 16kHz sampled speech audio. It is important your a... | 0_6_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/preprocessing.md | https://huggingface.co/docs/transformers/en/preprocessing/#audio | .md | 1. Use 🤗 Datasets' [`~datasets.Dataset.cast_column`] method to upsample the sampling rate to 16kHz:
```py
>>> dataset = dataset.cast_column("audio", Audio(sampling_rate=16_000))
```
2. Call the `audio` column again to resample the audio file:
```py
>>> dataset[0]["audio"]
{'array': array([ 2.3443763e-05, 2.1729... | 0_6_6 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/preprocessing.md | https://huggingface.co/docs/transformers/en/preprocessing/#audio | .md | 3.8356509e-05, -7.3497440e-06, -2.1754686e-05], dtype=float32),
'path': '/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~JOINT_ACCOUNT/602ba55abb1e6d0fbce92065.wav',
'sampling_rate': 16000}
```
Next, load a feature extractor to normalize and... | 0_6_7 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/preprocessing.md | https://huggingface.co/docs/transformers/en/preprocessing/#audio | .md | Load the feature extractor with [`AutoFeatureExtractor.from_pretrained`]:
```py
>>> from transformers import AutoFeatureExtractor | 0_6_8 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/preprocessing.md | https://huggingface.co/docs/transformers/en/preprocessing/#audio | .md | >>> feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base")
```
Pass the audio `array` to the feature extractor. We also recommend adding the `sampling_rate` argument in the feature extractor in order to better debug any silent errors that may occur.
```py
>>> audio_input = [dataset[0]["a... | 0_6_9 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/preprocessing.md | https://huggingface.co/docs/transformers/en/preprocessing/#audio | .md | {'input_values': [array([ 3.8106556e-04, 2.7506407e-03, 2.8015103e-03, ...,
5.6335266e-04, 4.6588284e-06, -1.7142107e-04], dtype=float32)]}
```
Just like the tokenizer, you can apply padding or truncation to handle variable sequences in a batch. Take a look at the sequence length of these two audio samples:
```p... | 0_6_10 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/preprocessing.md | https://huggingface.co/docs/transformers/en/preprocessing/#audio | .md | >>> dataset[1]["audio"]["array"].shape
(106496,)
```
Create a function to preprocess the dataset so the audio samples are the same lengths. Specify a maximum sample length, and the feature extractor will either pad or truncate the sequences to match it:
```py
>>> def preprocess_function(examples):
... audio_arr... | 0_6_11 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/preprocessing.md | https://huggingface.co/docs/transformers/en/preprocessing/#audio | .md | ... inputs = feature_extractor(
... audio_arrays,
... sampling_rate=16000,
... padding=True,
... max_length=100000,
... truncation=True,
... )
... return inputs
```
Apply the `preprocess_function` to the first few examples in the dataset:
```py
>>> processed_datas... | 0_6_12 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/preprocessing.md | https://huggingface.co/docs/transformers/en/preprocessing/#audio | .md | ```py
>>> processed_dataset["input_values"][0].shape
(100000,) | 0_6_13 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/preprocessing.md | https://huggingface.co/docs/transformers/en/preprocessing/#audio | .md | >>> processed_dataset["input_values"][1].shape
(100000,)
``` | 0_6_14 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/preprocessing.md | https://huggingface.co/docs/transformers/en/preprocessing/#computer-vision | .md | For computer vision tasks, you'll need an [image processor](main_classes/image_processor) to prepare your dataset for the model.
Image preprocessing consists of several steps that convert images into the input expected by the model. These steps
include but are not limited to resizing, normalizing, color channel correct... | 0_7_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/preprocessing.md | https://huggingface.co/docs/transformers/en/preprocessing/#computer-vision | .md | <Tip>
Image preprocessing often follows some form of image augmentation. Both image preprocessing and image augmentation
transform image data, but they serve different purposes:
* Image augmentation alters images in a way that can help prevent overfitting and increase the robustness of the model. You can get creati... | 0_7_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/preprocessing.md | https://huggingface.co/docs/transformers/en/preprocessing/#computer-vision | .md | * Image preprocessing guarantees that the images match the model’s expected input format. When fine-tuning a computer vision model, images must be preprocessed exactly as when the model was initially trained.
You can use any library you like for image augmentation. For image preprocessing, use the `ImageProcessor` as... | 0_7_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/preprocessing.md | https://huggingface.co/docs/transformers/en/preprocessing/#computer-vision | .md | </Tip>
Load the [food101](https://huggingface.co/datasets/food101) 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 an image processor with computer vision datasets:
<Tip>
Use 🤗 Datasets `split` parameter to o... | 0_7_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/preprocessing.md | https://huggingface.co/docs/transformers/en/preprocessing/#computer-vision | .md | >>> dataset = load_dataset("food101", split="train[:100]")
```
Next, take a look at the image with 🤗 Datasets [`Image`](https://huggingface.co/docs/datasets/package_reference/main_classes?highlight=image#datasets.Image) feature:
```py
>>> dataset[0]["image"]
```
<div class="flex justify-center">
<img src="https:... | 0_7_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/preprocessing.md | https://huggingface.co/docs/transformers/en/preprocessing/#computer-vision | .md | </div>
Load the image processor with [`AutoImageProcessor.from_pretrained`]:
```py
>>> from transformers import AutoImageProcessor | 0_7_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/preprocessing.md | https://huggingface.co/docs/transformers/en/preprocessing/#computer-vision | .md | >>> image_processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224")
``` | 0_7_6 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/preprocessing.md | https://huggingface.co/docs/transformers/en/preprocessing/#computer-vision | .md | First, let's add some image augmentation. You can use any library you prefer, but in this tutorial, we'll use torchvision's [`transforms`](https://pytorch.org/vision/stable/transforms.html) module. If you're interested in using another data augmentation library, learn how in the [Albumentations](https://colab.research.... | 0_7_7 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/preprocessing.md | https://huggingface.co/docs/transformers/en/preprocessing/#computer-vision | .md | or [Kornia notebooks](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification_kornia.ipynb). | 0_7_8 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/preprocessing.md | https://huggingface.co/docs/transformers/en/preprocessing/#computer-vision | .md | 1. Here we use [`Compose`](https://pytorch.org/vision/master/generated/torchvision.transforms.Compose.html) to chain together a couple of
transforms - [`RandomResizedCrop`](https://pytorch.org/vision/main/generated/torchvision.transforms.RandomResizedCrop.html) and [`ColorJitter`](https://pytorch.org/vision/main/genera... | 0_7_9 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/preprocessing.md | https://huggingface.co/docs/transformers/en/preprocessing/#computer-vision | .md | Note that for resizing, we can get the image size requirements from the `image_processor`. For some models, an exact height and
width are expected, for others only the `shortest_edge` is defined.
```py
>>> from torchvision.transforms import RandomResizedCrop, ColorJitter, Compose | 0_7_10 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/preprocessing.md | https://huggingface.co/docs/transformers/en/preprocessing/#computer-vision | .md | >>> size = (
... image_processor.size["shortest_edge"]
... if "shortest_edge" in image_processor.size
... else (image_processor.size["height"], image_processor.size["width"])
... ) | 0_7_11 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/preprocessing.md | https://huggingface.co/docs/transformers/en/preprocessing/#computer-vision | .md | >>> _transforms = Compose([RandomResizedCrop(size), ColorJitter(brightness=0.5, hue=0.5)])
```
2. The model accepts [`pixel_values`](model_doc/vision-encoder-decoder#transformers.VisionEncoderDecoderModel.forward.pixel_values)
as its input. `ImageProcessor` can take care of normalizing the images, and generating appr... | 0_7_12 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/preprocessing.md | https://huggingface.co/docs/transformers/en/preprocessing/#computer-vision | .md | ```py
>>> def transforms(examples):
... images = [_transforms(img.convert("RGB")) for img in examples["image"]]
... examples["pixel_values"] = image_processor(images, do_resize=False, return_tensors="pt")["pixel_values"]
... return examples
```
<Tip>
In the example above we set `do_resize=False` because... | 0_7_13 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/preprocessing.md | https://huggingface.co/docs/transformers/en/preprocessing/#computer-vision | .md | and leveraged the `size` attribute from the appropriate `image_processor`. If you do not resize images during image augmentation,
leave this parameter out. By default, `ImageProcessor` will handle the resizing.
If you wish to normalize images as a part of the augmentation transformation, use the `image_processor.imag... | 0_7_14 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/preprocessing.md | https://huggingface.co/docs/transformers/en/preprocessing/#computer-vision | .md | </Tip>
3. Then use 🤗 Datasets[`~datasets.Dataset.set_transform`] to apply the transforms on the fly:
```py
>>> dataset.set_transform(transforms)
```
4. Now when you access the image, you'll notice the image processor has added `pixel_values`. You can pass your processed dataset to the model now!
```py
>>> datase... | 0_7_15 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/preprocessing.md | https://huggingface.co/docs/transformers/en/preprocessing/#computer-vision | .md | ```py
>>> import numpy as np
>>> import matplotlib.pyplot as plt | 0_7_16 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/preprocessing.md | https://huggingface.co/docs/transformers/en/preprocessing/#computer-vision | .md | >>> img = dataset[0]["pixel_values"]
>>> plt.imshow(img.permute(1, 2, 0))
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/preprocessed_image.png"/>
</div>
<Tip>
For tasks like object detection, semantic segmentation, instance segmentati... | 0_7_17 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/preprocessing.md | https://huggingface.co/docs/transformers/en/preprocessing/#computer-vision | .md | offers post processing methods. These methods convert model's raw outputs into meaningful predictions such as bounding boxes,
or segmentation maps.
</Tip> | 0_7_18 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/preprocessing.md | https://huggingface.co/docs/transformers/en/preprocessing/#pad | .md | In some cases, for instance, when fine-tuning [DETR](./model_doc/detr), the model applies scale augmentation at training
time. This may cause images to be different sizes in a batch. You can use [`DetrImageProcessor.pad`]
from [`DetrImageProcessor`] and define a custom `collate_fn` to batch images together.
```py
>>>... | 0_8_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/preprocessing.md | https://huggingface.co/docs/transformers/en/preprocessing/#pad | .md | ... encoding = image_processor.pad(pixel_values, return_tensors="pt")
... labels = [item["labels"] for item in batch]
... batch = {}
... batch["pixel_values"] = encoding["pixel_values"]
... batch["pixel_mask"] = encoding["pixel_mask"]
... batch["labels"] = labels
... return batch
``` | 0_8_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/preprocessing.md | https://huggingface.co/docs/transformers/en/preprocessing/#multimodal | .md | 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 tu... | 0_9_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/preprocessing.md | https://huggingface.co/docs/transformers/en/preprocessing/#multimodal | .md | ```py
>>> from datasets import load_dataset | 0_9_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/preprocessing.md | https://huggingface.co/docs/transformers/en/preprocessing/#multimodal | .md | >>> lj_speech = load_dataset("lj_speech", split="train")
```
For ASR, you're mainly focused on `audio` and `text` so you can remove the other columns:
```py
>>> lj_speech = lj_speech.map(remove_columns=["file", "id", "normalized_text"])
```
Now take a look at the `audio` and `text` columns:
```py
>>> lj_speech[... | 0_9_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/preprocessing.md | https://huggingface.co/docs/transformers/en/preprocessing/#multimodal | .md | 7.3242188e-04, 2.1362305e-04, 6.1035156e-05], dtype=float32),
'path': '/root/.cache/huggingface/datasets/downloads/extracted/917ece08c95cf0c4115e45294e3cd0dee724a1165b7fc11798369308a465bd26/LJSpeech-1.1/wavs/LJ001-0001.wav',
'sampling_rate': 22050} | 0_9_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/preprocessing.md | https://huggingface.co/docs/transformers/en/preprocessing/#multimodal | .md | >>> lj_speech[0]["text"]
'Printing, in the only sense with which we are at present concerned, differs from most if not from all the arts and crafts represented in the Exhibition'
```
Remember you should always [resample](preprocessing#audio) your audio dataset's sampling rate to match the sampling rate of the dataset... | 0_9_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/preprocessing.md | https://huggingface.co/docs/transformers/en/preprocessing/#multimodal | .md | ```
Load a processor with [`AutoProcessor.from_pretrained`]:
```py
>>> from transformers import AutoProcessor | 0_9_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/preprocessing.md | https://huggingface.co/docs/transformers/en/preprocessing/#multimodal | .md | >>> processor = AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h")
```
1. Create a function to process the audio data contained in `array` to `input_values`, and tokenize `text` to `labels`. These are the inputs to the model:
```py
>>> def prepare_dataset(example):
... audio = example["audio"]
... ... | 0_9_6 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/preprocessing.md | https://huggingface.co/docs/transformers/en/preprocessing/#multimodal | .md | ... example.update(processor(audio=audio["array"], text=example["text"], sampling_rate=16000))
... return example
```
2. Apply the `prepare_dataset` function to a sample:
```py
>>> prepare_dataset(lj_speech[0])
```
The processor has now added `input_values` and `labels`, and the sampling rate has also be... | 0_9_7 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/sagemaker.md | https://huggingface.co/docs/transformers/en/sagemaker/ | .md | <!---
Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or a... | 1_0_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/sagemaker.md | https://huggingface.co/docs/transformers/en/sagemaker/ | .md | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your M... | 1_0_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/sagemaker.md | https://huggingface.co/docs/transformers/en/sagemaker/#run-training-on-amazon-sagemaker | .md | The documentation has been moved to [hf.co/docs/sagemaker](https://huggingface.co/docs/sagemaker). This page will be removed in `transformers` 5.0. | 1_1_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/sagemaker.md | https://huggingface.co/docs/transformers/en/sagemaker/#table-of-contents | .md | - [Train Hugging Face models on Amazon SageMaker with the SageMaker Python SDK](https://huggingface.co/docs/sagemaker/train)
- [Deploy Hugging Face models to Amazon SageMaker with the SageMaker Python SDK](https://huggingface.co/docs/sagemaker/inference) | 1_2_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_sharing.md | https://huggingface.co/docs/transformers/en/model_sharing/ | .md | <!--Copyright 2022 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | 2_0_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_sharing.md | https://huggingface.co/docs/transformers/en/model_sharing/ | .md | an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered ... | 2_0_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_sharing.md | https://huggingface.co/docs/transformers/en/model_sharing/#share-a-model | .md | The last two tutorials showed how you can fine-tune a model with PyTorch, Keras, and 🤗 Accelerate for distributed setups. The next step is to share your model with the community! At Hugging Face, we believe in openly sharing knowledge and resources to democratize artificial intelligence for everyone. We encourage you ... | 2_1_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_sharing.md | https://huggingface.co/docs/transformers/en/model_sharing/#share-a-model | .md | In this tutorial, you will learn two methods for sharing a trained or fine-tuned model on the [Model Hub](https://huggingface.co/models):
- Programmatically push your files to the Hub.
- Drag-and-drop your files to the Hub with the web interface.
<iframe width="560" height="315" src="https://www.youtube.com/embed/X... | 2_1_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_sharing.md | https://huggingface.co/docs/transformers/en/model_sharing/#share-a-model | .md | picture-in-picture" allowfullscreen></iframe>
<Tip>
To share a model with the community, you need an account on [huggingface.co](https://huggingface.co/join). You can also join an existing organization or create a new one.
</Tip> | 2_1_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_sharing.md | https://huggingface.co/docs/transformers/en/model_sharing/#repository-features | .md | Each repository on the Model Hub behaves like a typical GitHub repository. Our repositories offer versioning, commit history, and the ability to visualize differences.
The Model Hub's built-in versioning is based on git and [git-lfs](https://git-lfs.github.com/). In other words, you can treat one model as one reposit... | 2_2_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_sharing.md | https://huggingface.co/docs/transformers/en/model_sharing/#repository-features | .md | As a result, you can load a specific model version with the `revision` parameter:
```py
>>> model = AutoModel.from_pretrained(
... "julien-c/EsperBERTo-small", revision="4c77982" # tag name, or branch name, or commit hash
... )
```
Files are also easily edited in a repository, and you can view the commit histo... | 2_2_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_sharing.md | https://huggingface.co/docs/transformers/en/model_sharing/#setup | .md | Before sharing a model to the Hub, you will need your Hugging Face credentials. If you have access to a terminal, run the following command in the virtual environment where 🤗 Transformers is installed. This will store your access token in your Hugging Face cache folder (`~/.cache/` by default):
```bash
huggingface-c... | 2_3_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_sharing.md | https://huggingface.co/docs/transformers/en/model_sharing/#setup | .md | ```bash
huggingface-cli login
```
If you are using a notebook like Jupyter or Colaboratory, make sure you have the [`huggingface_hub`](https://huggingface.co/docs/hub/adding-a-library) library installed. This library allows you to programmatically interact with the Hub.
```bash
pip install huggingface_hub
```
The... | 2_3_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_sharing.md | https://huggingface.co/docs/transformers/en/model_sharing/#setup | .md | ```py
>>> from huggingface_hub import notebook_login | 2_3_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_sharing.md | https://huggingface.co/docs/transformers/en/model_sharing/#setup | .md | >>> notebook_login()
``` | 2_3_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_sharing.md | https://huggingface.co/docs/transformers/en/model_sharing/#convert-a-model-for-all-frameworks | .md | To ensure your model can be used by someone working with a different framework, we recommend you convert and upload your model with both PyTorch and TensorFlow checkpoints. While users are still able to load your model from a different framework if you skip this step, it will be slower because 🤗 Transformers will need... | 2_4_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_sharing.md | https://huggingface.co/docs/transformers/en/model_sharing/#convert-a-model-for-all-frameworks | .md | Converting a checkpoint for another framework is easy. Make sure you have PyTorch and TensorFlow installed (see [here](installation) for installation instructions), and then find the specific model for your task in the other framework.
<frameworkcontent>
<pt>
Specify `from_tf=True` to convert a checkpoint from Tensor... | 2_4_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_sharing.md | https://huggingface.co/docs/transformers/en/model_sharing/#convert-a-model-for-all-frameworks | .md | ```py
>>> pt_model = DistilBertForSequenceClassification.from_pretrained("path/to/awesome-name-you-picked", from_tf=True)
>>> pt_model.save_pretrained("path/to/awesome-name-you-picked")
```
</pt>
<tf>
Specify `from_pt=True` to convert a checkpoint from PyTorch to TensorFlow:
```py
>>> tf_model = TFDistilBertForSequen... | 2_4_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_sharing.md | https://huggingface.co/docs/transformers/en/model_sharing/#convert-a-model-for-all-frameworks | .md | ```
Then you can save your new TensorFlow model with its new checkpoint:
```py
>>> tf_model.save_pretrained("path/to/awesome-name-you-picked")
```
</tf>
<jax>
If a model is available in Flax, you can also convert a checkpoint from PyTorch to Flax:
```py
>>> flax_model = FlaxDistilBertForSequenceClassification.fro... | 2_4_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_sharing.md | https://huggingface.co/docs/transformers/en/model_sharing/#push-a-model-during-training | .md | <frameworkcontent>
<pt>
<Youtube id="Z1-XMy-GNLQ"/>
Sharing a model to the Hub is as simple as adding an extra parameter or callback. Remember from the [fine-tuning tutorial](training), the [`TrainingArguments`] class is where you specify hyperparameters and additional training options. One of these training options ... | 2_5_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_sharing.md | https://huggingface.co/docs/transformers/en/model_sharing/#push-a-model-during-training | .md | ```py
>>> training_args = TrainingArguments(output_dir="my-awesome-model", push_to_hub=True)
```
Pass your training arguments as usual to [`Trainer`]:
```py
>>> trainer = Trainer(
... model=model,
... args=training_args,
... train_dataset=small_train_dataset,
... eval_dataset=small_eval_dataset,
...... | 2_5_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_sharing.md | https://huggingface.co/docs/transformers/en/model_sharing/#push-a-model-during-training | .md | ... eval_dataset=small_eval_dataset,
... compute_metrics=compute_metrics,
... )
```
After you fine-tune your model, call [`~transformers.Trainer.push_to_hub`] on [`Trainer`] to push the trained model to the Hub. 🤗 Transformers will even automatically add training hyperparameters, training results and framewo... | 2_5_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_sharing.md | https://huggingface.co/docs/transformers/en/model_sharing/#push-a-model-during-training | .md | ```
</pt>
<tf>
Share a model to the Hub with [`PushToHubCallback`]. In the [`PushToHubCallback`] function, add:
- An output directory for your model.
- A tokenizer.
- The `hub_model_id`, which is your Hub username and model name.
```py
>>> from transformers import PushToHubCallback | 2_5_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_sharing.md | https://huggingface.co/docs/transformers/en/model_sharing/#push-a-model-during-training | .md | >>> push_to_hub_callback = PushToHubCallback(
... output_dir="./your_model_save_path", tokenizer=tokenizer, hub_model_id="your-username/my-awesome-model"
... )
```
Add the callback to [`fit`](https://keras.io/api/models/model_training_apis/), and 🤗 Transformers will push the trained model to the Hub:
```py
>>>... | 2_5_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_sharing.md | https://huggingface.co/docs/transformers/en/model_sharing/#use-the-pushtohub-function | .md | You can also call `push_to_hub` directly on your model to upload it to the Hub.
Specify your model name in `push_to_hub`:
```py
>>> pt_model.push_to_hub("my-awesome-model")
```
This creates a repository under your username with the model name `my-awesome-model`. Users can now load your model with the `from_pretra... | 2_6_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_sharing.md | https://huggingface.co/docs/transformers/en/model_sharing/#use-the-pushtohub-function | .md | >>> model = AutoModel.from_pretrained("your_username/my-awesome-model")
```
If you belong to an organization and want to push your model under the organization name instead, just add it to the `repo_id`:
```py
>>> pt_model.push_to_hub("my-awesome-org/my-awesome-model")
```
The `push_to_hub` function can also be u... | 2_6_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_sharing.md | https://huggingface.co/docs/transformers/en/model_sharing/#use-the-pushtohub-function | .md | ```py
>>> tokenizer.push_to_hub("my-awesome-model")
```
Or perhaps you'd like to add the TensorFlow version of your fine-tuned PyTorch model:
```py
>>> tf_model.push_to_hub("my-awesome-model")
```
Now when you navigate to your Hugging Face profile, you should see your newly created model repository. Clicking on t... | 2_6_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_sharing.md | https://huggingface.co/docs/transformers/en/model_sharing/#use-the-pushtohub-function | .md | For more details on how to create and upload files to a repository, refer to the Hub documentation [here](https://huggingface.co/docs/hub/how-to-upstream). | 2_6_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_sharing.md | https://huggingface.co/docs/transformers/en/model_sharing/#upload-with-the-web-interface | .md | Users who prefer a no-code approach are able to upload a model through the Hub's web interface. Visit [huggingface.co/new](https://huggingface.co/new) to create a new repository:
From here, add some i... | 2_7_0 |
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