quicktour.mdx

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# Quick tour

[[open-in-colab]]

Get up and running with 🤗 Transformers! Start using the [`pipeline`] for rapid inference, and quickly load a pretrained model and tokenizer with an [AutoClass](./model_doc/auto) to solve your text, vision or audio task.

<Tip>

All code examples presented in the documentation have a toggle on the top left for PyTorch and TensorFlow. If
not, the code is expected to work for both backends without any change.

</Tip>

## Pipeline

[`pipeline`] is the easiest way to use a pretrained model for a given task.

<Youtube id="tiZFewofSLM"/>

The [`pipeline`] supports many common tasks out-of-the-box:

**Text**:
* Sentiment analysis: classify the polarity of a given text.
* Text generation (in English): generate text from a given input.
* Name entity recognition (NER): label each word with the entity it represents (person, date, location, etc.).
* Question answering: extract the answer from the context, given some context and a question.
* Fill-mask: fill in the blank given a text with masked words.
* Summarization: generate a summary of a long sequence of text or document.
* Translation: translate text into another language.
* Feature extraction: create a tensor representation of the text.

**Image**:
* Image classification: classify an image.
* Image segmentation: classify every pixel in an image.
* Object detection: detect objects within an image.

**Audio**:
* Audio classification: assign a label to a given segment of audio.
* Automatic speech recognition (ASR): transcribe audio data into text.

<Tip>

For more details about the [`pipeline`] and associated tasks, refer to the documentation [here](./main_classes/pipelines).

</Tip>

### Pipeline usage

In the following example, you will use the [`pipeline`] for sentiment analysis.

Install the following dependencies if you haven't already:

<frameworkcontent>
<pt>
```bash
pip install torch
```
</pt>
<tf>
```bash
pip install tensorflow
```
</tf>
</frameworkcontent>

Import [`pipeline`] and specify the task you want to complete:

```py
>>> from transformers import pipeline

>>> classifier = pipeline("sentiment-analysis")
```

The pipeline downloads and caches a default [pretrained model](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) and tokenizer for sentiment analysis. Now you can use the `classifier` on your target text:

```py
>>> classifier("We are very happy to show you the 🤗 Transformers library.")
[{'label': 'POSITIVE', 'score': 0.9998}]
```

For more than one sentence, pass a list of sentences to the [`pipeline`] which returns a list of dictionaries:

```py
>>> results = classifier(["We are very happy to show you the 🤗 Transformers library.", "We hope you don't hate it."])
>>> for result in results:
...     print(f"label: {result['label']}, with score: {round(result['score'], 4)}")
label: POSITIVE, with score: 0.9998
label: NEGATIVE, with score: 0.5309
```

The [`pipeline`] can also iterate over an entire dataset. Start by installing the [🤗 Datasets](https://huggingface.co/docs/datasets/) library:

```bash
pip install datasets 
```

Create a [`pipeline`] with the task you want to solve for and the model you want to use.

```py
>>> import torch
>>> from transformers import pipeline

>>> speech_recognizer = pipeline("automatic-speech-recognition", model="facebook/wav2vec2-base-960h")
```

Next, load a dataset (see the 🤗 Datasets [Quick Start](https://huggingface.co/docs/datasets/quickstart.html) for more details) you'd like to iterate over. For example, let's load the [MInDS-14](https://huggingface.co/datasets/PolyAI/minds14) dataset:

```py
>>> from datasets import load_dataset, Audio

>>> dataset = load_dataset("PolyAI/minds14", name="en-US", split="train")  # doctest: +IGNORE_RESULT
```

We need to make sure that the sampling rate of the dataset matches the sampling 
rate `facebook/wav2vec2-base-960h` was trained on.

```py
>>> dataset = dataset.cast_column("audio", Audio(sampling_rate=speech_recognizer.feature_extractor.sampling_rate))
```

Audio files are automatically loaded and resampled when calling the `"audio"` column.
Let's extract the raw waveform arrays of the first 4 samples and pass it as a list to the pipeline:

```py
>>> result = speech_recognizer(dataset[:4]["audio"])
>>> print([d["text"] for d in result])
['I WOULD LIKE TO SET UP A JOINT ACCOUNT WITH MY PARTNER HOW DO I PROCEED WITH DOING THAT', "FONDERING HOW I'D SET UP A JOIN TO HET WITH MY WIFE AND WHERE THE AP MIGHT BE", "I I'D LIKE TOY SET UP A JOINT ACCOUNT WITH MY PARTNER I'M NOT SEEING THE OPTION TO DO IT ON THE APSO I CALLED IN TO GET SOME HELP CAN I JUST DO IT OVER THE PHONE WITH YOU AND GIVE YOU THE INFORMATION OR SHOULD I DO IT IN THE AP AND I'M MISSING SOMETHING UQUETTE HAD PREFERRED TO JUST DO IT OVER THE PHONE OF POSSIBLE THINGS", 'HOW DO I TURN A JOIN A COUNT']
```

For a larger dataset where the inputs are big (like in speech or vision), you will want to pass along a generator instead of a list that loads all the inputs in memory. See the [pipeline documentation](./main_classes/pipelines) for more information.

### Use another model and tokenizer in the pipeline

The [`pipeline`] can accommodate any model from the [Model Hub](https://huggingface.co/models), making it easy to adapt the [`pipeline`] for other use-cases. For example, if you'd like a model capable of handling French text, use the tags on the Model Hub to filter for an appropriate model. The top filtered result returns a multilingual [BERT model](https://huggingface.co/nlptown/bert-base-multilingual-uncased-sentiment) fine-tuned for sentiment analysis. Great, let's use this model!

```py
>>> model_name = "nlptown/bert-base-multilingual-uncased-sentiment"
```

<frameworkcontent>
<pt>
Use the [`AutoModelForSequenceClassification`] and [`AutoTokenizer`] to load the pretrained model and it's associated tokenizer (more on an `AutoClass` below):

```py
>>> from transformers import AutoTokenizer, AutoModelForSequenceClassification

>>> model = AutoModelForSequenceClassification.from_pretrained(model_name)
>>> tokenizer = AutoTokenizer.from_pretrained(model_name)
```
</pt>
<tf>
Use the [`TFAutoModelForSequenceClassification`] and [`AutoTokenizer`] to load the pretrained model and it's associated tokenizer (more on an `TFAutoClass` below):

```py
>>> from transformers import AutoTokenizer, TFAutoModelForSequenceClassification

>>> model = TFAutoModelForSequenceClassification.from_pretrained(model_name)
>>> tokenizer = AutoTokenizer.from_pretrained(model_name)
```
</tf>
</frameworkcontent>

Then you can specify the model and tokenizer in the [`pipeline`], and apply the `classifier` on your target text:

```py
>>> classifier = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
>>> classifier("Nous sommes très heureux de vous présenter la bibliothèque 🤗 Transformers.")
[{'label': '5 stars', 'score': 0.7273}]
```

If you can't find a model for your use-case, you will need to fine-tune a pretrained model on your data. Take a look at our [fine-tuning tutorial](./training) to learn how. Finally, after you've fine-tuned your pretrained model, please consider sharing it (see tutorial [here](./model_sharing)) with the community on the Model Hub to democratize NLP for everyone! 🤗

## AutoClass

<Youtube id="AhChOFRegn4"/>

Under the hood, the [`AutoModelForSequenceClassification`] and [`AutoTokenizer`] classes work together to power the [`pipeline`]. An [AutoClass](./model_doc/auto) is a shortcut that automatically retrieves the architecture of a pretrained model from it's name or path. You only need to select the appropriate `AutoClass` for your task and it's associated tokenizer with [`AutoTokenizer`]. 

Let's return to our example and see how you can use the `AutoClass` to replicate the results of the [`pipeline`].

### AutoTokenizer

A tokenizer is responsible for preprocessing text into a format that is understandable to the model. First, the tokenizer will split the text into words called *tokens*. There are multiple rules that govern the tokenization process, including how to split a word and at what level (learn more about tokenization [here](./tokenizer_summary)). The most important thing to remember though is you need to instantiate the tokenizer with the same model name to ensure you're using the same tokenization rules a model was pretrained with.

Load a tokenizer with [`AutoTokenizer`]:

```py
>>> from transformers import AutoTokenizer

>>> model_name = "nlptown/bert-base-multilingual-uncased-sentiment"
>>> tokenizer = AutoTokenizer.from_pretrained(model_name)
```

Next, the tokenizer converts the tokens into numbers in order to construct a tensor as input to the model. This is known as the model's *vocabulary*.

Pass your text to the tokenizer:

```py
>>> encoding = tokenizer("We are very happy to show you the 🤗 Transformers library.")
>>> print(encoding)
{'input_ids': [101, 11312, 10320, 12495, 19308, 10114, 11391, 10855, 10103, 100, 58263, 13299, 119, 102],
 'token_type_ids': [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]}
```

The tokenizer will return a dictionary containing:

* [input_ids](./glossary#input-ids): numerical representions of your tokens.
* [atttention_mask](.glossary#attention-mask): indicates which tokens should be attended to.

Just like the [`pipeline`], the tokenizer will accept a list of inputs. In addition, the tokenizer can also pad and truncate the text to return a batch with uniform length:

<frameworkcontent>
<pt>
```py
>>> pt_batch = tokenizer(
...     ["We are very happy to show you the 🤗 Transformers library.", "We hope you don't hate it."],
...     padding=True,
...     truncation=True,
...     max_length=512,
...     return_tensors="pt",
... )
```
</pt>
<tf>
```py
>>> tf_batch = tokenizer(
...     ["We are very happy to show you the 🤗 Transformers library.", "We hope you don't hate it."],
...     padding=True,
...     truncation=True,
...     max_length=512,
...     return_tensors="tf",
... )
```
</tf>
</frameworkcontent>

Read the [preprocessing](./preprocessing) tutorial for more details about tokenization.

### AutoModel

<frameworkcontent>
<pt>
🤗 Transformers provides a simple and unified way to load pretrained instances. This means you can load an [`AutoModel`] like you would load an [`AutoTokenizer`]. The only difference is selecting the correct [`AutoModel`] for the task. Since you are doing text - or sequence - classification, load [`AutoModelForSequenceClassification`]:

```py
>>> from transformers import AutoModelForSequenceClassification

>>> model_name = "nlptown/bert-base-multilingual-uncased-sentiment"
>>> pt_model = AutoModelForSequenceClassification.from_pretrained(model_name)
```

<Tip>

See the [task summary](./task_summary) for which [`AutoModel`] class to use for which task.

</Tip>

Now you can pass your preprocessed batch of inputs directly to the model. You just have to unpack the dictionary by adding `**`:

```py
>>> pt_outputs = pt_model(**pt_batch)
```

The model outputs the final activations in the `logits` attribute. Apply the softmax function to the `logits` to retrieve the probabilities:

```py
>>> from torch import nn

>>> pt_predictions = nn.functional.softmax(pt_outputs.logits, dim=-1)
>>> print(pt_predictions)
tensor([[0.0021, 0.0018, 0.0115, 0.2121, 0.7725],
        [0.2084, 0.1826, 0.1969, 0.1755, 0.2365]], grad_fn=<SoftmaxBackward0>)
```
</pt>
<tf>
🤗 Transformers provides a simple and unified way to load pretrained instances. This means you can load an [`TFAutoModel`] like you would load an [`AutoTokenizer`]. The only difference is selecting the correct [`TFAutoModel`] for the task. Since you are doing text - or sequence - classification, load [`TFAutoModelForSequenceClassification`]:

```py
>>> from transformers import TFAutoModelForSequenceClassification

>>> model_name = "nlptown/bert-base-multilingual-uncased-sentiment"
>>> tf_model = TFAutoModelForSequenceClassification.from_pretrained(model_name)
```

<Tip>

See the [task summary](./task_summary) for which [`AutoModel`] class to use for which task.

</Tip>

Now you can pass your preprocessed batch of inputs directly to the model by passing the dictionary keys directly to the tensors:

```py
>>> tf_outputs = tf_model(tf_batch)
```

The model outputs the final activations in the `logits` attribute. Apply the softmax function to the `logits` to retrieve the probabilities:

```py
>>> import tensorflow as tf

>>> tf_predictions = tf.nn.softmax(tf_outputs.logits, axis=-1)
>>> tf_predictions  # doctest: +IGNORE_RESULT
```
</tf>
</frameworkcontent>

<Tip>

All 🤗 Transformers models (PyTorch or TensorFlow) outputs the tensors *before* the final activation
function (like softmax) because the final activation function is often fused with the loss.

</Tip>

Models are a standard [`torch.nn.Module`](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) or a [`tf.keras.Model`](https://www.tensorflow.org/api_docs/python/tf/keras/Model) so you can use them in your usual training loop. However, to make things easier, 🤗 Transformers provides a [`Trainer`] class for PyTorch that adds functionality for distributed training, mixed precision, and more. For TensorFlow, you can use the `fit` method from [Keras](https://keras.io/). Refer to the [training tutorial](./training) for more details.

<Tip>

🤗 Transformers model outputs are special dataclasses so their attributes are autocompleted in an IDE.
The model outputs also behave like a tuple or a dictionary (e.g., you can index with an integer, a slice or a string) in which case the attributes that are `None` are ignored.

</Tip>

### Save a model

<frameworkcontent>
<pt>
Once your model is fine-tuned, you can save it with its tokenizer using [`PreTrainedModel.save_pretrained`]:

```py
>>> pt_save_directory = "./pt_save_pretrained"
>>> tokenizer.save_pretrained(pt_save_directory)  # doctest: +IGNORE_RESULT
>>> pt_model.save_pretrained(pt_save_directory)
```

When you are ready to use the model again, reload it with [`PreTrainedModel.from_pretrained`]:

```py
>>> pt_model = AutoModelForSequenceClassification.from_pretrained("./pt_save_pretrained")
```
</pt>
<tf>
Once your model is fine-tuned, you can save it with its tokenizer using [`TFPreTrainedModel.save_pretrained`]:

```py
>>> tf_save_directory = "./tf_save_pretrained"
>>> tokenizer.save_pretrained(tf_save_directory)  # doctest: +IGNORE_RESULT
>>> tf_model.save_pretrained(tf_save_directory)
```

When you are ready to use the model again, reload it with [`TFPreTrainedModel.from_pretrained`]:

```py
>>> tf_model = TFAutoModelForSequenceClassification.from_pretrained("./tf_save_pretrained")
```
</tf>
</frameworkcontent>

One particularly cool 🤗 Transformers feature is the ability to save a model and reload it as either a PyTorch or TensorFlow model. The `from_pt` or `from_tf` parameter can convert the model from one framework to the other:

<frameworkcontent>
<pt>
```py
>>> from transformers import AutoModel

>>> tokenizer = AutoTokenizer.from_pretrained(tf_save_directory)
>>> pt_model = AutoModelForSequenceClassification.from_pretrained(tf_save_directory, from_tf=True)
```
</pt>
<tf>
```py
>>> from transformers import TFAutoModel

>>> tokenizer = AutoTokenizer.from_pretrained(pt_save_directory)
>>> tf_model = TFAutoModelForSequenceClassification.from_pretrained(pt_save_directory, from_pt=True)
```
</tf>
</frameworkcontent>

## Custom model builds

You can modify the model's configuration class to change how a model is built. The configuration specifies a model's attributes, such as the number of hidden layers or attention heads. You start from scratch when you initialize a model from a custom configuration class. The model attributes are randomly initialized, and you'll need to train the model before you can use it to get meaningful results.

Start by importing [`AutoConfig`], and then load the pretrained model you want to modify. Within [`AutoConfig.from_pretrained`], you can specify the attribute you want to change, such as the number of attention heads:

```py
>>> from transformers import AutoConfig

>>> my_config = AutoConfig.from_pretrained("distilbert-base-uncased", n_heads=12)
```

<frameworkcontent>
<pt>
Create a model from your custom configuration with [`AutoModel.from_config`]:

```py
>>> from transformers import AutoModel

>>> my_model = AutoModel.from_config(my_config)
```
</pt>
<tf>
Create a model from your custom configuration with [`TFAutoModel.from_config`]:

```py
>>> from transformers import TFAutoModel

>>> my_model = TFAutoModel.from_config(my_config)
```
</tf>
</frameworkcontent>

Take a look at the [Create a custom architecture](./create_a_model) guide for more information about building custom configurations.

## What's next?

Now that you've completed the 🤗 Transformers quick tour, check out our guides and learn how to do more specific things like writing a custom model, fine-tuning a model for a task, and how to train a model with a script. If you're interested in learning more about 🤗 Transformers core concepts, grab a cup of coffee and take a look at our Conceptual Guides!