<!--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 agreed to in writing, software distributed under the License is distributed on
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.
-->

# 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 [SUPERB](https://huggingface.co/datasets/superb) dataset:

```py
>>> import datasets

>>> dataset = datasets.load_dataset("superb", name="asr", split="test")  # doctest: +IGNORE_RESULT
```

You can pass a whole dataset pipeline:

```py
>>> files = dataset["file"]
>>> speech_recognizer(files[:4])
[{'text': 'HE HOPED THERE WOULD BE STEW FOR DINNER TURNIPS AND CARROTS AND BRUISED POTATOES AND FAT MUTTON PIECES TO BE LADLED OUT IN THICK PEPPERED FLOWER FAT AND SAUCE'},
 {'text': 'STUFFERED INTO YOU HIS BELLY COUNSELLED HIM'},
 {'text': 'AFTER EARLY NIGHTFALL THE YELLOW LAMPS WOULD LIGHT UP HERE AND THERE THE SQUALID QUARTER OF THE BROTHELS'},
 {'text': 'HO BERTIE ANY GOOD IN YOUR MIND'}]
```

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)
>>> print(tf.math.round(tf_predictions * 10**4) / 10**4)
tf.Tensor(
[[0.0021 0.0018 0.0116 0.2121 0.7725]
 [0.2084 0.1826 0.1969 0.1755  0.2365]], shape=(2, 5), dtype=float32)
```
</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>
