Pipelines for inference
The pipeline() makes it simple to use any model from the Model Hub for inference on a variety of tasks such as text generation, image segmentation and audio classification. Even if you don’t have experience with a specific modality or understand the code powering the models, you can still use them with the pipeline()! This tutorial will teach you to:
- Use a pipeline() for inference.
- Use a specific tokenizer or model.
- Use a pipeline() for audio and vision tasks.
Take a look at the pipeline() documentation for a complete list of supported taska.
Pipeline usage
While each task has an associated pipeline(), it is simpler to use the general pipeline() abstraction which contains all the specific task pipelines. The pipeline() automatically loads a default model and tokenizer capable of inference for your task.
- Start by creating a pipeline() and specify an inference task:
>>> from transformers import pipeline
>>> generator = pipeline(task="text-generation")- Pass your input text to the pipeline():
>>> generator("Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone")
[{'generated_text': 'Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone, Seven for the Iron-priests at the door to the east, and thirteen for the Lord Kings at the end of the mountain'}]If you have more than one input, pass your input as a list:
>>> generator(
... [
... "Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone",
... "Nine for Mortal Men, doomed to die, One for the Dark Lord on his dark throne",
... ]
... )Any additional parameters for your task can also be included in the pipeline(). The text-generation task has a generate() method with several parameters for controlling the output. For example, if you want to generate more than one output, set the num_return_sequences parameter:
>>> generator(
... "Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone",
... num_return_sequences=2,
... )Choose a model and tokenizer
The pipeline() accepts any model from the Model Hub. There are tags on the Model Hub that allow you to filter for a model you’d like to use for your task. Once you’ve picked an appropriate model, load it with the corresponding AutoModelFor and [`AutoTokenizer’] class. For example, load the AutoModelForCausalLM class for a causal language modeling task:
>>> from transformers import AutoTokenizer, AutoModelForCausalLM
>>> tokenizer = AutoTokenizer.from_pretrained("distilgpt2")
>>> model = AutoModelForCausalLM.from_pretrained("distilgpt2")Create a pipeline() for your task, and specify the model and tokenizer you’ve loaded:
>>> from transformers import pipeline
>>> generator = pipeline(task="text-generation", model=model, tokenizer=tokenizer)Pass your input text to the pipeline() to generate some text:
>>> generator("Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone")
[{'generated_text': 'Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone, Seven for the Dragon-lords (for them to rule in a world ruled by their rulers, and all who live within the realm'}]Audio pipeline
The flexibility of the pipeline() means it can also be extended to audio tasks.
For example, let’s classify the emotion from a short clip of John F. Kennedy’s famous “We choose to go to the Moon” speech. Find an audio classification model on the Model Hub for emotion recognition and load it in the pipeline():
>>> from transformers import pipeline
>>> audio_classifier = pipeline(
... task="audio-classification", model="ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition"
... )Pass the audio file to the pipeline():
>>> audio_classifier("jfk_moon_speech.wav")
[{'label': 'calm', 'score': 0.13856211304664612},
{'label': 'disgust', 'score': 0.13148026168346405},
{'label': 'happy', 'score': 0.12635163962841034},
{'label': 'angry', 'score': 0.12439591437578201},
{'label': 'fearful', 'score': 0.12404385954141617}]Vision pipeline
Finally, using a pipeline() for vision tasks is practically identical.
Specify your vision task and pass your image to the classifier. The imaage can be a link or a local path to the image. For example, what species of cat is shown below?
>>> from transformers import pipeline
>>> vision_classifier = pipeline(task="image-classification")
>>> vision_classifier(
... images="https://huggingface.co./datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
... )
[{'label': 'lynx, catamount', 'score': 0.4403027892112732},
{'label': 'cougar, puma, catamount, mountain lion, painter, panther, Felis concolor',
'score': 0.03433405980467796},
{'label': 'snow leopard, ounce, Panthera uncia',
'score': 0.032148055732250214},
{'label': 'Egyptian cat', 'score': 0.02353910356760025},
{'label': 'tiger cat', 'score': 0.023034192621707916}]