The pipeline() makes it simple to use any model from the Hub for inference on any language, computer vision, speech, and multimodal tasks. Even if you don’t have experience with a specific modality or aren’t familiar with the underlying code behind the models, you can still use them for inference with the pipeline()! This tutorial will teach you to:
Take a look at the pipeline() documentation for a complete list of supported tasks and available parameters.
While each task has an associated pipeline(), it is simpler to use the general pipeline() abstraction which contains all the task-specific pipelines. The pipeline() automatically loads a default model and a preprocessing class capable of inference for your task.
>>> from transformers import pipeline
>>> generator = pipeline(task="text-generation")>>> generator(
... "Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone"
... ) # doctest: +SKIP
[{'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",
... ]
... ) # doctest: +SKIPAny 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,
... ) # doctest: +SKIPThe pipeline() accepts any model from the Hub. There are tags on the 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"
... ) # doctest: +SKIP
[{'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'}]The pipeline() also supports audio tasks like audio classification and automatic speech recognition.
For example, let’s classify the emotion in this audio clip:
>>> from datasets import load_dataset
>>> import torch
>>> torch.manual_seed(42)
>>> ds = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")
>>> audio_file = ds[0]["audio"]["path"]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():
>>> preds = audio_classifier(audio_file)
>>> preds = [{"score": round(pred["score"], 4), "label": pred["label"]} for pred in preds]
>>> preds
[{'score': 0.1315, 'label': 'calm'}, {'score': 0.1307, 'label': 'neutral'}, {'score': 0.1274, 'label': 'sad'}, {'score': 0.1261, 'label': 'fearful'}, {'score': 0.1242, 'label': 'happy'}]Using a pipeline() for vision tasks is practically identical.
Specify your task and pass your image to the classifier. The image 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")
>>> preds = vision_classifier(
... images="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
... )
>>> preds = [{"score": round(pred["score"], 4), "label": pred["label"]} for pred in preds]
>>> preds
[{'score': 0.4335, 'label': 'lynx, catamount'}, {'score': 0.0348, 'label': 'cougar, puma, catamount, mountain lion, painter, panther, Felis concolor'}, {'score': 0.0324, 'label': 'snow leopard, ounce, Panthera uncia'}, {'score': 0.0239, 'label': 'Egyptian cat'}, {'score': 0.0229, 'label': 'tiger cat'}]The pipeline() supports more than one modality. For example, a visual question answering (VQA) task combines text and image. Feel free to use any image link you like and a question you want to ask about the image. The image can be a URL or a local path to the image.
For example, if you use the same image from the vision pipeline above:
>>> image = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
>>> question = "Where is the cat?"Create a pipeline for vqa and pass it the image and question:
>>> from transformers import pipeline
>>> vqa = pipeline(task="vqa")
>>> preds = vqa(image=image, question=question)
>>> preds = [{"score": round(pred["score"], 4), "answer": pred["answer"]} for pred in preds]
>>> preds
[{'score': 0.911, 'answer': 'snow'}, {'score': 0.8786, 'answer': 'in snow'}, {'score': 0.6714, 'answer': 'outside'}, {'score': 0.0293, 'answer': 'on ground'}, {'score': 0.0272, 'answer': 'ground'}]