Using Pre-trained model from HuggingFace
Overview
Teaching: 20 min
Exercises: 0 minQuestions
How to use pre-trained model already available from Hugging Face hub
Objectives
To master the usage of pre-trained deep learning model from Hugging Face
Hugging Face hub
- Hugging Face hub is considered to be Github of Machine Learning
- It is a platform with over 120k models, 20k datasets and 50k demo apps, All open source and publicly available
- All in one platform where people can easily deploy, collaborate and build ML model
Transformers library
- The Transformers library, developed by Hugging Face, has played a significant role in making state-of-the-art NLP models more accessible to researchers and developers.
- It includes pre-trained models like BERT, GPT, RoBERTa, and more, which can be fine-tuned for specific tasks such as text classification, language generation, question answering, and more.
- The library offers a consistent API for various NLP tasks, making it easier for practitioners to experiment with and deploy these models.
Model task
The screenshot below describes the model task from Hugging Face that covers many different aspecs from Computer Vision to NLP, Audio or Reinforcement Learning
Pipeline for inference
- 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()!
Pipeline for NLP Sentiment Analysis
from transformers import pipeline
classifier = pipeline("sentiment-analysis")
classifier("I am so excited to use the new SuperPOD from NVIDIA")
[{'label': 'POSITIVE', 'score': 0.9995261430740356}]
classifier(
["I am so excited to use the new SuperPOD from NVIDIA", "I hate running late"])
[{'label': 'POSITIVE', 'score': 0.9995261430740356},
{'label': 'NEGATIVE', 'score': 0.9943193197250366}]
Pipeline Text Generation
from transformers import pipeline
generator = pipeline("text-generation")
generator("Using SMU latest HPC cluster NVIDIA SuperPOD, you will be able to")
[{'generated_text': 'Using SMU latest HPC cluster NVIDIA SuperPOD, you will be able to connect to other SSE nodes such as the following and use them as a HPC node:\n\n[CPU: CPU1, GIGABYTE'}]
Pipeline for Mask filling
from transformers import pipeline
unmasker = pipeline("fill-mask")
unmasker("This course will teach you all about <mask> models.", top_k=2)
[{'score': 0.19619698822498322,
'token': 30412,
'token_str': ' mathematical',
'sequence': 'This course will teach you all about mathematical models.'},
{'score': 0.04052705690264702,
'token': 38163,
'token_str': ' computational',
'sequence': 'This course will teach you all about computational models.'}]
Pipeline for Name Entity Recognition
from transformers import pipeline
ner = pipeline("ner", grouped_entities=True)
ner("My name is Tue Vu and I work at SMU in Dallas")
[{'entity_group': 'PER',
'score': 0.9868829,
'word': 'Tue Vu',
'start': 11,
'end': 17},
{'entity_group': 'ORG',
'score': 0.9965092,
'word': 'SMU',
'start': 32,
'end': 35},
{'entity_group': 'LOC',
'score': 0.9950755,
'word': 'Dallas',
'start': 39,
'end': 45}]
Pipeline for Question Answering
from transformers import pipeline
question_answerer = pipeline("question-answering")
question_answerer(
question="Where do I work?",
context="My name is Tue Vu and I work at SMU in Dallas",
)
{'score': 0.3651700019836426, 'start': 32, 'end': 35, 'answer': 'SMU'}
Pipeline for Conversational
from transformers import pipeline, Conversation
converse = pipeline("conversational")
conversation_1 = Conversation("What do you think about using HPC SuperPOD")
conversation_2 = Conversation("Do you believe in God?")
converse([conversation_1, conversation_2])
Answer:
[Conversation id: 44cf473c-29f2-4b44-be6c-15352dab13a2
user >> What do you think about using HPC SuperPOD
bot >> I think it's a good idea, but I don't think it's a good idea to use it for a lot of things. ,
Conversation id: 489d923c-f127-4847-8cde-972c77470230
user >> What do you do to optimize the Python workflow?
bot >> I believe in the power of love.]
Pipeline for Computer Vision - Image Classification
from transformers import pipeline
clf = pipeline("image-classification")
Display the image:
import urllib.request
from io import BytesIO
url = 'https://t4.ftcdn.net/jpg/02/66/72/41/360_F_266724172_Iy8gdKgMa7XmrhYYxLCxyhx6J7070Pr8.jpg'
with urllib.request.urlopen(url) as url:
img = Image.open(BytesIO(url.read()))
img
Model Inference
clf(img)
[{'score': 0.49216628074645996, 'label': 'Egyptian cat'},
{'score': 0.41306015849113464, 'label': 'tabby, tabby cat'},
{'score': 0.050162095576524734, 'label': 'tiger cat'},
{'score': 0.012556081637740135, 'label': 'lynx, catamount'},
{'score': 0.00524393143132329, 'label': 'ping-pong ball'}]
Key Points
Hugging Face, pre-trained, pipeline