Usage

🚀 Basic Use Case

After the installation of the GLiClass library, import the GLiClassModel class. Following this, you can load your chosen model with GLiClassModel.from_pretrained and utilize ZeroShotClassificationPipeline to classify your texts.

import torch
from gliclass import GLiClassModel, ZeroShotClassificationPipeline
from transformers import AutoTokenizer
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Initialize GLiClass model and tokenizer
model = GLiClassModel.from_pretrained("knowledgator/gliclass-small-v1.0")
tokenizer = AutoTokenizer.from_pretrained("knowledgator/gliclass-small-v1.0")

# Initialize Classification pipeline
pipeline = ZeroShotClassificationPipeline(
    model, tokenizer, classification_type='multi-label', device=device
)

# Sample text for classification
text = """
One day I will see the world!
"""

# Labels for classification
labels = ["travel", "dreams", "sport", "science", "politics"]

# Perform classification
results = pipeline(text, labels, threshold=0.5)[0]

# Display predicted classes and their scores
for result in results:
    print(f"{result['label']} => {result['score']:.3f}")

Expected Output

travel => 1.000
dreams => 1.000
sport => 1.000
science => 1.000
politics => 0.817

Retrieval-Augmented Classification (RAC)

With some models trained with retrieval-agumented classification, such as knowledgator/gliclass-base-v2.0-rac-init you can specify examples to improve classification accuracy:

import torch
from gliclass import GLiClassModel, ZeroShotClassificationPipeline
from transformers import AutoTokenizer
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

model = GLiClassModel.from_pretrained("knowledgator/gliclass-small-v1.0")
tokenizer = AutoTokenizer.from_pretrained("knowledgator/gliclass-small-v1.0")

pipeline = ZeroShotClassificationPipeline(
    model, tokenizer, classification_type='multi-label', device=device
)

# Add an example for model to improve performance
example = {
    "text": "A new machine learning platform automates complex data workflows but faces integration issues.",
    "all_labels": ["AI", "automation", "data_analysis", "usability", "integration"],
    "true_labels": ["AI", "integration", "automation"]
}

text = "The new AI-powered tool streamlines data analysis but has limited integration capabilities."
labels = ["AI", "automation", "data_analysis", "usability", "integration"]

results = pipeline(text, labels, threshold=0.1, rac_examples=[example])[0]

for predict in results:
    print(f"{predict['label']} => {predict['score']:.3f}")

Expected Output

AI => 1.000
automation => 1.000
data_analysis => 1.000
usability => 1.000
integration => 1.000

🛠️Pipelines

• Sentiment Analysis: Rapidly classify texts as positive, negative, or neutral.
• Document Classification: Efficiently organize and categorize large document collections.
• Search Results Re-ranking: Improve relevance and precision by reranking search outputs.
• News Categorization: Automatically tag and organize news articles into predefined categories.
• Fact Checking: Quickly validate and categorize statements based on factual accuracy.

We have prepared high-level classes that simplify working with GLiClass

Zero-Shot Classification

The ZeroShotClassificationPipeline is a pipeline for text classification tasks based on the GLiClass model. It evaluates input text against a set of predefined labels, supporting both single-label and multi-label classification.

1. Initialize the Pipeline
   Load a pretrained model and initialize the ZeroShotClassificationPipeline.

   import torch
   from gliclass import GLiClassModel, ZeroShotClassificationPipeline
   from transformers import AutoTokenizer
   device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
   
   model = GLiClassModel.from_pretrained("knowledgator/gliclass-small-v1.0")
   tokenizer = AutoTokenizer.from_pretrained("knowledgator/gliclass-small-v1.0")
   
   pipeline = ZeroShotClassificationPipeline(
       model, # pretrained model
       tokenizer, # pretrained tokenizer
       classification_type='single-label', # problem type 
       # use `multi-label` for multi-label classification
       device=device
   )

2. Classify a Text

   text = """
   Apple Inc. is an American multinational technology company headquartered in Cupertino, California. Apple is the world's largest technology company by revenue, with US$394.3 billion in 2022 revenue. As of March 2023, Apple is the world's biggest company by market capitalization. As of June 2022, Apple is the fourth-largest personal computer vendor by unit sales and the second-largest mobile phone manufacturer in the world. It is considered one of the Big Five American information technology companies, alongside Alphabet (parent company of Google), Amazon, Meta Platforms, and Microsoft. 
   Microsoft was founded by Bill Gates and Paul Allen on April 4, 1975 to develop and sell BASIC interpreters for the Altair 8800. During his career at Microsoft, Gates held the positions of chairman, chief executive officer, president and chief software architect, while also being the largest individual shareholder until May 2014.
   Apple was founded as Apple Computer Company on April 1, 1976, by Steve Wozniak, Steve Jobs (1955–2011) and Ronald Wayne to develop and sell Wozniak's Apple I personal computer. It was incorporated by Jobs and Wozniak as Apple Computer, Inc. in 1977. The company's second computer, the Apple II, became a best seller and one of the first mass-produced microcomputers. Apple went public in 1980 to instant financial success. The company developed computers featuring innovative graphical user interfaces, including the 1984 original Macintosh, announced that year in a critically acclaimed advertisement called "1984". By 1985, the high cost of its products, and power struggles between executives, caused problems. Wozniak stepped back from Apple and pursued other ventures, while Jobs resigned and founded NeXT, taking some Apple employees with him. 
   """
   labels = ["business", "computers", "sport", "politics", "science"]
   results = pipeline(
       text,
       labels,
       threshold=0.5,
       batch_size=8
   )[0] # Select 1st as we only have one text
   
   for result in results:
       print(f"{result['label']} => {result['score']:.3f}")
   Expected Output

   business => 0.519

Zero-Shot Classification with labels chunking

The ZeroShotClassificationWithLabelsChunkingPipeline is a pipeline for text classification that works almost identically to the ZeroShotClassificationPipeline, except that it pre-breaks the labels' list into chunks of a specified size.

tip

Use this pipeline if you want to classify a large number of labels (more than 25 per text)

1. Initialize the Pipeline

   from gliclass import ZeroShotClassificationWithLabelsChunkingPipeline
   
   pipeline = ZeroShotClassificationWithLabelsChunkingPipeline(
       model, # pretrained model
       tokenizer, # pretrained tokenizer
       classification_type='multi-labe', # problem type 
       # use `single-label` for single-label classification
       device=device
   )
   

2. Classify a Text

   text = """
   Apple Inc. is an American multinational technology company headquartered in Cupertino, California. Apple is the world's largest technology company by revenue, with US$394.3 billion in 2022 revenue. As of March 2023, Apple is the world's biggest company by market capitalization. As of June 2022, Apple is the fourth-largest personal computer vendor by unit sales and the second-largest mobile phone manufacturer in the world. It is considered one of the Big Five American information technology companies, alongside Alphabet (parent company of Google), Amazon, Meta Platforms, and Microsoft. 
   Microsoft was founded by Bill Gates and Paul Allen on April 4, 1975 to develop and sell BASIC interpreters for the Altair 8800. During his career at Microsoft, Gates held the positions of chairman, chief executive officer, president and chief software architect, while also being the largest individual shareholder until May 2014.
   Apple was founded as Apple Computer Company on April 1, 1976, by Steve Wozniak, Steve Jobs (1955–2011) and Ronald Wayne to develop and sell Wozniak's Apple I personal computer. It was incorporated by Jobs and Wozniak as Apple Computer, Inc. in 1977. The company's second computer, the Apple II, became a best seller and one of the first mass-produced microcomputers. Apple went public in 1980 to instant financial success. The company developed computers featuring innovative graphical user interfaces, including the 1984 original Macintosh, announced that year in a critically acclaimed advertisement called "1984". By 1985, the high cost of its products, and power struggles between executives, caused problems. Wozniak stepped back from Apple and pursued other ventures, while Jobs resigned and founded NeXT, taking some Apple employees with him. 
   """
   
   labels = [
   "business", "computers", "science", "innovation", "success", "leadership", "growth", "achievement", "entrepreneurship", "breakthrough", "market leader", "revolutionary", "profitable", "setbacks", "disputes", "crisis", "conflict", "problems", "struggles", "resignation", "high cost", "power struggles", "departure", "difficulties", "expensive", "competition", "rivalry", "challenges"
   ]
   
   results = pipeline(
       text,
       labels,
       threshold=0.85,
       labels_chunk_size = 15 # specify chunk size for your labels
   )[0]
   
   for result in results:
       print(f"{result['label']} => {result['score']:.3f}")
   
   Expected Output

   business => 1.000
   innovation => 1.000
   growth => 1.000
   entrepreneurship => 1.000
   leadership => 1.000
   success => 1.000
   achievement => 1.000
   breakthrough => 1.000
   computers => 1.000
   profitable => 1.000
   science => 1.000
   revolutionary => 1.000
   market leader => 1.000
   challenges => 1.000
   resignation => 1.000
   departure => 1.000
   expensive => 1.000
   high cost => 1.000
   difficulties => 0.999
   problems => 0.999
   setbacks => 0.998
   competition => 0.996
   rivalry => 0.995
   crisis => 0.992
   struggles => 0.991
   conflict => 0.987
   disputes => 0.949
   power struggles => 0.899