bert-base-cased-timeframe-classifier

Model Overview ⏳🔮🔄

This model is a text classification model trained to predict the tense of English sentences: Past, Present, or Future. It is based on the bert-base-uncased architecture.

Intended Use 🔍

This model can be used in applications such as:

• Identifying if statements are discussing past needs, motivations, products, etc. ⏪
• Determining current events or situations in text. ⏺️
• Predicting future plans or intentions based on sentence structure. ⏩

Example Sentences and Labels 📝

Training Details 🏋️‍♂️

The model was fine-tuned on the ProfessorLeVesseur/EnglishTense dataset, which provides a diverse set of sentences labeled with their respective tenses. The training involved optimizing the model's weights for three epochs using a learning rate of 5e-5.

Evaluation Results 📊

The model achieves a perfect accuracy of 1.00 on the test set, with precision, recall, and F1-scores also at 1.00 for all classes. These results indicate excellent performance in classifying sentence tenses.

Classification Report ✅

Limitations ⚠️

While the model performs well on the provided dataset, it may not generalize to all types of English text, particularly those with ambiguous or complex sentence structures.

How to Use 🚀

This model can be used for text classification tasks, either for individual text inputs or for batch processing via a DataFrame. Below are examples of both use cases.

Classifying Input Text

To classify a single piece of text and retrieve the predicted label along with the confidence score, you can use the following code:

from transformers import pipeline  # Import the pipeline function from the transformers library

# Initialize a text classification pipeline using the specified model
classifier = pipeline(
    "text-classification",  # Specify the task type as text classification
    model="ProfessorLeVesseur/bert-base-cased-timeframe-classifier"  # Specify the model to use from the Hugging Face Model Hub
)
result = classifier("MTSS.ai is the future of education, call it education².")  # Classify the input text and store the result
print(result)  # Output the result

Classifying Text in a DataFrame

For batch processing, you can classify multiple text entries stored in a DataFrame. This example demonstrates how to read a CSV file and add a new column with the predicted labels:

# Import libraries
from transformers import pipeline  # Import the pipeline function from the transformers library
import pandas as pd  # Import pandas for data manipulation

# Read the CSV file
file_path = 'filename.csv'  # Define the path to the CSV file
df = pd.read_csv(file_path)  # Read the CSV file into a DataFrame

# Initialize the text classification pipeline
classifier = pipeline(
    "text-classification",  # Specify the task type as text classification
    model="ProfessorLeVesseur/bert-base-cased-timeframe-classifier"  # Specify the model to use from the Hugging Face Model Hub
)

# Apply the classifier to each row in the "Text" column and store results in a new column "label"
df['label'] = df['Text'].apply(lambda text: classifier(text)[0]['label'])  # Classify each text and store the label

# Display the DataFrame with the new "label" column
df.head(5)  # Display the first 5 rows of the DataFrame