README.md

---
license: apache-2.0
datasets:
- ProfessorLeVesseur/EnglishTense
language:
- en
metrics:
- accuracy
- precision
- recall
- f1
base_model:
- google-bert/bert-base-uncased
pipeline_tag: text-classification
tags:
- time
- timeframe
- tense
- past
- current
- future
- chronological
evaluation_results:
  accuracy: 1.00
  macro_precision: 1.00
  macro_recall: 1.00
  macro_f1: 1.00
  weighted_precision: 1.00
  weighted_recall: 1.00
  weighted_f1: 1.00
  class_precision:
    Future: 1.00
    Past: 1.00
    Present: 1.00
  class_recall:
    Future: 1.00
    Past: 1.00
    Present: 1.00
  class_f1:
    Future: 1.00
    Past: 1.00
    Present: 1.00
  support:
    Future: 727
    Past: 577
    Present: 694
---

## 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 📝

| Sentence                                                                 | Label   |
|--------------------------------------------------------------------------|---------|
| the fishermen had caught a variety of fish including bass and perch      | Past |
| medical professionals are researching the impact of social determinants on health | Present |
| in the future robotic surgical systems will have been empowering surgeons to perform increasingly complex procedures | Future |

## 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 ✅

| Class       | Precision | Recall | F1-Score | Support |
|-------------|-----------|--------|----------|---------|
| Future      | 1.00      | 1.00   | 1.00     | 727     |
| Past        | 1.00      | 1.00   | 1.00     | 577     |
| Present     | 1.00      | 1.00   | 1.00     | 694     |
| **Accuracy**|           |        | 1.00     | 1998    |
| **Macro Avg** | 1.00    | 1.00   | 1.00     | 1998    |
| **Weighted Avg** | 1.00 | 1.00   | 1.00     | 1998    |

### 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:

```python
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:

```python
# 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
```