Instructions to use devulapellykushal/textdetection_99 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use devulapellykushal/textdetection_99 with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://devulapellykushal/textdetection_99") - Notebooks
- Google Colab
- Kaggle
π§ Text Classification Model
This repository hosts a custom-trained text classification model saved in Keras .h5 format. The model is designed to classify textual inputs into one or more predefined categories.
π¦ Files Included
text_classification_pipeline.h5β Trained Keras model file.requirements.txtβ List of Python dependencies (TensorFlow, etc.).README.mdβ This file.
π Model Details
- Framework: TensorFlow / Keras
- File Format:
.h5 - Task: Text Classification
- Language: English
- Training: Performed locally on custom dataset
π Usage
To load and use the model:
from tensorflow.keras.models import load_model
import numpy as np
# Load the model
model = load_model("text_classification_pipeline.h5")
# Example input (after preprocessing)
# Replace with your own tokenizer/vectorizer logic
sample_input = np.array([...]) # shape must match model input
prediction = model.predict(sample_input)
print(prediction)
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