Text Classification
Transformers
TensorFlow
roberta
generated_from_keras_callback
text-embeddings-inference
Instructions to use ViditRaj/ROBERTA_Ads_Classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ViditRaj/ROBERTA_Ads_Classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ViditRaj/ROBERTA_Ads_Classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ViditRaj/ROBERTA_Ads_Classifier") model = AutoModelForSequenceClassification.from_pretrained("ViditRaj/ROBERTA_Ads_Classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from ViditRaj/ROBERTA_Ads_Classifier: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/ViditRaj/ROBERTA_Ads_Classifier/resolve/main/tf_model.h5
- Command line
-
hf download hf://ViditRaj/ROBERTA_Ads_Classifier/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/ViditRaj/ROBERTA_Ads_Classifier/resolve/main/tf_model.h5
499 MB
- Xet hash:
- 8143b1cf99fefc6ebe702007cdbe6a4a49c2ae762660f97a7f3791fde2e57e83
- Size of remote file:
- 499 MB
- SHA256:
- 103e00130379038d6d4dcc8db674b999c43deede5081870d77ae89d3c3a82742
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