Text Classification
Transformers
TensorFlow
bert
generated_from_keras_callback
text-embeddings-inference
Instructions to use ViditRaj/Simple_BERT_Ads_Classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ViditRaj/Simple_BERT_Ads_Classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ViditRaj/Simple_BERT_Ads_Classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ViditRaj/Simple_BERT_Ads_Classifier") model = AutoModelForSequenceClassification.from_pretrained("ViditRaj/Simple_BERT_Ads_Classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from ViditRaj/Simple_BERT_Ads_Classifier: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/ViditRaj/Simple_BERT_Ads_Classifier/resolve/refs%2Fpr%2F1/tf_model.h5
- Command line
-
hf download hf://ViditRaj/Simple_BERT_Ads_Classifier@refs/pr/1/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/ViditRaj/Simple_BERT_Ads_Classifier/resolve/refs%2Fpr%2F1/tf_model.h5
438 MB
- Xet hash:
- aa6b2d38c22136af816380ec9ea3ae7726f76d5a236b9f9345caf3b5ba3d4e74
- Size of remote file:
- 438 MB
- SHA256:
- 278590e08a411ed7c55e7123262450c2a2a012c86587306895f045e7b6cc64d9
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