Instructions to use ViditRaj/BERT_LATEST_Ads_Classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ViditRaj/BERT_LATEST_Ads_Classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ViditRaj/BERT_LATEST_Ads_Classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ViditRaj/BERT_LATEST_Ads_Classifier") model = AutoModelForSequenceClassification.from_pretrained("ViditRaj/BERT_LATEST_Ads_Classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from ViditRaj/BERT_LATEST_Ads_Classifier: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/ViditRaj/BERT_LATEST_Ads_Classifier/resolve/main/tf_model.h5
- Command line
-
hf download hf://ViditRaj/BERT_LATEST_Ads_Classifier/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/ViditRaj/BERT_LATEST_Ads_Classifier/resolve/main/tf_model.h5
438 MB
- Xet hash:
- 12642b7856839edafd6080c5250456e5568bf39c5f4b135632de20f76a0697e8
- Size of remote file:
- 438 MB
- SHA256:
- f3c6ecbfe1818eb58990beec2afd2eb2372684ae1fe70cfdf8930a413ba76dff
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