Instructions to use udit-k/HamSpamBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use udit-k/HamSpamBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="udit-k/HamSpamBERT")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("udit-k/HamSpamBERT") model = AutoModelForSequenceClassification.from_pretrained("udit-k/HamSpamBERT") - Notebooks
- Google Colab
- Kaggle
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model-index:
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- name: HamSpamBERT
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results: []
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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model-index:
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- name: HamSpamBERT
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results: []
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widget:
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- text: "Ok i am on the way to home bye"
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example_title: "Sentiment analysis"
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- text: "PRIVATE! Your 2004 Account Statement for 07742676969 shows 786 unredeemed Bonus Points. To claim call 08719180248 Identifier Code: 45239 Expires"
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example_title: "Sentiment analysis"
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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