Instructions to use rafacost/bert_small_pt_en_uncased_email_spam with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rafacost/bert_small_pt_en_uncased_email_spam with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rafacost/bert_small_pt_en_uncased_email_spam")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rafacost/bert_small_pt_en_uncased_email_spam") model = AutoModelForSequenceClassification.from_pretrained("rafacost/bert_small_pt_en_uncased_email_spam") - Notebooks
- Google Colab
- Kaggle
Model Card for Model ID
Text Classification Model for SPAM/HAM
Model Details
Model Description
Text Classification Model for SPAM identification in Portuguese and English Email Subjects.
Trained on my own email inbox dataset of aprox. 50.000 emails.
v1 accuracy: 0.9429411764705883
- Developed by: rafacost
- Model type: Text Classification
- Language(s) (NLP): Portuguese Brazillian and English
- License: Apache 2.0
- Finetuned from model [optional]: bert-base-multilingual-cased
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