Text Classification
Transformers
Safetensors
English
distilbert
intent-classification
email
sales
outreach
text-embeddings-inference
Instructions to use Tom11112000/email-reply-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Tom11112000/email-reply-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Tom11112000/email-reply-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Tom11112000/email-reply-classifier") model = AutoModelForSequenceClassification.from_pretrained("Tom11112000/email-reply-classifier") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bf36d62cf5dc7d7bc6289150d5b281792b4fc8ee4ac527dd56672b0201fd143f
- Size of remote file:
- 5.27 kB
- SHA256:
- 99e338852380697745fd093b35b7003a94b27ffd2b633e6e0de4d2e40dd580b5
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