Instructions to use RozaA/Menon_NorBert3s with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RozaA/Menon_NorBert3s with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="RozaA/Menon_NorBert3s", trust_remote_code=True)# Load model directly from transformers import AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained("RozaA/Menon_NorBert3s", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Menon relevance classifier (NorBERT3-small)
This model predicts relevance for Menon leads.
Label: Is_relevant (0/1) Output: probability of relevance (class 1)
Usage
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
repo_id = "RozaA/Menon_NorBert3s"
tokenizer = AutoTokenizer.from_pretrained(repo_id, trust_remote_code=True)
model = AutoModelForSequenceClassification.from_pretrained(repo_id, trust_remote_code=True)
text = "Example text"
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
prob = torch.softmax(model(**inputs).logits, dim=1)[0,1].item()
print(prob)
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