Text Classification
Transformers
PyTorch
TensorFlow
English
roberta
roberta-based
historical newspaper
late modern english
text classification
Not-For-All-Audiences
text-embeddings-inference
Instructions to use npedrazzini/HistoroBERTa-SuicideIncidentClassifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use npedrazzini/HistoroBERTa-SuicideIncidentClassifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="npedrazzini/HistoroBERTa-SuicideIncidentClassifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("npedrazzini/HistoroBERTa-SuicideIncidentClassifier") model = AutoModelForSequenceClassification.from_pretrained("npedrazzini/HistoroBERTa-SuicideIncidentClassifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("npedrazzini/HistoroBERTa-SuicideIncidentClassifier")
model = AutoModelForSequenceClassification.from_pretrained("npedrazzini/HistoroBERTa-SuicideIncidentClassifier", device_map="auto")Quick Links
Not-For-All-Audiences
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View model card
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="npedrazzini/HistoroBERTa-SuicideIncidentClassifier")