Text Classification
Transformers
PyTorch
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use pavithrav/distilbert-base-uncased-finetuned-custom-data with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pavithrav/distilbert-base-uncased-finetuned-custom-data with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="pavithrav/distilbert-base-uncased-finetuned-custom-data")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("pavithrav/distilbert-base-uncased-finetuned-custom-data") model = AutoModelForSequenceClassification.from_pretrained("pavithrav/distilbert-base-uncased-finetuned-custom-data", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
#1 opened about 1 year ago
by
SFconvertbot