Text Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use Surbhipatil/Text_Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Surbhipatil/Text_Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Surbhipatil/Text_Classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Surbhipatil/Text_Classification") model = AutoModelForSequenceClassification.from_pretrained("Surbhipatil/Text_Classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cca67b8ad8bdeca632ccc4f6619bf5de5a9ddfb140762c6f8c604e57bb52212f
- Size of remote file:
- 5.78 kB
- SHA256:
- 99b3162e1c5f8d3aa7125686217645f9ba20846f1b8c5c3ba4a06b211c4bb12b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.