Text Classification
Transformers
PyTorch
TensorBoard
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use pabagcha/roberta_crypto_profiling_task1_3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pabagcha/roberta_crypto_profiling_task1_3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="pabagcha/roberta_crypto_profiling_task1_3")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("pabagcha/roberta_crypto_profiling_task1_3") model = AutoModelForSequenceClassification.from_pretrained("pabagcha/roberta_crypto_profiling_task1_3") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c50d10d986247ce4627eb3bc86ce3120997835238a5b8e8abf0e35a95597d912
- Size of remote file:
- 3.96 kB
- SHA256:
- f854f852ad032c7534602280513073263de34b763a63cea998fa5ea6a46ac604
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.