Text Classification
Transformers
PyTorch
TensorBoard
Safetensors
English
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use JeremiahZ/roberta-base-mnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JeremiahZ/roberta-base-mnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JeremiahZ/roberta-base-mnli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("JeremiahZ/roberta-base-mnli") model = AutoModelForSequenceClassification.from_pretrained("JeremiahZ/roberta-base-mnli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4c17038a49889513794a4bda1cc77259263101f96cb50f5bd679f611ea65a679
- Size of remote file:
- 499 MB
- SHA256:
- 7ecff98a71ba79939f41ed5d37be50642ae4cafb5523f20000d5cb16be068fb3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.