Text Classification
Transformers
PyTorch
TensorBoard
Safetensors
English
roberta
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use JeremiahZ/roberta-base-sst2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JeremiahZ/roberta-base-sst2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JeremiahZ/roberta-base-sst2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("JeremiahZ/roberta-base-sst2") model = AutoModelForSequenceClassification.from_pretrained("JeremiahZ/roberta-base-sst2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- db4183efd9fd3fe90b247e099726541bb98fa925296be700e451cfaf74dcce1e
- Size of remote file:
- 3.31 kB
- SHA256:
- 3705e8076674c01497726fff84432944bb664f778ca252a6efac16b3a1aa3671
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