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:
- c29ef9b5a60a20597ad951d51e70e77e8b6ab054cf33cc2a9924852a0405ed6b
- Size of remote file:
- 499 MB
- SHA256:
- 5b4c52bb5c8eec5c8823ec450a4b689ca85eb3953282e2f5e9a45ebbc0b0fd6e
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