Text Classification
Transformers
PyTorch
TensorBoard
Safetensors
roberta
stress
classification
glassdoor
Eval Results (legacy)
text-embeddings-inference
Instructions to use dstefa/roberta-base_stress_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dstefa/roberta-base_stress_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dstefa/roberta-base_stress_classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dstefa/roberta-base_stress_classification") model = AutoModelForSequenceClassification.from_pretrained("dstefa/roberta-base_stress_classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fb9310bf00e716a9baa3412b24d67d2b48d4d65f8565a70a2192b085dd0fcc8d
- Size of remote file:
- 512 Bytes
- SHA256:
- fb6d23b4e07ab512d863510cf2bb98557c25cce2dc3b305f5a4c122354c6e304
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