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:
- 1acd7029c4ab4f9b7082e6a5276dc2f5678afd4fb4943f613a4164bbc7b10712
- Size of remote file:
- 499 MB
- SHA256:
- 485e39cb2e356fb2d36785bccce13b83b6bd307b1f091f5c744b507188240327
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