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
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README.md
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license: mit
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base_model: roberta-base
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They also caused so much stress because some leaders valued optics over output.: Stressed
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Way too much work pressure.
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Understaffed, lots of deck revisions, unpredictable, terrible technology.
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Nice environment, good work life balance.
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model-index:
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- name: roberta-base_topic_classification_nyt_news
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results:
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---
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license: mit
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base_model: roberta-base
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- text: >-
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They also caused so much stress because some leaders valued optics over output.: Stressed
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- text: >-
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Way too much work pressure.
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example_title: Stressed
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Understaffed, lots of deck revisions, unpredictable, terrible technology.
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example_title: Stressed
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Nice environment, good work life balance.
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example_title: Not Stressed
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model-index:
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- name: roberta-base_topic_classification_nyt_news
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results:
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