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hermanda
/
robeczech-propaganda

Text Classification
Transformers
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use hermanda/robeczech-propaganda with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use hermanda/robeczech-propaganda with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-classification", model="hermanda/robeczech-propaganda")
    # pip install -U transformers accelerate
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSequenceClassification
    
    tokenizer = AutoTokenizer.from_pretrained("hermanda/robeczech-propaganda")
    model = AutoModelForSequenceClassification.from_pretrained("hermanda/robeczech-propaganda", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
robeczech-propaganda
508 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 9 commits
hermanda's picture
hermanda
Update model card for v2: calibration table, new thresholds, training data source
63e57ac verified about 2 months ago
  • .gitattributes
    1.52 kB
    initial commit 6 months ago
  • README.md
    2.44 kB
    Update model card for v2: calibration table, new thresholds, training data source about 2 months ago
  • config.json
    775 Bytes
    Update to v2: retrained on human-reviewed Argilla labels (comment-only, full finetune, 2x oversample) about 2 months ago
  • model.safetensors
    504 MB
    xet
    Update to v2: retrained on human-reviewed Argilla labels (comment-only, full finetune, 2x oversample) about 2 months ago
  • tokenizer.json
    3.94 MB
    Update to v2: retrained on human-reviewed Argilla labels (comment-only, full finetune, 2x oversample) about 2 months ago
  • tokenizer_config.json
    394 Bytes
    Update to v2: retrained on human-reviewed Argilla labels (comment-only, full finetune, 2x oversample) about 2 months ago
  • training_args.bin
    5.2 kB
    xet
    v4: 27k training data, all layers, class_weight=3.0, lr=1e-5, early_stop on loss 6 months ago