Text Classification
Transformers
Safetensors
English
roberta
multi-label-classification
disinformation
narrative-detection
propaganda
media-analysis
text-embeddings-inference
Instructions to use pjait/narrative_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pjait/narrative_classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="pjait/narrative_classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("pjait/narrative_classifier") model = AutoModel.from_pretrained("pjait/narrative_classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download metrics.txt from pjait/narrative_classifier: direct link, hf CLI and curl.
- Browser
- Download file 310 Bytes
-
https://huggingface.co/pjait/narrative_classifier/resolve/main/metrics.txt
- Command line
-
hf download hf://pjait/narrative_classifier/metrics.txt
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curl -L -o metrics.txt https://huggingface.co/pjait/narrative_classifier/resolve/main/metrics.txt
310 Bytes
| eval_loss: 0.02342209778726101 | |
| eval_narr_micro_f1: 0.49411764705882355 | |
| eval_narr_macro_f1: 0.18548155605115701 | |
| eval_narr_precision: 0.7 | |
| eval_narr_recall: 0.38181818181818183 | |
| eval_narr_subset_acc: 0.7867924528301887 | |
| eval_runtime: 1.7582 | |
| eval_samples_per_second: 301.446 | |
| eval_steps_per_second: 38.107 | |
| epoch: 3.0 | |