Token Classification
Transformers
Safetensors
English
eurobert
html
content-extraction
web-scraping
boilerplate-removal
encoder
rag
custom_code
Instructions to use feyninc/pulpie-orange-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use feyninc/pulpie-orange-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="feyninc/pulpie-orange-base", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("feyninc/pulpie-orange-base", trust_remote_code=True) model = AutoModelForTokenClassification.from_pretrained("feyninc/pulpie-orange-base", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Relicense weights to Apache 2.0
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Add commercial-licensing contact
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License model weights under CC BY-NC 4.0
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Add Pulpie Orange banner
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Add model card
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Replace Qwen3-0.6B with EuroBERT-610M distilled from 2.1B teacher (ROUGE-5: 0.849)
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Remove old Qwen model files
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Remove old Qwen model files
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Remove old Qwen model files
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Remove old Qwen model files
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Update: 100K training (ROUGE-5=0.839, qrater=73.8%)
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Upload hummingbird-latte-base-v1 (epoch 2 best, ROUGE-5=0.757, F1=0.771)
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initial commit
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