Text Classification
Transformers
PyTorch
Safetensors
English
marketing_classifier
feature-extraction
fineweb
marketing
content-filtering
data-curation
gemma
embedding
custom_code
Instructions to use marketeam/Fineweb-Classifier-Marketing with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use marketeam/Fineweb-Classifier-Marketing with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="marketeam/Fineweb-Classifier-Marketing", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("marketeam/Fineweb-Classifier-Marketing", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Add trust_remote_code wrapper (custom AutoModel + pipeline), base_model metadata, weights, and expanded model card
b3a38d0 verified - Xet hash:
- 562245d2c4c08b5ceb5710e21e5e2702cfba2c4cfa1057e2fef6296813a584d0
- Size of remote file:
- 1.22 GB
- SHA256:
- 21d1d09da7e062354a36f70bb176dbc5a9894e1afb70e72eff55a324367a95b2
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