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)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("marketeam/Fineweb-Classifier-Marketing", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Add trust_remote_code wrapper (custom AutoModel + pipeline), base_model metadata, weights, and expanded model card
b3a38d0 verified Download tokenizer.json from marketeam/Fineweb-Classifier-Marketing: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/marketeam/Fineweb-Classifier-Marketing/resolve/main/tokenizer.json
- Command line
-
hf download hf://marketeam/Fineweb-Classifier-Marketing/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/marketeam/Fineweb-Classifier-Marketing/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- b8e241e404a10d5495cc933310aabbd4de2845f68b22897c23bfc5198dde2af1
- Size of remote file:
- 17.1 MB
- SHA256:
- 883b037111086fd4dfebbbc9b7cee11e1517b5e0c0514879478661440f137085
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