Instructions to use stdnan/rubert-tiny-antispam with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use stdnan/rubert-tiny-antispam with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="stdnan/rubert-tiny-antispam")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("stdnan/rubert-tiny-antispam") model = AutoModelForSequenceClassification.from_pretrained("stdnan/rubert-tiny-antispam", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from stdnan/rubert-tiny-antispam: direct link, hf CLI and curl.
- Browser
- Download file 5.3 kB
-
https://huggingface.co/stdnan/rubert-tiny-antispam/resolve/main/training_args.bin
- Command line
-
hf download hf://stdnan/rubert-tiny-antispam/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/stdnan/rubert-tiny-antispam/resolve/main/training_args.bin
5.3 kB
- Xet hash:
- dbb61a7c4a26193617c81d49050296c4b13a898114cebd3980220488a1aff1e0
- Size of remote file:
- 5.3 kB
- SHA256:
- 2b3ee0d6514959a60b4ac6f3217d1af462f302566735821ebef53bf40edce7cf
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