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
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Download README.md from stdnan/rubert-tiny-antispam: direct link, hf CLI and curl.
- Browser
- Download file 606 Bytes
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https://huggingface.co/stdnan/rubert-tiny-antispam/resolve/main/README.md
- Command line
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hf download hf://stdnan/rubert-tiny-antispam/README.md
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curl -L -o README.md https://huggingface.co/stdnan/rubert-tiny-antispam/resolve/main/README.md
606 Bytes
| library_name: transformers | |
| license: mit | |
| base_model: cointegrated/rubert-tiny | |
| tags: | |
| - antispam | |
| model-index: | |
| - name: rubert-tiny-antispam | |
| results: [] | |
| language: | |
| - ru | |
| - en | |
| pipeline_tag: text-classification | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # rubert-tiny-antispam | |
| This model is a fine-tuned version of [cointegrated/rubert-tiny](https://huggingface.co/cointegrated/rubert-tiny) on an unknown dataset. | |
| It's WIP, so don't expect good performance. | |