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
metadata
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
rubert-tiny-antispam
This model is a fine-tuned version of cointegrated/rubert-tiny on an unknown dataset. It's WIP, so don't expect good performance.