Instructions to use elftsdmr/5000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use elftsdmr/5000 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="elftsdmr/5000")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("elftsdmr/5000") model = AutoModelForSequenceClassification.from_pretrained("elftsdmr/5000", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 96b087eca31b0614abbf46577f479414f52cce0dae143d0b76f8ebea7a07bef7
- Size of remote file:
- 711 MB
- SHA256:
- c82f77c0b8e62830f40d07c6e25a3dcc30acb86dfc79f4c5451840e2224c0a03
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