Instructions to use alenaa/evasiveness with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alenaa/evasiveness with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="alenaa/evasiveness")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("alenaa/evasiveness") model = AutoModelForSequenceClassification.from_pretrained("alenaa/evasiveness") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e4ce591c1846a7c53743cf58a718e90c160e671a92914ce8381aa783a84c03b4
- Size of remote file:
- 268 MB
- SHA256:
- 6d280db4af478e346121859d943fcd7d23bc6e6b61e73351f68db78754557193
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.