Instructions to use WSTR/name with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use WSTR/name with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="WSTR/name")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("WSTR/name") model = AutoModelForSequenceClassification.from_pretrained("WSTR/name", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1d07576a84149f76762698ef582875deffef4151c491d22a912a54b67016724b
- Size of remote file:
- 134 MB
- SHA256:
- 1271e7540ef70eb09e431d4543c0135b8ea78b5f388d79bf6385936bb84b2474
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