Instructions to use JasperLS/gelectra-base-injection-pt_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JasperLS/gelectra-base-injection-pt_v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JasperLS/gelectra-base-injection-pt_v1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("JasperLS/gelectra-base-injection-pt_v1") model = AutoModelForSequenceClassification.from_pretrained("JasperLS/gelectra-base-injection-pt_v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 547a8a631f8c524fa3880b0397e457f8854a313010c270072599f4927c2be75e
- Size of remote file:
- 3.58 kB
- SHA256:
- 90ed17cc46a9cfa7cc6b0d439aae047a6d04fa925e2546d92175a2346de5ac87
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