Instructions to use twielema/trained50 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use twielema/trained50 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="twielema/trained50")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("twielema/trained50") model = AutoModelForQuestionAnswering.from_pretrained("twielema/trained50", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d2b7c5eee921bc8d6f5e804c585d4bd440e4ba6f7731c2c238b42bdabdd79d50
- Size of remote file:
- 669 kB
- SHA256:
- dabbb9503b3d5c3ed43e786a9dadde0f53320d10fd3f7be045c3623c3d6454a4
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