Instructions to use MiBo/RepML with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MiBo/RepML with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MiBo/RepML")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("MiBo/RepML") model = AutoModelForSequenceClassification.from_pretrained("MiBo/RepML") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a270d994839e07790961821acba5c795cdc463137034b412127868e86336408c
- Size of remote file:
- 443 MB
- SHA256:
- 121632ae9cf59c2cc64b6b4aee2297958a16603ac37cd73f3d6c604bdc1ad46c
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