Instructions to use MohammadKarami/bloom560mtask2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MohammadKarami/bloom560mtask2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MohammadKarami/bloom560mtask2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("MohammadKarami/bloom560mtask2") model = AutoModelForSequenceClassification.from_pretrained("MohammadKarami/bloom560mtask2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- da51521eb1c9940fea1c6f93713ecf0f7e5cb5724e4288d674d6d670a422ec4d
- Size of remote file:
- 14.5 MB
- SHA256:
- c15972849c91c78dfab27e2ef0eb53b17f95c4fe81be6664f7e85c6a7fffde0e
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