Instructions to use Blablablab/10dimensions-power with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Blablablab/10dimensions-power with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Blablablab/10dimensions-power")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Blablablab/10dimensions-power") model = AutoModelForSequenceClassification.from_pretrained("Blablablab/10dimensions-power") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fbe446dc72fa691e71347b06184e44ef4aab8c5d1e06e0e690f001ea0140984b
- Size of remote file:
- 433 MB
- SHA256:
- 7ef9c70646c694efa9087e9050c23ca760a3f9776561bcce1ebd20be1a108d84
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