Instructions to use KalaiselvanD/albert_model__29_3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KalaiselvanD/albert_model__29_3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KalaiselvanD/albert_model__29_3")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("KalaiselvanD/albert_model__29_3") model = AutoModelForSequenceClassification.from_pretrained("KalaiselvanD/albert_model__29_3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2b410f802384c88eadeb0ff52588bcec521033f8f648d349ad4e8ecd30abee8b
- Size of remote file:
- 268 MB
- SHA256:
- 32ccd4c0071985ad596936731129e6ba124c534552193d1d00efdb23a9bd9dbe
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