Instructions to use KalaiselvanD/model_albert_20 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KalaiselvanD/model_albert_20 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KalaiselvanD/model_albert_20")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("KalaiselvanD/model_albert_20") model = AutoModelForSequenceClassification.from_pretrained("KalaiselvanD/model_albert_20", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1f2138e27dd7af05f8d560054bf9a5d73204052d07ee6d37e3ad2ba6d684aae2
- Size of remote file:
- 46.7 MB
- SHA256:
- 207811dd89621a41dae5172b2caa0e6b849e8a96220c4f66bb36507fa5cc1a4e
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