Instructions to use KalaiselvanD/albert_model__29_4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KalaiselvanD/albert_model__29_4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KalaiselvanD/albert_model__29_4")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("KalaiselvanD/albert_model__29_4") model = AutoModelForSequenceClassification.from_pretrained("KalaiselvanD/albert_model__29_4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bafee388d9dd649bc43c029d288d7d7b2e418b3008afd2b5be1a08d5a36fc52a
- Size of remote file:
- 268 MB
- SHA256:
- 255d40234b9f99592969e371d70389520368253e4431645d50e9aa5abab9ee61
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