Instructions to use sasi2400/AnswerBERT_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sasi2400/AnswerBERT_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sasi2400/AnswerBERT_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sasi2400/AnswerBERT_model") model = AutoModelForSequenceClassification.from_pretrained("sasi2400/AnswerBERT_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 92890be1ea0c2a945d2fcd4d2e110fd91dc5d38b1f9e8c0697ccd7329a827fb3
- Size of remote file:
- 438 MB
- SHA256:
- 89f46f77a8ac35966b47ff42fe116f30b6d1fd928cd3860c746d7e5085acd779
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