Instructions to use yuzc19/bert-base-uncased-data-influence-model-lambada with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yuzc19/bert-base-uncased-data-influence-model-lambada with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="yuzc19/bert-base-uncased-data-influence-model-lambada")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("yuzc19/bert-base-uncased-data-influence-model-lambada") model = AutoModelForSequenceClassification.from_pretrained("yuzc19/bert-base-uncased-data-influence-model-lambada") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- adbc7dd01af90fc9a139ee08b9e1e30b8827a1a04418bf332d031f77d53efd52
- Size of remote file:
- 438 MB
- SHA256:
- b8198ab27263cdbe184ccd1706ac275cc7ff225ac986292f16e2cc0a712632ca
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