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:
- 10309591b93e6404738c0e1253d96154dbecf35cb38418258985f9956c7efa9b
- Size of remote file:
- 4.66 kB
- SHA256:
- 538f03d57cb1e8641aad0e5df3a561c25236fb347a1facbeb8c1288396c7f014
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