Instructions to use jenspt/bert_regression_basic_16_batch_size with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jenspt/bert_regression_basic_16_batch_size with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jenspt/bert_regression_basic_16_batch_size")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jenspt/bert_regression_basic_16_batch_size") model = AutoModelForSequenceClassification.from_pretrained("jenspt/bert_regression_basic_16_batch_size", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 269eb5b46614f6702052cf10f5f9d75a99a12f2e8bfd94dd484e352eedf90b6a
- Size of remote file:
- 438 MB
- SHA256:
- 8d1028bc9fc4563d3962a084bc2ea7784fb50211f89fe104fed6dbd1ae838141
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