Instructions to use autonomous019/bert_small_uncased_512 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use autonomous019/bert_small_uncased_512 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="autonomous019/bert_small_uncased_512")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("autonomous019/bert_small_uncased_512") model = AutoModelForSequenceClassification.from_pretrained("autonomous019/bert_small_uncased_512") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 537c072091d6d69e47d20e2cf5b3a2bdff51c03a93e226ce43e716c338fec7ff
- Size of remote file:
- 115 MB
- SHA256:
- ad5768c8b6829ef023d404e688c555178a1014050aaa0108002149baa8f48d89
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