Instructions to use bdpc/SciBERT_TwoWayLoss_25K_bs64_P10_N5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bdpc/SciBERT_TwoWayLoss_25K_bs64_P10_N5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bdpc/SciBERT_TwoWayLoss_25K_bs64_P10_N5")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("bdpc/SciBERT_TwoWayLoss_25K_bs64_P10_N5") model = AutoModelForSequenceClassification.from_pretrained("bdpc/SciBERT_TwoWayLoss_25K_bs64_P10_N5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7ac6edbe714958490945bd89531f1d7f90ab5344254905bb1793cbb0099fba2e
- Size of remote file:
- 440 MB
- SHA256:
- 76b22d5c38301f796a37a12d28c707cc3bd4cd5ccf11c6a1c4981da0677e1a93
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