Instructions to use KM4STfulltext/Journal-SSCIBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KM4STfulltext/Journal-SSCIBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KM4STfulltext/Journal-SSCIBERT")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("KM4STfulltext/Journal-SSCIBERT") model = AutoModelForSequenceClassification.from_pretrained("KM4STfulltext/Journal-SSCIBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a49ab7452e1a9ef529d948dc5b7adac83ef1dd331c4a042f71bf00c05472a69e
- Size of remote file:
- 433 MB
- SHA256:
- 4999846e11034cf871d089efb26e1df51cefc0a260e9ce4c18315a12a5f4ee3b
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