Text Classification
Transformers
Safetensors
bert
scibert
data-paper-classification
scholarly-papers
binary-classification
Eval Results (legacy)
text-embeddings-inference
Instructions to use zehralx/scibert-data-paper with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zehralx/scibert-data-paper with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="zehralx/scibert-data-paper")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("zehralx/scibert-data-paper") model = AutoModelForSequenceClassification.from_pretrained("zehralx/scibert-data-paper", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8311c87d33df99824f639d37565f403088f235328a8e4b32778797599fb1b368
- Size of remote file:
- 440 MB
- SHA256:
- b3a8e2b9a93fe67bc1f9db19eb2215eb1c87e1dc18c3d0cf310cb02af44f9872
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