Text Classification
Transformers
PyTorch
English
bert
Generated from Trainer
text-embeddings-inference
Instructions to use DanL/scientific-challenges-and-directions with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DanL/scientific-challenges-and-directions with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="DanL/scientific-challenges-and-directions")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("DanL/scientific-challenges-and-directions") model = AutoModelForSequenceClassification.from_pretrained("DanL/scientific-challenges-and-directions", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9e5957fb2e3240f2405e39e218e8fcc9bb3c08f8fa56b06a9c1089ea0d40c210
- Size of remote file:
- 438 MB
- SHA256:
- 46c6663100b49bd10262c7eda415c764c699d935fcf6d87cb4cb50e924051fe8
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