Text Classification
Transformers
PyTorch
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use NikiBase/train.log with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NikiBase/train.log with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="NikiBase/train.log")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("NikiBase/train.log") model = AutoModelForSequenceClassification.from_pretrained("NikiBase/train.log", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bf57c4000d3fb212a8f0ae2f72a7bd2e309d5ace120c73a5bd348aa223c85a7e
- Size of remote file:
- 3.45 kB
- SHA256:
- d3ff98e54bc9aa75f8032f4dff3c2e07e74a4b8009f4bcdf7c92c361095c60f0
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