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:
- cb75054b76ad9a311c2821bf1bea26fab0901fec399fd2f52dd3069f9277582d
- Size of remote file:
- 541 MB
- SHA256:
- 4eecb3324de91a04c97f688eb8462ef506211de4bb809b6d8d0cb8b01e5171cc
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