Instructions to use gouravchat/tmp_trainer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gouravchat/tmp_trainer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="gouravchat/tmp_trainer")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("gouravchat/tmp_trainer") model = AutoModelForSequenceClassification.from_pretrained("gouravchat/tmp_trainer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0a7e0617f811b5ae3c017662e0ba190f9aa3afb5300ebdb06e8dc179fd13567a
- Size of remote file:
- 10.3 MB
- SHA256:
- c3ea132df8a4ce084baed60d7b9287fca43b9d29e0cc433b0e3c50a9ee2891fb
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