Instructions to use mjaydenkim/test_trainer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mjaydenkim/test_trainer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mjaydenkim/test_trainer")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mjaydenkim/test_trainer") model = AutoModelForSequenceClassification.from_pretrained("mjaydenkim/test_trainer") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a948ceca6a2c61fda345989a96c462b78b27fc8b97371f085ffa682822474e6a
- Size of remote file:
- 499 MB
- SHA256:
- 935b22c82a88c4ee7d29ceb7f1e4141b433ce43093fdd10e00bd89166fdd6a9f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.