Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use DipanAI/test_bug_temporary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DipanAI/test_bug_temporary with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="DipanAI/test_bug_temporary")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("DipanAI/test_bug_temporary") model = AutoModelForSequenceClassification.from_pretrained("DipanAI/test_bug_temporary") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 924e5c2a29db113e2df4e1e1194053155aafb90ff977e98fd8115a9b3a1904e8
- Size of remote file:
- 3.96 kB
- SHA256:
- ea2a00847965188f451e7387bd93f4fbc6f3c8b891877e1cc47bf51ea1ddca7e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.