Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use kkotkar1/test_trainer_old with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kkotkar1/test_trainer_old with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="kkotkar1/test_trainer_old")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kkotkar1/test_trainer_old") model = AutoModelForSequenceClassification.from_pretrained("kkotkar1/test_trainer_old") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 31bfadb08f3fc6cb5fcd50dd7dea2059e18d4bffa13c90deee37bc3507dc6743
- Size of remote file:
- 3.38 kB
- SHA256:
- 02bcaa087a9fd16834f1157771c067cd0d6c9911c810430a51242a00f688f559
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.