Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use P829692/thesis-model-2-original with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use P829692/thesis-model-2-original with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="P829692/thesis-model-2-original")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("P829692/thesis-model-2-original") model = AutoModelForSequenceClassification.from_pretrained("P829692/thesis-model-2-original") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8bafdbc466f2abc032d5e05b6f279853e587ac33118ccc87e55afb6691d56da9
- Size of remote file:
- 3.52 kB
- SHA256:
- 495672b6fac29e4689e4a98e3e4350e08f9a8478e88e15b9edc35f7a00e91402
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.