Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use dd123/test_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dd123/test_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dd123/test_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dd123/test_model") model = AutoModelForSequenceClassification.from_pretrained("dd123/test_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b628888612237cf85f3c1c034bfc290a0f44d4a1bedf68c0e75221cb36363b02
- Size of remote file:
- 3.58 kB
- SHA256:
- 285ccce9e5917efab941d633ecc6c428a12d1f3d40d32e1df53658f0180361c4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.