Text Classification
Transformers
PyTorch
TensorBoard
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use wnic00/hihu3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wnic00/hihu3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="wnic00/hihu3")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("wnic00/hihu3") model = AutoModelForSequenceClassification.from_pretrained("wnic00/hihu3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e99038f3415ed6efdf7527a897bdbd6e2cd935fae018a9510303c8c7dac67f69
- Size of remote file:
- 1.11 GB
- SHA256:
- 671a1922c21571ad42d3a3e7021a8c52064bf53d6f0249d826a9023c2f775434
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.