Instructions to use Aalaa/baseline_transfomer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Aalaa/baseline_transfomer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Aalaa/baseline_transfomer")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Aalaa/baseline_transfomer") model = AutoModelForSequenceClassification.from_pretrained("Aalaa/baseline_transfomer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e48f0465725d081b63e6624aa3da7738e98e0c7a06846631019c00dae9548f4a
- Size of remote file:
- 3.52 kB
- SHA256:
- 7f326c7a05267899997f12b30eb104a707207946955ac579355d3fb9c012592a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.