Instructions to use bdpc/test_twowayloss_implementation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bdpc/test_twowayloss_implementation with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bdpc/test_twowayloss_implementation")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("bdpc/test_twowayloss_implementation") model = AutoModelForSequenceClassification.from_pretrained("bdpc/test_twowayloss_implementation", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 327b445b452493bf678c74ce98235e2904e5480acec1c3eec298daf5397b7b08
- Size of remote file:
- 439 MB
- SHA256:
- 19985ac5ee7026eea15edaecf82666acace1cff376f3e301042b1e825e2e7d3a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.