Instructions to use franfj/DIPROMATS_subtask_2_v4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use franfj/DIPROMATS_subtask_2_v4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="franfj/DIPROMATS_subtask_2_v4")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("franfj/DIPROMATS_subtask_2_v4") model = AutoModelForSequenceClassification.from_pretrained("franfj/DIPROMATS_subtask_2_v4") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3f91178a0e00b9c7e27a77546435c5c09451c0ed9ebf4c70da6eec07e68f5ff2
- Size of remote file:
- 329 MB
- SHA256:
- ab300cb9334fd16e70007ca99b2d206c2e9b97af764234d7b05920bd13aa7da3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.