Instructions to use franfj/DIPROMATS_subtask_2_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use franfj/DIPROMATS_subtask_2_v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="franfj/DIPROMATS_subtask_2_v2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("franfj/DIPROMATS_subtask_2_v2") model = AutoModelForSequenceClassification.from_pretrained("franfj/DIPROMATS_subtask_2_v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from franfj/DIPROMATS_subtask_2_v2: direct link, hf CLI and curl.
- Browser
- Download file 329 MB
-
https://huggingface.co/franfj/DIPROMATS_subtask_2_v2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://franfj/DIPROMATS_subtask_2_v2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/franfj/DIPROMATS_subtask_2_v2/resolve/main/pytorch_model.bin
329 MB
- Xet hash:
- d327f32d2b740f0fba0f4e2d14742c3c790e58a8cf72739b80d4c926d9191060
- Size of remote file:
- 329 MB
- SHA256:
- f5b30cfafbb4f481222e80c66efba4e47deb2e0351295b46da5188a1372d6b87
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.