Instructions to use franfj/DIPROMATS_subtask_3_group2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use franfj/DIPROMATS_subtask_3_group2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="franfj/DIPROMATS_subtask_3_group2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("franfj/DIPROMATS_subtask_3_group2") model = AutoModelForSequenceClassification.from_pretrained("franfj/DIPROMATS_subtask_3_group2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from franfj/DIPROMATS_subtask_3_group2: direct link, hf CLI and curl.
- Browser
- Download file 329 MB
-
https://huggingface.co/franfj/DIPROMATS_subtask_3_group2/resolve/main/model.safetensors
- Command line
-
hf download hf://franfj/DIPROMATS_subtask_3_group2/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/franfj/DIPROMATS_subtask_3_group2/resolve/main/model.safetensors
329 MB
- Xet hash:
- 4a59e441370bbfd2de557749fc1820f84f4034adc2922b75c0bf77d2a0335c2a
- Size of remote file:
- 329 MB
- SHA256:
- e0b4e403f36016ad0e611d1b19c99d795e31f5a2e25233399e026f222dd64c0c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.