Instructions to use franfj/UNEDMediaBiasTeam_at_SemEval23_Task3_Subtask3_MULTI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use franfj/UNEDMediaBiasTeam_at_SemEval23_Task3_Subtask3_MULTI with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="franfj/UNEDMediaBiasTeam_at_SemEval23_Task3_Subtask3_MULTI")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("franfj/UNEDMediaBiasTeam_at_SemEval23_Task3_Subtask3_MULTI") model = AutoModelForSequenceClassification.from_pretrained("franfj/UNEDMediaBiasTeam_at_SemEval23_Task3_Subtask3_MULTI", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from franfj/UNEDMediaBiasTeam_at_SemEval23_Task3_Subtask3_MULTI: direct link, hf CLI and curl.
- Browser
- Download file 712 MB
-
https://huggingface.co/franfj/UNEDMediaBiasTeam_at_SemEval23_Task3_Subtask3_MULTI/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://franfj/UNEDMediaBiasTeam_at_SemEval23_Task3_Subtask3_MULTI@refs/pr/1/pytorch_model.bin
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curl -L -o pytorch_model.bin https://huggingface.co/franfj/UNEDMediaBiasTeam_at_SemEval23_Task3_Subtask3_MULTI/resolve/refs%2Fpr%2F1/pytorch_model.bin
712 MB
- Xet hash:
- 79707b5335529785077af51da94a08f7f3cba433115f516dcf6223476ae90835
- Size of remote file:
- 712 MB
- SHA256:
- c33640336a6b77c027acbad95c3c51e8dd92f15c87fd858fdc3e32d7b59095ef
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