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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 565db28edc277709cff5f6b9f15ed721fe52af3fab2364e99eced8bfcb2a9ebc
- Size of remote file:
- 329 MB
- SHA256:
- dd0b9e260026c5a1389ce6a83a5980a003cc23b1471a9629edbbc9c255916de8
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