Instructions to use boronbrown48/topic_otherTopics_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use boronbrown48/topic_otherTopics_v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="boronbrown48/topic_otherTopics_v1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("boronbrown48/topic_otherTopics_v1") model = AutoModelForSequenceClassification.from_pretrained("boronbrown48/topic_otherTopics_v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9b691c1d2f820e744d44e48d677884b89ae0ca5a3ba28a42bb0918e1de3c47d8
- Size of remote file:
- 421 MB
- SHA256:
- 17e6c1e98e534b6eeab8dbf76ee8527a96756a9a5ab06d527357115d982f8512
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