Instructions to use zfan3/SPTM_V2_ID1023 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zfan3/SPTM_V2_ID1023 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="zfan3/SPTM_V2_ID1023")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("zfan3/SPTM_V2_ID1023") model = AutoModelForSequenceClassification.from_pretrained("zfan3/SPTM_V2_ID1023", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b5c9999df4750609b7e1efa52d14c659f1288f4b068dabc938a3f107f4213889
- Size of remote file:
- 136 MB
- SHA256:
- 911fb7e5813955b731066605a2e8c5db3cf3e1319c09d1b1a3b5771bca10a864
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