Instructions to use TimB-92/test-classification-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TimB-92/test-classification-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="TimB-92/test-classification-model", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("TimB-92/test-classification-model", trust_remote_code=True) model = AutoModel.from_pretrained("TimB-92/test-classification-model", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 178351eeadb96345a294bb44e5ef556886f2e97a7f31726c0d908d061b315094
- Size of remote file:
- 672 MB
- SHA256:
- ba54163add74c9f167845c3e6e366bd441ad30e9236e1beeeba668fe552041b9
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