Text Classification
Transformers
PyTorch
Swedish
bert
feature-extraction
sentence-similarity
text-embeddings-inference
Instructions to use Gabriel/Swe-review-setfit-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Gabriel/Swe-review-setfit-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Gabriel/Swe-review-setfit-model")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Gabriel/Swe-review-setfit-model") model = AutoModel.from_pretrained("Gabriel/Swe-review-setfit-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 60794287af7bdbcdb1331c7b34cfff544ee960aadf4c34e37ae5b68a5322ec94
- Size of remote file:
- 499 MB
- SHA256:
- 4e72c31550fe861dbbe471f1151eb0cdac7598ecc62c2fa9fc3eed02d89d2411
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