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
| { | |
| "cls_token": "[CLS]", | |
| "do_basic_tokenize": true, | |
| "do_lower_case": false, | |
| "mask_token": "[MASK]", | |
| "name_or_path": "/root/.cache/torch/sentence_transformers/KBLab_sentence-bert-swedish-cased/", | |
| "never_split": null, | |
| "pad_token": "[PAD]", | |
| "sep_token": "[SEP]", | |
| "special_tokens_map_file": "/ceph/hpc/home/eufatonr/.cache/huggingface/transformers/37f2eab7cd9b3716ce0160ea9562138ae9247fb3ea61a2fd0190b16d0970444e.dd8bd9bfd3664b530ea4e645105f557769387b3da9f79bdb55ed556bdd80611d", | |
| "strip_accents": false, | |
| "tokenize_chinese_chars": true, | |
| "tokenizer_class": "BertTokenizer", | |
| "unk_token": "[UNK]" | |
| } | |