Text Classification
sentence-transformers
PyTorch
setfit
bert
Eval Results (legacy)
text-embeddings-inference
Instructions to use germla/satoken with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use germla/satoken with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("germla/satoken") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - setfit
How to use germla/satoken with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("germla/satoken") preds = model.predict(["i loved the spiderman movie!", "pineapple on pizza is the worst"]) print(preds) - Notebooks
- Google Colab
- Kaggle
File size: 496 Bytes
294887b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | {
"bos_token": "<s>",
"clean_up_tokenization_spaces": true,
"cls_token": "<s>",
"do_lower_case": true,
"eos_token": "</s>",
"mask_token": {
"__type": "AddedToken",
"content": "<mask>",
"lstrip": true,
"normalized": true,
"rstrip": false,
"single_word": false
},
"model_max_length": 512,
"pad_token": "<pad>",
"sep_token": "</s>",
"strip_accents": null,
"tokenize_chinese_chars": true,
"tokenizer_class": "BertTokenizer",
"unk_token": "<unk>"
}
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