Instructions to use HasinMDG/SetFit_Labse_IPTC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use HasinMDG/SetFit_Labse_IPTC with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("HasinMDG/SetFit_Labse_IPTC") 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 HasinMDG/SetFit_Labse_IPTC with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("HasinMDG/SetFit_Labse_IPTC") - Notebooks
- Google Colab
- Kaggle
File size: 482 Bytes
8125043 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | {
"cls_token": "[CLS]",
"do_basic_tokenize": true,
"do_lower_case": false,
"full_tokenizer_file": null,
"mask_token": "[MASK]",
"model_max_length": 512,
"name_or_path": "/kaggle/working/iptc_classifier/",
"never_split": null,
"pad_token": "[PAD]",
"sep_token": "[SEP]",
"special_tokens_map_file": "labse-pytorch/special_tokens_map.json",
"strip_accents": null,
"tokenize_chinese_chars": true,
"tokenizer_class": "BertTokenizer",
"unk_token": "[UNK]"
}
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