Instructions to use lewispons/Email-classifier-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use lewispons/Email-classifier-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("lewispons/Email-classifier-v2") 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 lewispons/Email-classifier-v2 with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("lewispons/Email-classifier-v2") - Notebooks
- Google Colab
- Kaggle
File size: 596 Bytes
fd9e625 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | {
"cls_token": "[CLS]",
"do_basic_tokenize": true,
"do_lower_case": true,
"mask_token": "[MASK]",
"model_max_length": 512,
"name_or_path": "/home/studio-lab-user/.cache/torch/sentence_transformers/sentence-transformers_all-MiniLM-L6-v2/",
"never_split": null,
"pad_token": "[PAD]",
"sep_token": "[SEP]",
"special_tokens_map_file": "/home/studio-lab-user/.cache/torch/sentence_transformers/sentence-transformers_all-MiniLM-L6-v2/special_tokens_map.json",
"strip_accents": null,
"tokenize_chinese_chars": true,
"tokenizer_class": "BertTokenizer",
"unk_token": "[UNK]"
}
|