Sentence Similarity
sentence-transformers
PyTorch
Safetensors
Transformers
deberta-v2
feature-extraction
text-embeddings-inference
Instructions to use HAYDERphd/polyBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use HAYDERphd/polyBERT with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("HAYDERphd/polyBERT") sentences = [ "[*]CC[*]", "[*]COC[*]", "[*]CC(C)C[*]" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Transformers
How to use HAYDERphd/polyBERT with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("HAYDERphd/polyBERT") model = AutoModel.from_pretrained("HAYDERphd/polyBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 382 Bytes
7bf9ed3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | {
"bos_token": "[CLS]",
"cls_token": "[CLS]",
"do_lower_case": false,
"eos_token": "[SEP]",
"mask_token": "[MASK]",
"name_or_path": "polyBERT_sentence_transformers/",
"pad_token": "[PAD]",
"sep_token": "[SEP]",
"sp_model_kwargs": {},
"special_tokens_map_file": null,
"split_by_punct": false,
"tokenizer_class": "DebertaV2Tokenizer",
"unk_token": "[UNK]"
}
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