Feature Extraction
sentence-transformers
PyTorch
Safetensors
Transformers
English
bert
Phrase Representation
String Matching
Fuzzy Join
Entity Retrieval
text-embeddings-inference
Instructions to use Lihuchen/pearl_small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Lihuchen/pearl_small with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Lihuchen/pearl_small") 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] - Transformers
How to use Lihuchen/pearl_small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Lihuchen/pearl_small")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Lihuchen/pearl_small") model = AutoModel.from_pretrained("Lihuchen/pearl_small") - Notebooks
- Google Colab
- Kaggle
File size: 314 Bytes
d620c21 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 | {
"clean_up_tokenization_spaces": true,
"cls_token": "[CLS]",
"do_lower_case": true,
"mask_token": "[MASK]",
"model_max_length": 512,
"pad_token": "[PAD]",
"sep_token": "[SEP]",
"strip_accents": null,
"tokenize_chinese_chars": true,
"tokenizer_class": "BertTokenizer",
"unk_token": "[UNK]"
}
|