Sentence Similarity
sentence-transformers
PyTorch
Transformers
distilbert
feature-extraction
text-embeddings-inference
Instructions to use gemasphi/laprador_trained with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use gemasphi/laprador_trained with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("gemasphi/laprador_trained") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use gemasphi/laprador_trained with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("gemasphi/laprador_trained") model = AutoModel.from_pretrained("gemasphi/laprador_trained", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from gemasphi/laprador_trained: direct link, hf CLI and curl.
- Browser
- Download file 335 Bytes
-
https://huggingface.co/gemasphi/laprador_trained/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://gemasphi/laprador_trained/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/gemasphi/laprador_trained/resolve/main/tokenizer_config.json
335 Bytes
| {"do_lower_case": false, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "model_max_length": 512, "special_tokens_map_file": null, "name_or_path": "./artifacts/model_hf:v3/", "tokenizer_class": "DistilBertTokenizer"} |