Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use ceggian/sbert_pt_reddit_softmax_32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ceggian/sbert_pt_reddit_softmax_32 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ceggian/sbert_pt_reddit_softmax_32") 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 ceggian/sbert_pt_reddit_softmax_32 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("ceggian/sbert_pt_reddit_softmax_32") model = AutoModel.from_pretrained("ceggian/sbert_pt_reddit_softmax_32") - Notebooks
- Google Colab
- Kaggle
Upload tokenizer_config.json
Browse files- tokenizer_config.json +1 -0
tokenizer_config.json
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{"do_lower_case": true, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "do_basic_tokenize": true, "never_split": null, "model_max_length": 512, "special_tokens_map_file": null, "name_or_path": "ceggian/bert_post_trained_reddit_batch32", "tokenizer_class": "BertTokenizer"}
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