Sentence Similarity
sentence-transformers
PyTorch
Transformers
roberta
feature-extraction
text-embeddings-inference
Instructions to use teven/roberta_kelm_tekgen with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use teven/roberta_kelm_tekgen with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("teven/roberta_kelm_tekgen") 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 teven/roberta_kelm_tekgen with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("teven/roberta_kelm_tekgen") model = AutoModel.from_pretrained("teven/roberta_kelm_tekgen", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| {"unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>", "add_prefix_space": false, "errors": "replace", "sep_token": "</s>", "cls_token": "<s>", "pad_token": "<pad>", "mask_token": "<mask>", "special_tokens_map_file": null, "name_or_path": "./", "tokenizer_class": "RobertaTokenizer"} |