Instructions to use KennethEnevoldsen/dfm-sentence-encoder-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KennethEnevoldsen/dfm-sentence-encoder-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="KennethEnevoldsen/dfm-sentence-encoder-large")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("KennethEnevoldsen/dfm-sentence-encoder-large") model = AutoModel.from_pretrained("KennethEnevoldsen/dfm-sentence-encoder-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| {"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": false, "do_basic_tokenize": true, "name_or_path": "chcaa/dfm-encoder-large-v1", "never_split": null, "special_tokens_map_file": null, "tokenizer_class": "BertTokenizer", "model_max_length": 512} |