Instructions to use schreon/xnext-lhm_queries_encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use schreon/xnext-lhm_queries_encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="schreon/xnext-lhm_queries_encoder")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("schreon/xnext-lhm_queries_encoder") model = AutoModel.from_pretrained("schreon/xnext-lhm_queries_encoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 430 Bytes
5214b3d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | {
"cls_token": "[CLS]",
"do_basic_tokenize": true,
"do_lower_case": false,
"mask_token": "[MASK]",
"max_len": 512,
"name_or_path": "deepset/gbert-base-germandpr-ctx_encoder",
"never_split": null,
"pad_token": "[PAD]",
"sep_token": "[SEP]",
"special_tokens_map_file": null,
"strip_accents": false,
"tokenize_chinese_chars": true,
"tokenizer_class": "DPRQuestionEncoderTokenizer",
"unk_token": "[UNK]"
}
|