Instructions to use deepset/gbert-base-germandpr-question_encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deepset/gbert-base-germandpr-question_encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="deepset/gbert-base-germandpr-question_encoder")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("deepset/gbert-base-germandpr-question_encoder") model = AutoModel.from_pretrained("deepset/gbert-base-germandpr-question_encoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from deepset/gbert-base-germandpr-question_encoder: direct link, hf CLI and curl.
- Browser
- Download file 327 Bytes
-
https://huggingface.co/deepset/gbert-base-germandpr-question_encoder/resolve/c5b8d88c45320a0f8ba714c286ff3bc0af4d72fa/tokenizer_config.json
- Command line
-
hf download hf://deepset/gbert-base-germandpr-question_encoder@c5b8d88c45320a0f8ba714c286ff3bc0af4d72fa/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/deepset/gbert-base-germandpr-question_encoder/resolve/c5b8d88c45320a0f8ba714c286ff3bc0af4d72fa/tokenizer_config.json
327 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": false, "max_len": 512, "special_tokens_map_file": null, "name_or_path": "deepset/gbert-base", "do_basic_tokenize": true, "never_split": null} |