Instructions to use deutsche-telekom/bert-multi-english-german-squad2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deutsche-telekom/bert-multi-english-german-squad2 with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "question-answering" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("question-answering", model="deutsche-telekom/bert-multi-english-german-squad2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("deutsche-telekom/bert-multi-english-german-squad2") model = AutoModelForQuestionAnswering.from_pretrained("deutsche-telekom/bert-multi-english-german-squad2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from deutsche-telekom/bert-multi-english-german-squad2: direct link, hf CLI and curl.
- Browser
- Download file 264 Bytes
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https://huggingface.co/deutsche-telekom/bert-multi-english-german-squad2/resolve/main/tokenizer_config.json
- Command line
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hf download hf://deutsche-telekom/bert-multi-english-german-squad2/tokenizer_config.json
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curl -L -o tokenizer_config.json https://huggingface.co/deutsche-telekom/bert-multi-english-german-squad2/resolve/main/tokenizer_config.json
264 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": null, "model_max_length": 512, "name_or_path": "bert-base-multilingual-cased"} |