Instructions to use dccuchile/bert-base-spanish-wwm-cased-finetuned-qa-mlqa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dccuchile/bert-base-spanish-wwm-cased-finetuned-qa-mlqa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="dccuchile/bert-base-spanish-wwm-cased-finetuned-qa-mlqa")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("dccuchile/bert-base-spanish-wwm-cased-finetuned-qa-mlqa") model = AutoModelForQuestionAnswering.from_pretrained("dccuchile/bert-base-spanish-wwm-cased-finetuned-qa-mlqa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| {"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, "name_or_path": "dccuchile/bert-base-spanish-wwm-cased", "do_basic_tokenize": true, "never_split": null, "model_max_length": 512, "special_tokens_map_file": "/data/jcanete/cache/9848a00af462c42dfb4ec88ef438fbab5256330f7f6f50badc48d277f9367d49.f982506b52498d4adb4bd491f593dc92b2ef6be61bfdbe9d30f53f963f9f5b66", "tokenizer_class": "BertTokenizer"} |