Instructions to use lysandre/tiny-tapas-random-wtq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lysandre/tiny-tapas-random-wtq with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("table-question-answering", model="lysandre/tiny-tapas-random-wtq")# Load model directly from transformers import AutoTokenizer, AutoModelForTableQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("lysandre/tiny-tapas-random-wtq") model = AutoModelForTableQuestionAnswering.from_pretrained("lysandre/tiny-tapas-random-wtq") - Notebooks
- Google Colab
- Kaggle
Commit ·
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Parent(s): 47f9a4d
Tokenizer
Browse files- special_tokens_map.json +1 -0
- tokenizer_config.json +1 -0
- vocab.txt +0 -0
special_tokens_map.json
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{"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "additional_special_tokens": ["[EMPTY]"]}
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tokenizer_config.json
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{"do_lower_case": true, "do_basic_tokenize": true, "never_split": null, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "empty_token": "[EMPTY]", "tokenize_chinese_chars": true, "strip_accents": null, "cell_trim_length": -1, "max_column_id": null, "max_row_id": null, "strip_column_names": false, "update_answer_coordinates": false, "drop_rows_to_fit": false, "model_max_length": 512, "additional_special_tokens": ["[EMPTY]"], "name_or_path": "nielsr/tapas-base-finetuned-wtq"}
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vocab.txt
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