Table Question Answering
Transformers
PyTorch
Safetensors
English
bart
text2text-generation
multitabqa
multi-table-question-answering
Instructions to use vaishali/multitabqa-base-sql with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vaishali/multitabqa-base-sql with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("table-question-answering", model="vaishali/multitabqa-base-sql")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("vaishali/multitabqa-base-sql") model = AutoModelForSeq2SeqLM.from_pretrained("vaishali/multitabqa-base-sql", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from vaishali/multitabqa-base-sql: direct link, hf CLI and curl.
- Browser
- Download file 384 Bytes
-
https://huggingface.co/vaishali/multitabqa-base-sql/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://vaishali/multitabqa-base-sql/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/vaishali/multitabqa-base-sql/resolve/main/tokenizer_config.json
384 Bytes
| { | |
| "add_prefix_space": false, | |
| "bos_token": "<s>", | |
| "cls_token": "<s>", | |
| "eos_token": "</s>", | |
| "errors": "replace", | |
| "mask_token": "<mask>", | |
| "model_max_length": 1024, | |
| "name_or_path": "facebook/bart-base", | |
| "pad_token": "<pad>", | |
| "sep_token": "</s>", | |
| "special_tokens_map_file": null, | |
| "tokenizer_class": "BartTokenizer", | |
| "trim_offsets": true, | |
| "unk_token": "<unk>" | |
| } | |