Zhengbao Jiang
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Browse files- README.md +52 -0
- config.json +38 -0
- merges.txt +0 -0
- pytorch_model.bin +3 -0
- special_tokens_map.json +1 -0
- tokenizer_config.json +67 -0
- vocab.json +0 -0
README.md
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---
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language: en
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tags:
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- tapex
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- table-question-answering
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datasets:
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- wikitablequestions
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---
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# OmniTab
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OmniTab is a table-based QA model proposed in [OmniTab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering](https://arxiv.org/pdf/2207.03637.pdf). The original Github repository is [https://github.com/jzbjyb/OmniTab](https://github.com/jzbjyb/OmniTab).
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## Description
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`neulab/omnitab-large-16shot-finetuned-wtq-16shot` (based on BART architecture) is initialized with `microsoft/tapex-large`, continuously pretrained on natural and synthetic data (SQL2NL model trained in the 16-shot setting), and fine-tuned on [WikiTableQuestions](https://huggingface.co/datasets/wikitablequestions) in the 16-shot setting.
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## Usage
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```python
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
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import pandas as pd
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tokenizer = AutoTokenizer.from_pretrained("neulab/omnitab-large-16shot-finetuned-wtq-16shot")
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model = AutoModelForSeq2SeqLM.from_pretrained("neulab/omnitab-large-16shot-finetuned-wtq-16shot")
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data = {
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"year": [1896, 1900, 1904, 2004, 2008, 2012],
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"city": ["athens", "paris", "st. louis", "athens", "beijing", "london"]
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}
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table = pd.DataFrame.from_dict(data)
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query = "In which year did beijing host the Olympic Games?"
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encoding = tokenizer(table=table, query=query, return_tensors="pt")
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outputs = model.generate(**encoding)
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print(tokenizer.batch_decode(outputs, skip_special_tokens=True))
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# [' 2008']
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```
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## Reference
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```bibtex
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@inproceedings{jiang-etal-2022-omnitab,
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title = "{O}mni{T}ab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering",
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author = "Jiang, Zhengbao and Mao, Yi and He, Pengcheng and Neubig, Graham and Chen, Weizhu",
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booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
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month = jul,
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year = "2022",
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}
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```
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config.json
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{
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"_name_or_path": "neulab/omnitab-large-16shot-finetuned-wtq-16shot",
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"activation_dropout": 0.0,
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"activation_function": "gelu",
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"architectures": [
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"BartForConditionalGeneration"
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],
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"attention_dropout": 0.1,
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"bos_token_id": 0,
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"classifier_dropout": 0.0,
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"d_model": 1024,
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"decoder_attention_heads": 16,
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"decoder_ffn_dim": 4096,
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"decoder_layerdrop": 0.0,
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"decoder_layers": 12,
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"decoder_start_token_id": 2,
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"dropout": 0.1,
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"encoder_attention_heads": 16,
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"encoder_ffn_dim": 4096,
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"encoder_layerdrop": 0.0,
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"encoder_layers": 12,
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"eos_token_id": 2,
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"forced_bos_token_id": 0,
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"forced_eos_token_id": 2,
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"init_std": 0.02,
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"is_encoder_decoder": true,
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"max_length": 1024,
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"max_position_embeddings": 1024,
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"model_type": "bart",
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"num_hidden_layers": 12,
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"pad_token_id": 1,
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"scale_embedding": false,
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"torch_dtype": "float32",
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"transformers_version": "4.17.0.dev0",
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"use_cache": true,
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"vocab_size": 50265,
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"no_repeat_ngram_size": 3
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}
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merges.txt
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:bcadcacbc4645ceffdb15b6c74efd940a29dfc3e61297883bd0cc6219ea11d7d
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size 812847041
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special_tokens_map.json
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{"bos_token": {"content": "<s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, "eos_token": {"content": "</s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, "unk_token": {"content": "<unk>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, "sep_token": {"content": "</s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, "pad_token": {"content": "<pad>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, "cls_token": {"content": "<s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, "mask_token": {"content": "<mask>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true}}
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tokenizer_config.json
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{
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"do_lower_case": false,
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"errors": "replace",
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"bos_token": {
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"content": "<s>",
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"single_word": false,
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"lstrip": false,
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"rstrip": false,
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"normalized": true,
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"__type": "AddedToken"
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},
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"eos_token": {
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"content": "</s>",
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"single_word": false,
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"lstrip": false,
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"rstrip": false,
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"normalized": true,
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"__type": "AddedToken"
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},
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"unk_token": {
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"content": "<unk>",
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"single_word": false,
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"lstrip": false,
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"rstrip": false,
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"normalized": true,
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"__type": "AddedToken"
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},
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"sep_token": {
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"content": "</s>",
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"single_word": false,
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"lstrip": false,
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"rstrip": false,
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"normalized": true,
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"__type": "AddedToken"
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},
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"cls_token": {
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"content": "<s>",
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"single_word": false,
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"lstrip": false,
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"rstrip": false,
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"normalized": true,
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"__type": "AddedToken"
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},
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"pad_token": {
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"content": "<pad>",
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"single_word": false,
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"lstrip": false,
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"rstrip": false,
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"normalized": true,
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"__type": "AddedToken"
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},
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"mask_token": {
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"content": "<mask>",
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"single_word": false,
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"lstrip": true,
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"rstrip": false,
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"normalized": true,
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"__type": "AddedToken"
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},
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"add_prefix_space": false,
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"max_cell_length": 15,
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"model_max_length": 1024,
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"special_tokens_map_file": null,
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"name_or_path": "neulab/omnitab-large-16shot-finetuned-wtq-16shot",
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"use_fast": true,
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"tokenizer_class": "TapexTokenizer"
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}
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vocab.json
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