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127
README.md
127
README.md
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@ -1,64 +1,65 @@
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---
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language: en
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tags:
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- tapex
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license: apache-2.0
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---
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# TAPEX (base-sized model)
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TAPEX was proposed in [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://arxiv.org/abs/2107.07653) by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou. The original repo can be found [here](https://github.com/microsoft/Table-Pretraining).
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## Model description
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TAPEX (**Ta**ble **P**re-training via **Ex**ecution) is a conceptually simple and empirically powerful pre-training approach to empower existing models with *table reasoning* skills. TAPEX realizes table pre-training by learning a neural SQL executor over a synthetic corpus, which is obtained by automatically synthesizing executable SQL queries.
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TAPEX is based on the BART architecture, the transformer encoder-encoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder.
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## Intended Uses
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You can use the raw model for simulating neural SQL execution, i.e., employ TAPEX to execute a SQL query on a given table. However, the model is mostly meant to be fine-tuned on a supervised dataset. Currently TAPEX can be fine-tuned to tackle table question answering tasks and table fact verification tasks. See the [model hub](https://huggingface.co/models?search=tapex) to look for fine-tuned versions on a task that interests you.
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### How to Use
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Here is how to use this model in transformers:
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```python
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from transformers import TapexTokenizer, BartForConditionalGeneration
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import pandas as pd
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tokenizer = TapexTokenizer.from_pretrained("microsoft/tapex-base")
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model = BartForConditionalGeneration.from_pretrained("microsoft/tapex-base")
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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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# tapex accepts uncased input since it is pre-trained on the uncased corpus
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query = "select year where city = beijing"
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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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### How to Fine-tuning
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Please find the fine-tuning script [here](https://github.com/SivilTaram/transformers/tree/add_tapex_bis/examples/research_projects/tapex).
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### BibTeX entry and citation info
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```bibtex
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@inproceedings{
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liu2022tapex,
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title={{TAPEX}: Table Pre-training via Learning a Neural {SQL} Executor},
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author={Qian Liu and Bei Chen and Jiaqi Guo and Morteza Ziyadi and Zeqi Lin and Weizhu Chen and Jian-Guang Lou},
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booktitle={International Conference on Learning Representations},
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year={2022},
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url={https://openreview.net/forum?id=O50443AsCP}
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}
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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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license: mit
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---
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# TAPEX (base-sized model)
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TAPEX was proposed in [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://arxiv.org/abs/2107.07653) by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou. The original repo can be found [here](https://github.com/microsoft/Table-Pretraining).
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## Model description
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TAPEX (**Ta**ble **P**re-training via **Ex**ecution) is a conceptually simple and empirically powerful pre-training approach to empower existing models with *table reasoning* skills. TAPEX realizes table pre-training by learning a neural SQL executor over a synthetic corpus, which is obtained by automatically synthesizing executable SQL queries.
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TAPEX is based on the BART architecture, the transformer encoder-decoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder.
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## Intended Uses
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You can use the raw model for simulating neural SQL execution, i.e., employ TAPEX to execute a SQL query on a given table. However, the model is mostly meant to be fine-tuned on a supervised dataset. Currently TAPEX can be fine-tuned to tackle table question answering tasks and table fact verification tasks. See the [model hub](https://huggingface.co/models?search=tapex) to look for fine-tuned versions on a task that interests you.
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### How to Use
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Here is how to use this model in transformers:
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```python
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from transformers import TapexTokenizer, BartForConditionalGeneration
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import pandas as pd
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tokenizer = TapexTokenizer.from_pretrained("microsoft/tapex-base")
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model = BartForConditionalGeneration.from_pretrained("microsoft/tapex-base")
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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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# tapex accepts uncased input since it is pre-trained on the uncased corpus
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query = "select year where city = beijing"
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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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### How to Fine-tuning
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Please find the fine-tuning script [here](https://github.com/SivilTaram/transformers/tree/add_tapex_bis/examples/research_projects/tapex).
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### BibTeX entry and citation info
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```bibtex
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@inproceedings{
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liu2022tapex,
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title={{TAPEX}: Table Pre-training via Learning a Neural {SQL} Executor},
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author={Qian Liu and Bei Chen and Jiaqi Guo and Morteza Ziyadi and Zeqi Lin and Weizhu Chen and Jian-Guang Lou},
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booktitle={International Conference on Learning Representations},
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year={2022},
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url={https://openreview.net/forum?id=O50443AsCP}
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}
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```
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112
config.json
112
config.json
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@ -1,73 +1,39 @@
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{
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"_name_or_path": "bart-base",
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"activation_dropout": 0.0,
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"activation_function": "gelu",
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"add_bias_logits": false,
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"add_final_layer_norm": false,
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"architectures": [
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"BartModel"
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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": 768,
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"decoder_attention_heads": 12,
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"decoder_ffn_dim": 3072,
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"decoder_layerdrop": 0.0,
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"decoder_layers": 6,
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"decoder_start_token_id": 2,
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"dropout": 0.1,
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"early_stopping": true,
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"encoder_attention_heads": 12,
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"encoder_ffn_dim": 3072,
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"encoder_layerdrop": 0.0,
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"encoder_layers": 6,
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"eos_token_id": 2,
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"forced_eos_token_id": 2,
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"gradient_checkpointing": false,
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"id2label": {
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"0": "LABEL_0",
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"1": "LABEL_1",
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"2": "LABEL_2"
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},
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"init_std": 0.02,
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"is_encoder_decoder": true,
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"label2id": {
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"LABEL_0": 0,
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"LABEL_1": 1,
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"LABEL_2": 2
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},
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"max_position_embeddings": 1024,
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"model_type": "bart",
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"no_repeat_ngram_size": 3,
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"normalize_before": false,
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"normalize_embedding": true,
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"num_beams": 4,
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"num_hidden_layers": 6,
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"pad_token_id": 1,
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"scale_embedding": false,
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"task_specific_params": {
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"summarization": {
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"length_penalty": 1.0,
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"max_length": 128,
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"min_length": 12,
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"num_beams": 4
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},
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"summarization_cnn": {
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"length_penalty": 2.0,
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"max_length": 142,
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"min_length": 56,
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"num_beams": 4
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},
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"summarization_xsum": {
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"length_penalty": 1.0,
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"max_length": 62,
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"min_length": 11,
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"num_beams": 6
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}
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},
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"torch_dtype": "float32",
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"transformers_version": "4.12.0.dev0",
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"use_cache": true,
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"vocab_size": 50265
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}
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{
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"_name_or_path": "microsoft/tapex-base",
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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": 768,
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"decoder_attention_heads": 12,
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"decoder_ffn_dim": 3072,
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"decoder_layerdrop": 0.0,
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"decoder_layers": 6,
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"decoder_start_token_id": 2,
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"dropout": 0.1,
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"encoder_attention_heads": 12,
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"encoder_ffn_dim": 3072,
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"encoder_layerdrop": 0.0,
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"encoder_layers": 6,
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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_position_embeddings": 1024,
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"model_type": "bart",
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"normalize_before": false,
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"normalize_embedding": true,
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"num_beams": 4,
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"num_hidden_layers": 6,
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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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}
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{
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"_from_model_config": true,
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"bos_token_id": 0,
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"decoder_start_token_id": 2,
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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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"num_beams": 4,
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"pad_token_id": 1,
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"transformers_version": "4.27.0.dev0"
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}
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{"do_lower_case": true, "errors": "replace", "bos_token": {"content": "<s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "eos_token": {"content": "</s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "unk_token": {"content": "<unk>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "sep_token": {"content": "</s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "cls_token": {"content": "<s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "pad_token": {"content": "<pad>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "mask_token": {"content": "<mask>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "add_prefix_space": false, "max_cell_length": 15, "model_max_length": 1024, "special_tokens_map_file": null, "name_or_path": "microsoft/tapex-base", "tokenizer_class": "TapexTokenizer"}
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