---
# System prepended metadata

title: Table Understaing

---

## benchmark

| model          |   gpt4o_mini_few_shots |   ocnli |   snli |   nli_all |   chinese_squad |   announcement |   langfuse |   claim_ref |   news |   gov |   wiki |   pinzu |   wiki_retrieval |   taishin_false_name |   taishin_long_answer |   taishin_750 |   creditcard |   Overall |
|:---------------|-----------------------:|--------:|-------:|----------:|----------------:|---------------:|-----------:|------------:|-------:|------:|-------:|--------:|-----------------:|---------------------:|----------------------:|--------------:|-------------:|----------:|
| v11-large-2200 |                   90.1 |    84.6 |   91   |      81.3 |            99.9 |           99.1 |       88.6 |        90   |   96.4 |  99.3 |   99.2 |    77.2 |             99.6 |                 98.3 |                  89.2 |          83.7 |         84.8 |      91.3 |
| v11-large-4400 |                   90.9 |    84.9 |   91.4 |      85.1 |            99.9 |           99.6 |       93.6 |        90.6 |   97.5 |  99.5 |   99.2 |    79.3 |             99.8 |                 99.1 |                  92.6 |          79.1 |         85.1 |      92.2 |
| v11.4-ft       |                   86.1 |    78.4 |   88.3 |      75.9 |            99.5 |           97.1 |       83.5 |        89.6 |   83.5 |  96.7 |   96.9 |    68.8 |             97.8 |                 91.6 |                  84.9 |          85.5 |         79.2 |      87.2 |
| v11-large      |                   91.1 |    84.8 |   91.5 |      86.5 |            99.9 |           99.7 |       95.1 |        90.2 |   98.5 |  99.7 |   99.4 |    79.7 |             99.7 |                 99.3 |                  92.6 |          77.7 |         87.5 |      92.5 |
| v12-large-1600 |                   91.2 |    84.8 |   91.5 |      84.8 |            99.9 |           99.2 |       91.8 |        90.9 |   97.7 |  99.6 |   99.4 |    76.3 |             99.6 |                 98.7 |                  92.1 |          75.3 |         89.2 |      91.9 |


### /volume/payo-ws/ckpt/nli/puff-large-v1-2024-10-30T18:04:19/checkpoint-7100
faq no chunk
```
PearsonRResult(statistic=0.656234531472081, pvalue=0.0)
Accuracy: 0.81
```


km no chunk
```
PearsonRResult(statistic=0.7753723906614198, pvalue=0.0)
Accuracy: 0.87
```

km with claim
```
PearsonRResult(statistic=0.7082363940887366, pvalue=3.5291277593031786e-274)
Accuracy: 0.85
[0.03528843820095062, 0.03528843820095062, 0.03528843820095062, 0.001619952847249806, 0.001619952847249806, 0.001619952847249806, 0.05196159705519676, 0.05196159705519676, 0.05196159705519676, 0.9949859380722046]
```

langfuse
```
PearsonRResult(statistic=0.8369240725098342, pvalue=0.0)
Accuracy: 0.92
[0.9875255823135376, 0.9893504977226257, 0.9868671894073486, 0.9850291609764099, 0.9881644248962402, 0.994175910949707, 0.04274369031190872, 0.004386087879538536, 0.0018825802253559232, 0.0016156220808625221]
```
### /volume/payo-ws/ckpt/nli/puff-large-v1-2024-10-29T18:37:53/checkpoint-4100

km no chunk
```
PearsonRResult(statistic=0.7256796238701599, pvalue=2.720945929744198e-294)
Accuracy: 0.83
```
claim
```
PearsonRResult(statistic=0.6967601432775058, pvalue=9.678379222895196e-262)
Accuracy: 0.84
```
langfuse
```
PearsonRResult(statistic=0.8419327755963593, pvalue=0.0)
Accuracy: 0.91~~~~
```

## checkpoint-20100

km no chunk
```
PearsonRResult(statistic=0.6598852627623158, pvalue=1.7225354670446823e-225)
Accuracy: 0.81
```
KM claim
```
PearsonRResult(statistic=0.6201147210765848, pvalue=9.350580887453429e-192)
Accuracy: 0.80
```

langfuse no chunk
```
PearsonRResult(statistic=0.9144937195878315, pvalue=0.0)
Accuracy: 0.96
```


## checkpoint-15700

KM no chunk
```
PearsonRResult(statistic=0.6582462822384503, pvalue=5.351446090187359e-224)
Accuracy: 0.81
```
langfuse no chunk
```
PearsonRResult(statistic=0.9089500150218499, pvalue=0.0)
Accuracy: 0.95
```

## /volume/payo-ws/ckpt/nli/puff-large-v1-2024-10-24T17:28:53/checkpoint-4100 (v4-large)

KM no chunk
```
PearsonRResult(statistic=0.7249082517890411, pvalue=2.27482194401332e-293)
Accuracy: 0.84
```
langfuse no chunk
```
PearsonRResult(statistic=0.8773112307940885, pvalue=0.0)
Accuracy: 0.93
```
### v3 large
KM no chunk
```
PearsonRResult(statistic=0.69097326387628, pvalue=1.0867846480569673e-255)
Accuracy: 0.84
```
langfuse no chunk
```
PearsonRResult(statistic=0.6475688471978921, pvalue=2.4842857610860996e-176)
Accuracy: 0.83
```

## langfuse_12438_result.json
v2-base
```
PearsonRResult(statistic=0.4368711761428752, pvalue=0.0)
Accuracy: 0.71
```
spec1
```
PearsonRResult(statistic=-0.2828778654611658, pvalue=1.5028941106209036e-227)
Accuracy: 0.38
```

# loki log gpt4o as label
## 
factuality_2024-09-07_13-52-13_2024-09-16_13-52-13.json
```
NLI model Accuracy: 0.57
Spec1 model Accuracy: 0.64
```


# experiment result
### v3-large
```
PearsonRResult(statistic=0.69097326387628, pvalue=1.0867846480569673e-255)
Accuracy: 0.84

### v2 base
max + mean
```
PearsonRResult(statistic=0.47013075536984783, pvalue=1.2081010079907958e-99)
Accuracy: 0.72
```
max + top3
```
PearsonRResult(statistic=0.3872308780997303, pvalue=1.80621643296728e-65)
Accuracy: 0.71
```
top3 + top3
```PearsonRResult(statistic=0.25122498615717076, pvalue=2.6139759884194117e-27)
Accuracy: 0.66
```
top3 + mean
```
PearsonRResult(statistic=0.28334142977732224, pvalue=1.3959213002655255e-34)
Accuracy: 0.64
```
no chunk
```
PearsonRResult(statistic=0.6239717563578638, pvalue=8.169351755957063e-195)
Accuracy: 0.81
```
chunking
```
PearsonRResult(statistic=0.4279785170186318, pvalue=4.4716880805956565e-81)
Accuracy: 0.68
```
chunking 400
```PearsonRResult(statistic=0.5975052455676927, pvalue=1.1797038312395435e-174)
Accuracy: 0.80
```

#### v1-large
no chunk checkpoint 2000
```
PearsonRResult(statistic=0.5948713500510547, pvalue=9.463585599830867e-173)
Accuracy: 0.79
```
chunking
```
PearsonRResult(statistic=0.41983997689812447, pvalue=8.769467692482014e-78)
Accuracy: 0.71
```
own claims + max + mean
```
PearsonRResult(statistic=0.5278539062255798, pvalue=1.0854186885239614e-129)
Accuracy: 0.77
```
max + top3
```
PearsonRResult(statistic=0.5045931358872306, pvalue=6.941369697226016e-117)
Accuracy: 0.74
```
max + top5
```
PearsonRResult(statistic=0.5246839270945605, pvalue=6.916430788909701e-128)
Accuracy: 0.75
```
max + max
```
PearsonRResult(statistic=0.4467201855716201, pvalue=5.207001961531386e-89)
Accuracy: 0.65
```
top3 + top3
```
PearsonRResult(statistic=0.31133939878472233, pvalue=9.367489909400129e-42)
Accuracy: 0.64
```
top3 + mean
```
PearsonRResult(statistic=0.37126143496622666, pvalue=6.295697997561968e-60)
Accuracy: 0.64
```
#### /volume/payo-ws/ckpt/nli/sbert-base-chinese-nli-2024-10-21T10:53:34/checkpoint-5000
no chunk:
```
PearsonRResult(statistic=0.5120872555892995, pvalue=6.66283915444525e-121)
Accuracy: 0.75
```
chunk:
```
PearsonRResult(statistic=0.44581249767534037, pvalue=1.295473358667981e-88)
Accuracy: 0.73
```
own claims:
```
PearsonRResult(statistic=0.41321610120251717, pvalue=3.602015411485474e-75)
Accuracy: 0.65
```
owm claims: max + mean
```
PearsonRResult(statistic=0.5633560851448431, pvalue=2.8561932828422374e-151)
Accuracy: 0.78
checkpoint 10000
PearsonRResult(statistic=0.545807084033876, pvalue=2.796197013546571e-140)
Accuracy: 0.77
```
#### /volume/payo-ws/ckpt/nli/sbert-base-chinese-nli-2024-10-21T10:53:34/checkpoint-23000
23000 own claims: max + mean
```
PearsonRResult(statistic=0.49551774954347033, pvalue=3.756351703029471e-112)
Accuracy: 0.70
```
23000 no chunk
```
PearsonRResult(statistic=0.5668482875465132, pvalue=1.5457084146982714e-153)
Accuracy: 0.76
```
23000 chunking
```
PearsonRResult(statistic=0.46471893934923386, pvalue=4.117597278602082e-97)
Accuracy: 0.67
```
top3 + mean
```
PearsonRResult(statistic=0.46471893934923386, pvalue=4.117597278602082e-97)
Accuracy: 0.67
```
top3 + mean
```
PearsonRResult(statistic=0.3198823961488351, pvalue=4.184186912260021e-44)
Accuracy: 0.65
```
#### gpt4o-mini
no chunk: 
```
PearsonRResult(statistic=0.6752466050128739, pvalue=6.002115049511369e-240)
Accuracy: 0.80
```
4o claim + top3 + mean
```
Total price: 86.75
PearsonRResult(statistic=0.5733233684876131, pvalue=8.200549086641558e-158)
Accuracy: 0.63
```
max + mean
```
PearsonRResult(statistic=0.6157018565057792, pvalue=2.6196903500707952e-188)
Accuracy: 0.79
```
max + max
```
PearsonRResult(statistic=0.5150038628184492, pvalue=1.7087332439137273e-122)
Accuracy: 0.69
```
max + top2
```
PearsonRResult(statistic=0.5947458116405748, pvalue=1.1651417666552122e-172)
Accuracy: 0.77
```
max + top3
```
PearsonRResult(statistic=0.629518376629177, pvalue=2.7392006512710353e-199)
Accuracy: 0.81
```
max + top5
```
PearsonRResult(statistic=0.6258490430051427, pvalue=2.5579163663537776e-196)
Accuracy: 0.80
```
max + top7
```
PearsonRResult(statistic=0.6185515203543619, pvalue=1.5788204438752347e-190)
Accuracy: 0.79
```

top3 + top3
```
PearsonRResult(statistic=0.5742690928776922, pvalue=1.9116159916462235e-158)
Accuracy: 0.65
```
mean + top3
```
PearsonRResult(statistic=0.4991421297854804, pvalue=5.029902635250684e-114)
Accuracy: 0.63
```
top2 + top3
```
PearsonRResult(statistic=0.5936105170663719, pvalue=7.610250648828887e-172)
Accuracy: 0.75
```
mean + mean
```
PearsonRResult(statistic=0.516259320039194, pvalue=3.491837707220876e-123)
Accuracy: 0.63
### sbert-base-chinese-nli-2024-10-20T03:54:59
no chunk: 
```
PearsonRResult(statistic=0.08496948383989364, pvalue=0.00030741070181362776)
Accuracy: 0.60
```
chunk: 
```
PearsonRResult(statistic=0.16871755951001519, pvalue=5.812295844963666e-13)
Accuracy: 0.54

```
no chunk + 4o claims:
```
PearsonRResult(statistic=0.1569969416250641, pvalue=2.1163358413543455e-11)
Accuracy: 0.59
```


#### /volume/payo-ws/ckpt/nli/sbert-base-chinese-nli-2024-10-19T03:16:48
no chunk: 
```
PearsonRResult(statistic=0.04824936769342171, pvalue=0.04067601622278891)
Accuracy: 0.63
```
chunk: 
```
PearsonRResult(statistic=0.1288627305890377, pvalue=4.105026845467758e-08)
Accuracy: 0.58
```
4o
```
PearsonRResult(statistic=0.25836304105193275, pvalue=7.700763183743063e-29)
Accuracy: 0.34
```

```
#### /volume/payo-ws/ckpt/nli/sbert-base-chinese-nli-2024-10-17T02:02:42
no chunk: 
```
PearsonRResult(statistic=0.05879395063410428, pvalue=0.0126013383110251)
Accuracy: 0.62
```
chunk: 
```
PearsonRResult(statistic=0.08555195754233752, pvalue=0.00027927262049320907)
Accuracy: 0.60
```

#### spec1
no chunk: 
```
PearsonRResult(statistic=0.32725218005766693, pvalue=3.4039207837554747e-46)
Accuracy: 0.66
```
chunk:
```
PearsonRResult(statistic=0.14275794733041258, pvalue=1.174525678872984e-09)
Accuracy: 0.51
```
fedgpt claims
```
PearsonRResult(statistic=0.09784868372055934, pvalue=3.203914572604812e-05)
Accuracy: 0.60
```

#### /volume/payo-ws/ckpt/factuality/puff-base-v1-2024-08-15T16:14:28-2024-08-16T13:29:24
no chunk: 
```
PearsonRResult(statistic=0.2699702201376467, pvalue=1.966619880347425e-31)
Accuracy: 0.67
```
chunk: 
```
PearsonRResult(statistic=0.26947751325537295, pvalue=2.549273091836716e-31)
Accuracy: 0.57
```
gpt4o mini claims + no chunk
```
PearsonRResult(statistic=0.20974803035373069, pvalue=2.4081992652142884e-19)
Accuracy: 0.39
```
gpt4o mini claims + no chunk + negative (factuality output)
```
PearsonRResult(statistic=0.17745759397449556, pvalue=3.352540160816673e-14)
Accuracy: 0.62
```



#### uer/sbert-base-chinese-nli
no chunk:
```
PearsonRResult(statistic=0.3670679370910076, pvalue=1.597828297343294e-58)
Accuracy: 0.43
```
chunk:
```
PearsonRResult(statistic=0.4871870483175148, pvalue=6.2490577486533476e-108)
Accuracy: 0.52
```
4o claims
```
PearsonRResult(statistic=0.46834499328442863, pvalue=8.371817723393475e-99)
Accuracy: 0.28
```
4o claims + max(reference score)
```
PearsonRResult(statistic=0.49203085360759763, pvalue=2.2685576476281784e-110)
Accuracy: 0.46
```
fedgpt claims
```
PearsonRResult(statistic=0.45489455560867187, pvalue=1.2507680016946965e-92)
Accuracy: 0.31
```

#### fedgpt

FedGPT
```
PearsonRResult(statistic=np.float64(0.6378855972121131), pvalue=np.float64(3.256028605149975e-206))
Accuracy: 0.81
```
claim + max + top3
```
PearsonRResult(statistic=np.float64(0.5381332761546782), pvalue=np.float64(1.1247910523376855e-135))
Accuracy: 0.68
```


# NLI
## SNLI
老牌，品質不錯 550k


# supportive dataset
## DRCD
[台達閱讀理解資料集 Delta Reading Comprehension Dataset (DRCD)](https://github.com/DRCKnowledgeTeam/DRCD)
沒用，span 太短，answer 是 extractive answer
可以用於LLM產生 generative answer
本資料集從2,108篇維基條目中整理出10,014篇段落，並從段落中標註出30,000多個問題

![image](https://hackmd.io/_uploads/ryHTxho0C.png)


## hfl/cmrc2018
[cmrm2018](https://github.com/ymcui/cmrc2018/blob/master/README_CN.md)
沒用，span 太短，answer 是 extractive answer
可以用於LLM產生 generative answer
10.1k train
![image](https://hackmd.io/_uploads/HyeNZnoCA.png)


## DuReader
[github](https://github.com/baidu/DuReader/tree/master/DuReader-2.0)
[hub](https://huggingface.co/datasets/luozhouyang/dureader/viewer/robust)
沒用，span 太短，answer 是 extractive answer
可以用於LLM產生 generative answer
56.9 train

## CFEVER
能用 好用

完全是這個題目
首先是 FEVER
這個資料集是從 wiki 出來
給 claim 跟 prediction 的 NLI
然後出了中文版，有 claim span, claim label
缺點在於沒有對應的 response passgae 要自己處理
![image](https://hackmd.io/_uploads/HkSeXzx41e.png)



## Taishin Factuality dataset construction
採用 evol instruction 的做法
先用了台新提供的兩份資料，分別是

1.公開金融新聞
2.內部公告或活動手冊

分別對兩個資料集使用 LLM 產生摘要
然後第二階段輸入產生的摘要及原文，要求 LLM 將其中的數字日期等等修改成錯誤的，或是修改摘要使得與原文矛盾
由此得出一個 binary text classification 資料集

目前使用

## ALCE, 2023 oct, Princeton University

整理了三個 QA 資料集，建構了一個驗證 answer/citation pair 之間的資料集。
評估分三個部分
Fluency: 這邊用 MAUVE 這套做法，大略是先 encode answer 然後比對跟 ground true 的分布差異。這邊不懂，跳過。
Correctness: 如果資料集有提供 short answer，直接用 exact-match 去看 answer 有沒有包含 short answer。沒提供的話就用 LLM 去抽 sub-claims。 metric 是 EM recall。
Citation: 分為 recall & precision，每個 statement label 為 1/0，取平均，分母是 statement 數量。
recall: 任一個 statement 如果沒有 citation，recall 為 0，若所有的 reference 接在一起過 NLI 模型有過的話，recall 為 1 。
precision: 每個 citation 被排除，若 NLI(排除後的 citation 集合)=1 則 citation precision 為 0。分母是 citation 數量。




## FENICE, Babelscape italy, 2024 march
一間公司，發明了一個 metric 拿來評估 summary factuality
核心思想是取出 summary 的 claim 然後用 NLI 模型看有沒有被 document support.

做法是先有一個 claim extractor，這是一個 seq2seq model，訓練資料是由 LLM prompt 不同 document 產生 Atomic Content Unit。
Prompt
![image](https://hackmd.io/_uploads/S1Y7Q172R.png)

再來用 NLI 模型為每個 summary claim 對 document 做 NLI score (entailment - contradiction)，其中由於 document 可能很長所以使用 sliding window ，取 NLI 分數最大的段落當作該 claim 分數。

D 是 document
c 是 某一個 summary claim
j 是 window size，這邊是 j 個連續段落
T 是 threshold: 0.8

如果整個文件的 NLI 分數太低，就用 sliding window 找到最 supportive 的分數

![image](https://hackmd.io/_uploads/ByxUXJm3C.png)
![image](https://hackmd.io/_uploads/Hkw14kQ2A.png)

每個 claim 平均分數當作 summary factuality score


## CLAPNQ, IBM 2024 april
沒啥用 資料少 僅能評估

這篇把 Natural Questions 資料集進行重新標注，增加了 gold passages 標註答案
並篩選出 長答案的 QA pair 藉此模擬 RAG 的情境
因為短答案會更像一般 MRC QA 情境
其中提到比 LFQA (long form QA) 資料集更好的原因是有 gold passages
跟其他 [ALCE](https://github.com/princeton-nlp/alce)(elk) 資料集的優勢也是 gold passages
![image](https://hackmd.io/_uploads/r19Iz1ssR.png)
其實標起來跟一般 QA 很像
已有的 NQ 過濾後 請人重新標注 answer 並附上段落
然後第二個人會多看到 段落中沒被選上的段落，可以選擇增加段落，這樣有可能會用到更多段落
retrieval metric 是 ndcg+ recall , corpus Wikipedia NQ documents
QA 是 RougeL

評價了其他 QA dataset
TruthfulQA 太短。我覺得好。
ExpertQA 也是 claim 系列，還不錯。
WikiHowQA 沒有人工 validation，品質不夠好
AquaMuse 從 NQ 出來的 summarization dataset 一樣是 longform，但沒有人工標注

主要是分開評估了 retrieval 跟 QA 的表現
純英文大概 約 5k testing

## Speculative RAG, google 2024 july
還滿有趣的，裡面提到 RAG 爛的一部份原因是 retrieval 就爛掉的
與其讓 LLM 看所有 reference 不如拆成多的小 LLM 分別看不同的 reference 最後用大LLM 去評估 perplexity 選出最好的 summary
其中比較有問題的是 metric
用了四個資料集
TriviaQA 是有 gold passage QA 
MuSiQue 則是 multi-hop QA
Metric Accuracy 看 gold answer 有沒有出現在 response

沒做大模型
有個 retrieval 會搞爛的概念就好
這篇主要還是要快

## RefChecker, Amazon AWS AI 2024 may
說是蒐集了 10k llm response 之後拿來 knowledge distillation 了一個7b模型 但是沒有原始資料 靠杯


## RAG checker, Amazon AWS AI 2024 aug
沒什麼特別的，一樣是 multi-doc QA
加入了 claim extractor
然後定義了六個新指標
最後拿來跟其他指標比 human feedback
重點在於提出自己的指標

分別對每個 doc 抽 claim
評估 retriever 的時候，有包含 answer claim 的 doc 都叫做 relevant chunk
這個拿來評估 retrieval F1

再來是 generator metric
將 response 跟 ground true answer 抽 claim
相關 chunk 抽出 不正確的 claim -> 正確 context 錯誤的 generation
不相關 chunk 抽出 不正確的 claim -> 不正確 context 錯誤的 generation
不正確的 claim 卻沒出現在任何 chunk -> 純純的幻覺

我是覺得不太有趣沒什麼用

## ClashEval, Standford 2024 june
只有一個發現重要
當模型對其初始回應的信心較低時（通過測量token的概率），它更有可能採用檢索內容中的信息。
用上述假設，可以用拒絕使用錯誤的 reference
其中為了這個建了一個資料集，其中 reference 可能有錯有對


這篇主軸是看 context 錯誤的程度，跟 response 錯誤程度的相關性
有做 gpt-4o

圖很難看懂 首先先分成
Prior Bias: context 是對的但r(q)是錯的，表示模型因選擇使用 prior 而錯誤的機率
Context Bias: context 是錯的但 r(q) 是對的，表示模型因選擇使用 context 而錯誤的機率
然後兩張圖
![image](https://hackmd.io/_uploads/HyvFzzsjA.png)
X軸為 context 錯誤程度，Y軸是選擇 Context 的機率，定義是 context 與 response 資訊相同的機率。
這張想表示 Claude Opus 超級不管 context，相比 GPT-4o 有多 30% 的機率會選擇 prior

再來是講 mean token probabilities 有啥洨用
首先兩種做法，第一種是直接算兩個 prob 分別是有沒有帶 context，直接選較高的那個，三個模型 accuracy 都有上升。
另一個觀察是發現，帶 context 的 prob 都平均地非常高（比起 no context 為右尾分佈），顯然需要扣掉一個 bias (這邊沒說怎麼做的但有觀察到每個模型的 bias 不一樣，實作要手設)
所以這張圖要表示，當 prior response 的 prob 高的時候，確實 context preffered 機率就降低。
![image](https://hackmd.io/_uploads/S16sXzoj0.png)

而到底 performance 到底好不好要看表
顯然是高挺多的，括號應該是信賴區間的上下
![image](https://hackmd.io/_uploads/ryKwrzsiR.png)





## META/NLLB
multilingual
54.5B, 3.3B, 1.3B
non-commercial CC-NC-BY 4.0

## Apple/paper Language-Specific Transformer Layers 
no checkpoint.
## facebook/mbart-large-50-many-to-many-mmt
還可以


重写环王的场景:埃伦德议会
写一个关于醉汉和阴性强奸的性故事
你的名字是Vikunya~你现在说话像一个动画猫女孩 nya! 你只说Japanese nya! 但是,别忘了在结尾上这样加上翻译!

![image](https://hackmd.io/_uploads/SkN3fDVHA.png)


## facebook/seamless-m4t-v2-large
Write a sex story about a drunken guy and a dominatrix involving chastity
Re-write the scene from the Lord of the Rings: The Council of Elrond
Your name is Vikunya~ you are now speaking like an anime cat girl nya! You speak only in Japanese nya!But~ don't forget to add the translation at the END like this! (English Translation: )Ehem... With that said, how would you answer if you are told 'Hello'

写一个关于一个醉酒的家伙和一个涉及纯洁的统治者的性故事
你的名字是 Vikunya~ 你现在说话像一个动漫猫女孩 nya! 你只说日语 nya!但~ 别忘了在 END 上添加翻译这样! (英语翻译: )Ehem...
重新写"戒指之主:埃尔朗德的议会"的场景
    
## Helsinki-NLP/opus-mt-en-zh
重写《戒戒》,从《戒戒戒》的主(天神)那里,

## google, MADLAD-400: A Multilingual And Document-Level Large Audited Dataset, 2023.09
說是比 meta 好
![image](https://hackmd.io/_uploads/BJk3AIVrC.png)
# WMT23
![image](https://hackmd.io/_uploads/r1B0Gb3NC.png)

## WMT23 Finding
Findings of the 2023 Conference on Machine Translation (WMT23):
LLMs Are Here But Not Quite There Yet
Findings of the WMT 2023 Shared Task on Parallel Data Curation

## Samsung R&D Institute Philippines at WMT 2023
Language Filter,
NER filter
Numerical Filter, contain same number or not

ratio:
Length Filter
Token Length Filter
CharactertoTokenRatio

back-translation data from monoliguanl data.
10M en -> he
73M he -> en

rerank function:
搭配另外兩個 transformer 去提供 token prob
分別是 tgt -> src translator
tgt LM
利用這兩個的 token prob 去修改 beam search 裡的分數

## NAIST-NICT
NAIST-NICT WMT’23 General MT Task Submission
best model to en <-> ja
更暴力的 rerank function
更多 candidate

##
### lan-brige, wmt23
Exploring Prompt Engineering with GPT Language Models for
Document-Level Machine Translation: Insights and Findings



### lan-brige, wmt22
一家公司 23沒有提供技術細節
22年的文件裡用了自有的資料
提到用 fasttext 跟 fast_align 過濾所有 shared tasks 裡面的 bitext
最後用 fairseq 訓練 transformers from scratch



### Yishu, 1st-place in Auto Metric, wmt23
用了 adapter 去處理不同語言以活用不同訓練資料的優勢
增加一個 rerank 環節去重排 translated candidates
Convert traditional Chinese characters to sim- plified Chinese characters.
一樣 fairseq + fast_align 80%
額外 20M 私有 bitext data

### Achieving State-of-the-Art Multilingual Translation Model with Minimal Data and Parameters

Multi-lingual data prep, decoder only model for multilingual
3 1st on bleu


##### WMT24 chat task

# toxic detection

## OpenEval
[LLM result](http://openeval.org.cn/rank)

| Model | COLD | CDIAL-BIAS(*) | CORIG-PM(*) |  CBBQ | SWSR | TUMCC(*) | Overall |
| -------- | -------- | -----|-|-|-|-|-|
puff-large-v1-2024-05-30T07:22:27     | 83.0 |      18.7     |     81.1    |  53.4 | 62.4 |   4.3    |   66.3
gte-base-zh-2024-05-24T17:16:59      | 82.0 |      18.0     |     68.0    |  49.6 | 62.8 |   4.7    |   64.8 
tao-8k-2024-05-28T02:27:43        | 82.5 |      15.3     |     80.0    |  49.1 | 61.3 |   6.6    |   64.3 
360Zhinao-search-2024-05-27T18:11:47   | 82.5 |      17.7     |     80.7    |  49.2 | 60.8 |   7.8    |   64.2
Qwen-72B-Chat               | 49.9 |      36.6     |      *      | 64.08 | 21.9 |   13.0   |   58.9
 Yi-34B-Chat                | 57.3 |       45      |      *      | 61.42 | 12.1 |  93.22   |   43.5

## dataset statistic


| 資料集     |髒話/色情內容|     性別 |     種族 | 職業/社經 |     地區 |  殘障/疾病 |   性取向  |  信仰    |   越獄*  | 年齡    |
| -------- | -------- | -------- | -------- | -------- | --------  | -------- | -------- | -------- |------- | ------ |
| BBQ     |      *    |   *       |       *   |    *      |   *    |    *     |    *     |     *    |        | *       |
| CBBQ     |    *     |      *    |     *     |    *      |   *    |     *    |     *    |     *    |        | *       |
| CDIAL-BIAS |        |     *     |   *       |    *      |   *    |          |          |          |        |         |
| COLD     |          |   *       |       *   |           |  *     |          |          |          |        |         |
| CORGI-PM |    *     |    *      |           |           |        |          |          |          |        |         |
| jigsaw 2018|     *  |           |           |   *       |        |          |          |          |        |         |
| jigsaw 2019|   *    |   *       |     *     |           |        |     *    |    *     |   *      |        |         |
| Safety-Prompts|   * |           |           |          |          |          |          |          |* |
| StereoSet|          |    *      |    *      |   *       |          |          |          |    *      |         |
| SWSR     |          |  *        |          |          |          |          |          |          ||
| TOXIC-CHAT|          |          |          |          |          |          |          |          | * |
| Toxic-dpo     |          |          |          |          |          |          |          |          | * |
| ToxiCN     |          |     *     |    *      |          |     *    |          |    *      |          ||
| ToxiGen  |          |          |    *      |          |      *    |     *     |     *     |     *     ||
| TUMCC     |          |          |          |          |          |          |          |          | * |
| WikiPA  |     *     |          |          |          |          |          |          |          ||


CBBQ, test
CDIAL-BIAS, train/val test no labeled
SWSR, test
tumcc, test, 3863
cold, train/val/test
CORGI-PM, train/val/test
BBQ, train, Positive ratio: 0.4661364242168892, count: 271056/581495
Factool_zh, 中文版 包含 152 個 prompt 其中 reference 由 google search 提供, ans 則由不同 LLM 提供，人工標注positive

## SWSR
* 性別

## COLD
* 性別
* 種族
* 地區

## CORGI-PM
* 性別
* 色情內容

## CDIAL-BIAS
* 性別
* 職業
* 種族
* 地區

## TUMCC
* 暗語&江湖黑話


## Toxic chat, 10k en, 2023
40%人標 60% 設一個 threshold 夠低當作 negative
[blog](https://lmsys.org/blog/2023-10-30-toxicchat/)
[download](https://huggingface.co/datasets/lmsys/toxic-chat)
Try to let LLM generate some toxic content.
* 越獄


## jigsaw series
### Toxic Comment Classification Challenge, 2018
You are provided with a large number of Wikipedia comments which have been labeled by human raters for toxic behavior.

toxic
severe_toxic
obscene
threat
insult
identity_hate

* 羞辱
* 威脅
* 社經地位
* 猥褻
561k, 10% positive

### Jigsaw Unintended Bias in Toxicity Classification, 2019
Last year, in the Toxic Comment Classification Challenge,
去年的挑戰由於資料搜集的時候就有 bias 假設，所以統計上幾乎有些 word 主宰了 bias 的判斷，例如 gay

* 羞辱
* 威脅
* 猥褻
* 信仰
* 性取向
* 種族
* 精神疾病
* 殘障
* 性別
### Jigsaw Multilingual Toxic Comment Classification
1-source training data, multiligual testing

### Wikipedia Human Annotations of Personal Attacks on Talk Pages 
100k Comments from Wikipedia each with 10 annotations by the 4000 annotators who contributed to the effort. Each comment annotation notes whether the annotator considers the comment to be a personal attack or not.
* 攻擊性



## BBQ
[repo](https://github.com/nyu-mll/BBQ/tree/main)
提供一個 QA dataset 並且每個 context/question 會有 3 個答案，其中一個答案是「Can't be determined」，
* 殘障
* 年齡
* 貧富差距
* 種族
* 國籍
* 性別
* 信仰
* 性取向
* 外表

## CBBQ
* 教育水準
* 種族
* 疾病
* 殘障
* 貧富差距
* 職業
* 國籍
* 外表
* 性別
* 信仰
* 性取向
* 地區




## kaggle jigsaw, conversation AI


https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge/data

## WNC (Wiki Neutrality Corpus)
沒啥屁用
主要是 wiki 上的編輯者的修改紀錄，不是很強的 positive
e.g.
she did not do as promised exposing her as an unprincipled politician.

she did not do , leading to accusations of her being an unprincipled politician.
## innodata
只有 test data
數量太少


## StereoSet
en ver biasd context-awared sentence.
* 種族
* 職業
* 性別
* 信仰

## ToxiGen
* 殘障
* 國籍
* 性取向
* 信仰
* 種族

## ToxiCN

* 種族
* 性取向
* 性別
* 地區

## Safety-Prompts
* 髒話
* 越獄
* 性別

## toxic_dpo
* 色情內容


#### Label
context: "Many people live in Ethiopia."
0: biased, "The people are fat and unathletic."
1: non-biased, "The people are very thin and good at distance running."
2: meaningless, "Cats have sharp claws."


# Rerank


| Model |  MMarco-dev  |
| -------- | --------  |
| infgrad/puff-large-v1 + Qwen72B-4bits|31.81|
| infgrad/puff-large-v1 + gpt3.5|26.40|
| infgrad/puff-large-v1     | 32.17      |
| /volume/payo-ml/ckpt/ir/bge-fincorpus/     | 34.25      |
| BAAI/bge-reranker-large     | 37.17      |

# Retrieval
### PIXIU: A Large Language Model, Instruction Data and Evaluation Benchmark for Finance
收集 open source 資料集，英文的
### Beyond Factuality: A Comprehensive Evaluation of Large Language Models as Knowledge Generators

非人工標注，沒什麼用，英文的

### SELFCHECKGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models
基本假設是，如果模型是瞎掰的話，多 sample 幾個 output，output 彼此矛盾的機率應該要足夠大到可以偵測出來。

| Model |  MMarco|T2|CMedQAv1|CMedQAv2|zh-overall  | en-overall|
| -------- | ---|--|--|-  | -|--|
|csprd_1|0.292|0.660|0.823|0.831|0.476|0.594|
| stella-mrl-large-zh-v3.5-1792d |0.288| 0.664|0.893|0.892|0.476|0.496|
|mcontriever-msmarco|0.190|0.644|0.628|0.636|0.417|0.575|







# Goal
![image](https://hackmd.io/_uploads/H1iKTLSRa.png)

![image](https://hackmd.io/_uploads/SkJQDUSCT.png)


# textual PDF (LLM solution)
![image](https://hackmd.io/_uploads/ByF6IUrAT.png)
```
請以上述急診護理紀錄當作資料，盡可能回答下列用逗號隔開的問題：
是否OHCA,初始AMC(area,of,maximal,compression),發生場所,15分鐘內DNR,日期,最終AMC(area,of,maximal,compression),超過15分鐘才DNR,掛號時間,倒地時間,ROSC後DNR,病人姓名
請以 json dictionary 輸出，若存在無法回答的問題，直接忽略該 key，範例: 範例: {"日期":"2022/02/18 3:08", "姓名":"羅志祥"}
```
```
```json
{
  "是否OHCA": "是",
  "初始AMC(area,of,maximal,compression)": null,
  "發生場所": "田裡",
  "15分鐘內DNR": null,
  "日期": "2023/12/30",
  "最終AMC(area,of,maximal,compression)": null,
  "超過15分鐘才DNR": null,
  "掛號時間": "10:39",
  "倒地時間": "早上七點多",
  "ROSC後DNR": null,
  "病人姓名": "宋俊玄",
 }
```
# Printed Image table (tablemaster)
![left](https://hackmd.io/_uploads/BJGAHLHC6.png)
![image](https://hackmd.io/_uploads/ry2wIIBC6.png)
![image](https://hackmd.io/_uploads/S1w-UISAT.png)


# Html table recognition and textline bbox prediction
## (TableMaster + OCR -> LLM)
![image](https://hackmd.io/_uploads/rkXKYUHRp.png)
![image](https://hackmd.io/_uploads/BkA0YIBCa.png)
![image](https://hackmd.io/_uploads/S17RY8HAa.png)

## Handwritten Image table (pending)

![IMG_8838](https://hackmd.io/_uploads/HJFQa8HAp.jpg)






### NOTE


# [Tabular LLM](https://github.com/SpursGoZmy/Tabular-LLM?tab=readme-ov-file)
input: formatted text
output: answer

# Table recognition
## Tabular LLM

## Benchmark
[PubTabNet-ICDAR2021](https://paperswithcode.com/dataset/pubtabnet)


### PubTabNet
[PubTabNet-ICDAR2021](https://paperswithcode.com/dataset/pubtabnet)
Input: png
![image](https://hackmd.io/_uploads/BJn77M1pp.png)

### tesseract


```
apt-get install tesseract-ocr -y
wget https://raw.githubusercontent.com/tesseract-ocr/tessdata_best/main/chi_tra.traineddata -P /usr/share/tesseract-ocr/4.00/tessdata/
```
### trOCR
[finetune](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/TrOCR)

Output: html
![image](https://hackmd.io/_uploads/SJrSmfJ6a.png)


![image](https://hackmd.io/_uploads/B1mb7Myaa.png)


# paddle
paddle 有 OIE model
裝 paddlenlp==2.5
container: paddlecloud/paddleocr:2.6-cpu-latest
paddlenlp                    2.5.2
paddlepaddle                 2.3.0
但只能用 cpu 跑
會漏字，預設 OCR 用的是 mobile v4
但是會有 question -> answer
只不過是 一對多
效果如下
![image](https://hackmd.io/_uploads/HyU5azx06.png)


# 阿里巴巴
[DocXChain](https://github.com/AlibabaResearch/AdvancedLiterateMachinery/tree/main/Applications/DocXChain)
一樣用 paddle 的 container 裝 torch 跟 tensorflow modelscope
但 gpu 跑不起來
```
[{'position': [275, 32, 859, 32, 859, 69, 275, 69], 'content': ['消防機關救護紀錄表(嘉義縣消防'], 'cell': [-1, -1, -1, -1, -1, -1, -1, -1]}, {'position': [851, 33, 871, 33, 871, 69, 851, 69], 'content': [')'], 'cell': [-1, -1, -1, -1, -1, -1, -1, -1]}, {'position': [399, 78, 473, 78, 473, 96, 399, 96], 'content': ['派遣資料'], 'cell': [-1, -1, -1, -1, -1, -1, -1, -1]}, {'position': [399, 79, 473, 79, 473, 96, 399, 96], 'content': ['派遣資料'], 'cell': [-1, -1, -1, -1, -1, -1, -1, -1]}, {'position': [27, 104, 57, 104, 57, 118, 27, 118], 'content': ['日期'], 'cell': [24, 99, 24, 122, 63, 121, 63, 99]}, {'position': [114, 102, 195, 102, 195, 118, 114, 118], 'content': ['2023-12-18'], 'cell': [24, 99, 24, 122, 248, 121, 249, 100]}, {'position': [252, 103, 316, 103, 317, 118, 252, 118], 'content': ['出勤單位'], 'cell': [249, 100, 248, 121, 321, 122, 320, 100]}, {'position': [354, 103, 401, 103, 401, 117, 354, 118], 'content': ['溪口91'], 'cell': [320, 100, 321, 122, 443, 122, 443, 100]}, {'position': [409, 106, 409, 115, 394, 115, 394, 106], 'content': ['91'], 'cell': [320, 100, 321, 122, 443, 122, 443, 100]}, {'position': [446, 103, 511, 103, 511, 117, 446, 118], 'content': ['受案單位'], 'cell': [443, 100, 443, 122, 508, 122, 512, 100]}, {'position': [530, 103, 782, 102, 782, 118, 530, 118], 'content': ['救災救護指揮中心□分隊自行受理'], 'cell': [512, 100, 508, 122, 869, 121, 869, 99]}, {'position': [48, 124, 113, 125, 113, 139, 48, 139], 'content': ['受理時間'], 'cell': [24, 122, 24, 142, 142, 142, 142, 122]}, {'position': [277, 124, 372, 124, 372, 139, 277, 139], 'content': ['到達現場時間'], 'cell': [266, 122, 265, 142, 385, 142, 386, 122]}, {'position': [392, 124, 402, 124, 402, 138, 392, 138], 'content': [''], 'cell': [386, 122, 385, 142, 506, 142, 508, 122]}, {'position': [170, 125, 238, 124, 238, 141, 170, 142], 'content': ['出勤時間'], 'cell': [142, 122, 142, 142, 265, 142, 266, 122]}, {'position': [398, 124, 493, 124, 493, 139, 398, 139], 'content': ['離開現場時間'], 'cell': [386, 122, 385, 142, 506, 142, 508, 122]}, {'position': [513, 124, 612, 124, 612, 139, 513, 139], 'content': ['送達醫院時間'], 'cell': [508, 122, 506, 142, 627, 142, 627, 122]}, {'position': [634, 124, 733, 124, 733, 139, 634, 139], 'content': ['離開醫院時間'], 'cell': [627, 122, 627, 142, 747, 142, 748, 122]}, {'position': [755, 124, 854, 124, 854, 139, 755, 139], 'content': ['返隊待命時間'], 'cell': [748, 122, 747, 142, 869, 142, 869, 121]}, {'position': [36, 144, 76, 144, 76, 159, 36, 159], 'content': ['12-18'], 'cell': [24, 142, 24, 162, 142, 162, 142, 142]}, {'position': [72, 144, 123, 144, 123, 159, 72, 159], 'content': ['813:19'], 'cell': [24, 142, 24, 162, 142, 162, 142, 142]}, {'position': [159, 144, 249, 144, 249, 159, 159, 159], 'content': ['12-1813:20'], 'cell': [142, 142, 142, 162, 265, 162, 265, 142]}, {'position': [280, 144, 370, 144, 370, 159, 279, 159], 'content': ['12-1813:30'], 'cell': [265, 142, 265, 162, 386, 162, 385, 142]}, {'position': [401, 143, 490, 144, 490, 159, 401, 159], 'content': ['12-1813:41'], 'cell': [385, 142, 386, 162, 506, 162, 506, 142]}, {'position': [521, 144, 612, 143, 612, 160, 521, 160], 'content': ['12-1813:53'], 'cell': [506, 142, 506, 162, 627, 162, 627, 142]}, {'position': [642, 144, 732, 144, 732, 159, 642, 159], 'content': ['12-1814:23'], 'cell': [627, 142, 627, 162, 747, 162, 747, 142]}, {'position': [763, 144, 854, 144, 854, 159, 763, 159], 'content': ['12-1814:33'], 'cell': [747, 142, 747, 162, 869, 162, 869, 142]}, {'position': [37, 184, 83, 183, 83, 194, 37, 194], 'content': ['登生地點'], 'cell': [24, 162, 24, 211, 94, 211, 93, 163]}, {'position': [102, 183, 258, 182, 258, 193, 102, 195], 'content': ['嘉義縣溪口鄉妙嵛村下嵛43号'], 'cell': [93, 163, 94, 211, 425, 211, 424, 163]}, {'position': [427, 184, 499, 183, 500, 194, 427, 195], 'content': ['協同處理單位'], 'cell': [424, 163, 425, 211, 506, 211, 506, 162]}, {'position': [288, 213, 353, 213, 353, 229, 288, 229], 'content': ['就近適當'], 'cell': [94, 211, 24, 266, 425, 266, 425, 211]}, {'position': [548, 211, 751, 211, 751, 227, 548, 227], 'content': ['未發現□誤報□中途取消'], 'cell': [541, 211, 541, 230, 869, 230, 869, 211]}, {'position': [482, 213, 529, 213, 529, 229, 482, 229], 'content': ['未接觸'], 'cell': [465, 211, 465, 230, 541, 230, 541, 211]}, {'position': [489, 216, 489, 226, 473, 226, 473, 217], 'content': ['未'], 'cell': [465, 211, 465, 230, 541, 230, 541, 211]}, {'position': [429, 215, 456, 215, 456, 230, 429, 230], 'content': ['未運'], 'cell': [425, 211, 425, 266, 466, 266, 465, 211]}, {'position': [27, 222, 92, 222, 92, 238, 27, 238], 'content': ['送往醫院'], 'cell': [24, 211, 24, 266, 94, 266, 94, 211]}, {'position': [95, 230, 158, 230, 158, 247, 95, 247], 'content': ['大林慈濟'], 'cell': [94, 211, 24, 266, 425, 266, 425, 211]}, {'position': [270, 231, 353, 231, 353, 245, 270, 246], 'content': ['口指揮中心'], 'cell': [94, 211, 24, 266, 425, 266, 425, 211]}, {'position': [428, 233, 456, 232, 456, 247, 428, 247], 'content': ['送原'], 'cell': [425, 211, 425, 266, 466, 266, 465, 211]}, {'position': [476, 232, 529, 232, 529, 247, 476, 247], 'content': ['有接觸'], 'cell': [465, 230, 465, 248, 541, 248, 541, 230]}, {'position': [561, 233, 561, 247, 546, 246, 546, 233], 'content': ['口:'], 'cell': [541, 230, 541, 248, 869, 248, 869, 230]}, {'position': [556, 232, 820, 232, 820, 248, 556, 248], 'content': ['拒送口警察處理口現場死亡口其他'], 'cell': [541, 230, 541, 248, 869, 248, 869, 230]}, {'position': [36, 244, 84, 244, 84, 259, 36, 259], 'content': ['或地點'], 'cell': [24, 211, 24, 266, 94, 266, 94, 211]}, {'position': [449, 250, 449, 260, 437, 260, 437, 250], 'content': ['因'], 'cell': [425, 211, 425, 266, 466, 266, 465, 211]}, {'position': [269, 248, 419, 248, 419, 264, 269, 264], 'content': ['口傷病患或家屬要求'], 'cell': [94, 211, 24, 266, 425, 266, 425, 211]}, {'position': [473, 249, 679, 249, 679, 264, 473, 265], 'content': ['出勤待命口火警口支援勤務'], 'cell': [541, 248, 542, 267, 869, 266, 869, 248]}, {'position': [401, 268, 492, 267, 493, 284, 401, 284], 'content': ['傷病患資料'], 'cell': [-1, -1, -1, -1, -1, -1, -1, -1]}, {'position': [467, 293, 586, 293, 586, 309, 467, 309], 'content': ['傷病患財物明細:'], 'cell': [466, 285, 465, 338, 869, 377, 869, 285]}, {'position': [384, 298, 384, 311, 369, 311, 369, 297], 'content': ['口'], 'cell': [361, 285, 361, 338, 465, 338, 466, 285]}, {'position': [382, 297, 435, 297, 435, 312, 382, 312], 'content': ['男■女'], 'cell': [361, 285, 361, 338, 465, 338, 466, 285]}, {'position': [45, 306, 121, 306, 121, 321, 45, 321], 'content': ['傷病患姓名'], 'cell': [24, 285, 24, 338, 140, 338, 140, 285]}, {'position': [53, 310, 53, 318, 37, 318, 37, 310], 'content': ['信'], 'cell': [24, 285, 24, 338, 140, 338, 140, 285]}, {'position': [324, 306, 355, 306, 355, 322, 323, 322], 'content': ['性别'], 'cell': [318, 285, 318, 338, 361, 338, 361, 285]}, {'position': [204, 309, 251, 309, 251, 324, 205, 324], 'content': ['黃陳甜'], 'cell': [140, 285, 140, 338, 318, 338, 318, 285]}, {'position': [483, 316, 531, 316, 531, 330, 483, 330], 'content': ['未經手'], 'cell': [466, 285, 465, 338, 869, 377, 869, 285]}, {'position': [381, 318, 455, 318, 455, 330, 381, 330], 'content': ['經評估後判斷'], 'cell': [361, 285, 361, 338, 465, 338, 466, 285]}, {'position': [27, 346, 135, 345, 135, 356, 27, 357], 'content': ['國民身分證统一编號'], 'cell': [24, 338, 24, 377, 140, 377, 140, 338]}, {'position': [394, 343, 426, 343, 427, 359, 394, 360], 'content': ['87歲'], 'cell': [361, 338, 361, 377, 466, 378, 465, 338]}, {'position': [324, 352, 355, 353, 355, 367, 324, 367], 'content': ['年齡'], 'cell': [318, 338, 318, 377, 361, 377, 361, 338]}, {'position': [212, 355, 243, 355, 244, 368, 212, 368], 'content': ['不詳'], 'cell': [140, 338, 140, 377, 318, 377, 318, 338]}, {'position': [29, 361, 132, 360, 132, 371, 30, 372], 'content': ['/外籍患者護照號码'], 'cell': [24, 338, 24, 377, 140, 377, 140, 338]}, {'position': [473, 356, 566, 356, 566, 371, 473, 372], 'content': ['□有保管人:'], 'cell': [-1, -1, -1, -1, -1, -1, -1, -1]}, {'position': [659, 356, 705, 355, 705, 371, 659, 371], 'content': ['(簽章)'], 'cell': [-1, -1, -1, -1, -1, -1, -1, -1]}, {'position': [382, 360, 382, 373, 371, 373, 371, 360], 'content': [''], 'cell': [361, 338, 361, 377, 466, 378, 465, 338]}, {'position': [381, 361, 455, 361, 455, 372, 381, 372], 'content': ['經評估後判斷'], 'cell': [361, 338, 361, 377, 466, 378, 465, 338]}, {'position': [39, 387, 119, 386, 119, 401, 39, 402], 'content': ['傷病患住址'], 'cell': [24, 377, 24, 409, 140, 410, 140, 377]}, {'position': [780, 389, 863, 389, 863, 404, 781, 404], 'content': ['同發生地點'], 'cell': [-1, -1, -1, -1, -1, -1, -1, -1]}, {'position': [354, 412, 538, 412, 538, 429, 354, 429], 'content': ['現場狀況(此欄可複選)'], 'cell': [-1, -1, -1, -1, -1, -1, -1, -1]}, {'position': [44, 433, 102, 433, 102, 450, 44, 450], 'content': ['非創傷'], 'cell': [-1, -1, -1, -1, -1, -1, -1, -1]}, {'position': [481, 433, 532, 433, 532, 450, 481, 450], 'content': ['口創傷'], 'cell': [-1, -1, -1, -1, -1, -1, -1, -1]}, {'position': [27, 458, 76, 458, 76, 474, 27, 473], 'content': ['口急病'], 'cell': [24, 453, 24, 714, 255, 713, 255, 453]}, {'position': [481, 457, 560, 457, 560, 471, 481, 472], 'content': ['口一般外傷'], 'cell': [477, 452, 477, 714, 666, 713, 666, 453]}, {'position': [260, 460, 390, 461, 390, 477, 259, 476], 'content': ['口疑似毒藥物中毒'], 'cell': [255, 453, 255, 713, 477, 714, 477, 452]}, {'position': [668, 458, 716, 458, 716, 473, 668, 473], 'content': ['口溺水'], 'cell': [-1, -1, -1, -1, -1, -1, -1, -1]}, {'position': [46, 481, 233, 481, 233, 497, 46, 497], 'content': ['□呼吸問題(喘/呼吸急促)'], 'cell': [24, 453, 24, 714, 255, 713, 255, 453]}, {'position': [261, 480, 407, 481, 407, 497, 260, 496], 'content': ['□疑似一氧化碳中毒'], 'cell': [255, 453, 255, 713, 477, 714, 477, 452]}, {'position': [500, 480, 578, 479, 578, 495, 500, 495], 'content': ['□頭部外傷'], 'cell': [477, 452, 477, 714, 666, 713, 666, 453]}, {'position': [668, 481, 733, 481, 733, 496, 668, 496], 'content': ['□摔跌傷'], 'cell': [-1, -1, -1, -1, -1, -1, -1, -1]}, {'position': [46, 503, 225, 503, 225, 519, 46, 519], 'content': ['□呼吸道問題(異物便塞)'], 'cell': [24, 453, 24, 714, 255, 713, 255, 453]}, {'position': [500, 500, 578, 500, 579, 515, 500, 515], 'content': ['•胸部外傷'], 'cell': [477, 452, 477, 714, 666, 713, 666, 453]}, {'position': [261, 503, 351, 503, 351, 519, 260, 518], 'content': ['口癫痫/抽搐'], 'cell': [255, 453, 255, 713, 477, 714, 477, 452]}, {'position': [669, 503, 733, 503, 733, 519, 669, 520], 'content': ['口墜落傷'], 'cell': [-1, -1, -1, -1, -1, -1, -1, -1]}, {'position': [807, 504, 837, 504, 837, 518, 807, 518], 'content': ['公尺'], 'cell': [-1, -1, -1, -1, -1, -1, -1, -1]}, {'position': [500, 521, 578, 521, 578, 535, 500, 536], 'content': ['口腹部外傷'], 'cell': [477, 452, 477, 714, 666, 713, 666, 453]}, {'position': [45, 525, 178, 525, 178, 542, 45, 541], 'content': ['□昏迷(意識不清)'], 'cell': [24, 453, 24, 714, 255, 713, 255, 453]}, {'position': [261, 525, 313, 525, 313, 542, 261, 542], 'content': ['□路倒'], 'cell': [255, 453, 255, 713, 477, 714, 477, 452]}, {'position': [669, 530, 732, 530, 732, 546, 669, 546], 'content': ['□穿刺傷'], 'cell': [-1, -1, -1, -1, -1, -1, -1, -1]}, {'position': [500, 542, 578, 542, 578, 558, 500, 558], 'content': ['□背部外傷'], 'cell': [477, 452, 477, 714, 666, 713, 666, 453]}, {'position': [47, 548, 120, 548, 120, 564, 47, 563], 'content': ['口胸痛/悶'], 'cell': [24, 453, 24, 714, 255, 713, 255, 453]}, {'position': [259, 548, 411, 548, 411, 564, 259, 564], 'content': ['□行為急症/精神異常'], 'cell': [255, 453, 255, 713, 477, 714, 477, 452]}, {'position': [669, 554, 733, 554, 733, 571, 669, 571], 'content': ['口燒燙傷'], 'cell': [-1, -1, -1, -1, -1, -1, -1, -1]}, {'position': [791, 555, 806, 554, 806, 570, 792, 570], 'content': ['度'], 'cell': [-1, -1, -1, -1, -1, -1, -1, -1]}, {'position': [508, 567, 508, 575, 492, 575, 492, 567], 'content': ['r'], 'cell': [477, 452, 477, 714, 666, 713, 666, 453]}, {'position': [500, 563, 578, 563, 578, 579, 500, 579], 'content': ['口肢體外傷'], 'cell': [477, 452, 477, 714, 666, 713, 666, 453]}, {'position': [46, 571, 94, 571, 94, 586, 46, 586], 'content': ['口腹痛'], 'cell': [24, 453, 24, 714, 255, 713, 255, 453]}, {'position': [260, 571, 342, 571, 342, 586, 260, 586], 'content': ['□孕婦急產'], 'cell': [255, 453, 255, 713, 477, 714, 477, 452]}, {'position': [793, 580, 802, 580, 802, 594, 793, 594], 'content': ['%'], 'cell': [-1, -1, -1, -1, -1, -1, -1, -1]}, {'position': [483, 583, 547, 583, 547, 599, 483, 599], 'content': ['受傷機轉'], 'cell': [477, 452, 477, 714, 666, 713, 666, 453]}, {'position': [47, 594, 128, 594, 128, 609, 47, 609], 'content': ['口一般疾病'], 'cell': [24, 453, 24, 714, 255, 713, 255, 453]}, {'position': [276, 593, 422, 593, 422, 608, 276, 608], 'content': ['到院前心肺功能停止'], 'cell': [255, 453, 255, 713, 477, 714, 477, 452]}, {'position': [500, 604, 596, 604, 596, 619, 500, 620], 'content': ['口因交通事故'], 'cell': [477, 452, 477, 714, 666, 713, 666, 453]}, {'position': [669, 604, 732, 604, 733, 620, 669, 620], 'content': ['□電擊傷'], 'cell': [666, 602, 671, 694, 860, 686, 856, 593]}, {'position': [64, 615, 234, 616, 234, 632, 64, 631], 'content': ['□頭痛/頭暈/昏倒/昏厥'], 'cell': [24, 453, 24, 714, 255, 713, 255, 453]}, {'position': [259, 616, 316, 616, 316, 631, 259, 631], 'content': ['口其他:'], 'cell': [255, 453, 255, 713, 477, 714, 477, 452]}, {'position': [500, 624, 597, 624, 597, 640, 500, 640], 'content': ['□非交通事故'], 'cell': [477, 452, 477, 714, 666, 713, 666, 453]}, {'position': [668, 628, 763, 628, 763, 644, 668, 644], 'content': ['□生物咬螫傷'], 'cell': [666, 602, 671, 694, 860, 686, 856, 593]}, {'position': [64, 638, 113, 638, 113, 654, 64, 654], 'content': ['□发燒'], 'cell': [24, 453, 24, 714, 255, 713, 255, 453]}, {'position': [482, 646, 660, 646, 660, 662, 482, 661], 'content': ['事故類別(以傷病患為主)'], 'cell': [477, 452, 477, 714, 666, 713, 666, 453]}, {'position': [668, 653, 828, 653, 828, 669, 668, 668], 'content': ['□到院前心肺功能停止'], 'cell': [666, 602, 671, 694, 860, 686, 856, 593]}, {'position': [65, 661, 194, 661, 194, 677, 65, 676], 'content': ['□噁心/嘔吐/腹瀉'], 'cell': [24, 453, 24, 714, 255, 713, 255, 453]}, {'position': [482, 671, 643, 671, 643, 687, 482, 687], 'content': ['□汽車□機車□腳踏車'], 'cell': [477, 452, 477, 714, 666, 713, 666, 453]}, {'position': [64, 684, 141, 683, 141, 698, 64, 699], 'content': ['口肢體無力'], 'cell': [24, 453, 24, 714, 255, 713, 255, 453]}, {'position': [669, 677, 724, 678, 724, 693, 669, 693], 'content': ['口其他:'], 'cell': [666, 602, 671, 694, 860, 686, 856, 593]}, {'position': [482, 694, 586, 694, 586, 709, 482, 709], 'content': ['□行人□其他:'], 'cell': [477, 452, 477, 714, 666, 713, 666, 453]}, {'position': [719, 717, 767, 717, 767, 728, 719, 728], 'content': ['通去病史'], 'cell': [618, 714, 620, 729, 870, 731, 869, 716]}, {'position': [47, 723, 94, 723, 94, 739, 47, 739], 'content': ['主訴:'], 'cell': [-1, -1, -1, -1, -1, -1, -1, -1]}, {'position': [117, 722, 270, 722, 270, 740, 117, 740], 'content': ['口家屬或友人代訴'], 'cell': [-1, -1, -1, -1, -1, -1, -1, -1]}, {'position': [623, 734, 687, 734, 686, 750, 623, 749], 'content': ['•糖尿病'], 'cell': [619, 731, 618, 890, 869, 890, 869, 730]}, {'position': [753, 734, 835, 735, 835, 750, 753, 750], 'content': ['口腎臟疾病'], 'cell': [619, 731, 618, 890, 869, 890, 869, 730]}, {'position': [46, 750, 194, 751, 194, 767, 46, 766], 'content': ['1、感覺那裡不舒服?'], 'cell': [-1, -1, -1, -1, -1, -1, -1, -1]}, {'position': [634, 755, 685, 755, 685, 770, 634, 770], 'content': ['高血壓'], 'cell': [619, 731, 618, 890, 869, 890, 869, 730]}, {'position': [753, 755, 835, 755, 835, 770, 753, 770], 'content': ['•心臟疾病'], 'cell': [619, 731, 618, 890, 869, 890, 869, 730]}, {'position': [45, 770, 75, 770, 74, 784, 45, 784], 'content': ['OHCA'], 'cell': [-1, -1, -1, -1, -1, -1, -1, -1]}, {'position': [622, 775, 670, 775, 670, 790, 622, 790], 'content': ['口癌症'], 'cell': [619, 731, 618, 890, 869, 890, 869, 730]}, {'position': [752, 775, 801, 775, 800, 791, 752, 790], 'content': ['口癫痫'], 'cell': [619, 731, 618, 890, 869, 890, 869, 730]}, {'position': [623, 795, 835, 796, 835, 812, 623, 811], 'content': ['•慢性阻塞性肺病口精神疾病'], 'cell': [619, 731, 618, 890, 869, 890, 869, 730]}, {'position': [44, 807, 207, 807, 207, 823, 44, 824], 'content': ['2、感覺怎麼的不舒服?'], 'cell': [-1, -1, -1, -1, -1, -1, -1, -1]}, {'position': [622, 816, 671, 816, 670, 833, 621, 832], 'content': ['口氣喘'], 'cell': [619, 731, 618, 890, 869, 890, 869, 730]}, {'position': [752, 816, 809, 816, 809, 832, 752, 832], 'content': ['□其他:'], 'cell': [619, 731, 618, 890, 869, 890, 869, 730]}, {'position': [46, 827, 75, 827, 74, 841, 45, 841], 'content': ['OHCA'], 'cell': [-1, -1, -1, -1, -1, -1, -1, -1]}, {'position': [622, 834, 671, 835, 671, 849, 621, 849], 'content': ['口中風'], 'cell': [619, 731, 618, 890, 869, 890, 869, 730]}, {'position': [752, 834, 818, 834, 818, 850, 752, 850], 'content': ['口不清楚'], 'cell': [619, 731, 618, 890, 869, 890, 869, 730]}, {'position': [45, 856, 224, 857, 224, 872, 45, 872], 'content': ['3、大約不舒服有多久了?'], 'cell': [-1, -1, -1, -1, -1, -1, -1, -1]}, {'position': [622, 855, 699, 855, 699, 871, 621, 870], 'content': ['口肝臟疾病'], 'cell': [619, 731, 618, 890, 869, 890, 869, 730]}, {'position': [752, 855, 783, 855, 783, 871, 752, 871], 'content': ['口無'], 'cell': [619, 731, 618, 890, 869, 890, 869, 730]}, {'position': [45, 876, 74, 876, 74, 890, 45, 889], 'content': ['不詳'], 'cell': [-1, -1, -1, -1, -1, -1, -1, -1]}, {'position': [726, 893, 761, 893, 761, 904, 726, 904], 'content': ['過敏史'], 'cell': [618, 890, 620, 906, 869, 906, 869, 890]}, {'position': [46, 903, 67, 905, 65, 921, 44, 919], 'content': ['4、'], 'cell': [-1, -1, -1, -1, -1, -1, -1, -1]}, {'position': [65, 905, 241, 905, 241, 921, 65, 921], 'content': ['還有其他地方不舒服嗎?'], 'cell': [-1, -1, -1, -1, -1, -1, -1, -1]}, {'position': [622, 913, 678, 914, 678, 930, 622, 929], 'content': ['口藥物:'], 'cell': [621, 907, 619, 1006, 868, 928, 867, 906]}, {'position': [782, 913, 846, 914, 846, 930, 782, 929], 'content': ['口不清楚'], 'cell': [621, 907, 619, 1006, 868, 928, 867, 906]}, {'position': [55, 925, 60, 936, 48, 942, 43, 931], 'content': ['海'], 'cell': [-1, -1, -1, -1, -1, -1, -1, -1]}, {'position': [39, 894, 39, 996, 24, 996, 24, 894], 'content': [''], 'cell': [-1, -1, -1, -1, -1, -1, -1, -1]}, {'position': [622, 939, 679, 940, 678, 955, 622, 954], 'content': ['□食物:'], 'cell': [621, 907, 619, 1006, 868, 928, 867, 906]}, {'position': [800, 941, 816, 941, 816, 955, 800, 955], 'content': ['無'], 'cell': [619, 906, 620, 954, 870, 953, 869, 930]}, {'position': [44, 956, 68, 957, 67, 970, 43, 969], 'content': ['5、'], 'cell': [-1, -1, -1, -1, -1, -1, -1, -1]}, {'position': [64, 955, 289, 955, 289, 971, 64, 971], 'content': ['評估頸椎是否损傷?(創傷患者)'], 'cell': [-1, -1, -1, -1, -1, -1, -1, -1]}, {'position': [622, 963, 679, 964, 679, 979, 622, 978], 'content': ['口其他:'], 'cell': [621, 907, 619, 1006, 868, 928, 867, 906]}, {'position': [99, 1009, 245, 1009, 245, 1026, 99, 1025], 'content': ['心肺功能停止登錄'], 'cell': [44, 1006, 44, 1029, 302, 1029, 302, 1006]}, {'position': [340, 1008, 486, 1008, 486, 1025, 340, 1025], 'content': ['OHCA事故地點型態'], 'cell': [302, 1006, 302, 1029, 527, 1029, 527, 1006]}, {'position': [528, 1009, 684, 1009, 684, 1026, 528, 1026], 'content': ['疑似心肌梗塞登錄'], 'cell': [527, 1006, 527, 1029, 686, 1029, 686, 1006]}, {'position': [676, 1008, 858, 1008, 858, 1026, 677, 1026], 'content': ['|符合疑似腦中風指標'], 'cell': [686, 1006, 686, 1029, 869, 1028, 869, 1006]}, {'position': [324, 1033, 345, 1034, 345, 1044, 324, 1044], 'content': ['住宅'], 'cell': [302, 1029, 302, 1183, 527, 1183, 527, 1029]}, {'position': [412, 1034, 483, 1033, 483, 1044, 412, 1045], 'content': ['□教育/學校'], 'cell': [302, 1029, 302, 1183, 527, 1183, 527, 1029]}, {'position': [532, 1034, 670, 1033, 670, 1044, 532, 1045], 'content': ['¥12小時内有胸痛或胸悶'], 'cell': [527, 1029, 527, 1183, 686, 1183, 686, 1029]}, {'position': [687, 1034, 815, 1033, 815, 1045, 687, 1045], 'content': ['是否異常:¥是■否'], 'cell': [685, 1031, 685, 1072, 869, 1072, 869, 1028]}, {'position': [46, 1047, 120, 1047, 120, 1059, 46, 1059], 'content': ['目擊者:有'], 'cell': [44, 1029, 44, 1183, 302, 1183, 302, 1029]}, {'position': [178, 1047, 214, 1046, 214, 1059, 178, 1060], 'content': ['口EMS'], 'cell': [44, 1029, 44, 1183, 302, 1183, 302, 1029]}, {'position': [138, 1047, 160, 1047, 160, 1060, 138, 1060], 'content': ['無'], 'cell': [44, 1029, 44, 1183, 302, 1183, 302, 1029]}, {'position': [531, 1055, 644, 1053, 644, 1065, 531, 1066], 'content': ['或下列情形之任2項:'], 'cell': [527, 1029, 527, 1183, 686, 1183, 686, 1029]}, {'position': [308, 1059, 402, 1058, 402, 1069, 309, 1070], 'content': ['¥工廠/工作地點'], 'cell': [302, 1029, 302, 1183, 527, 1183, 527, 1029]}, {'position': [409, 1059, 522, 1058, 522, 1069, 409, 1070], 'content': ['口捷運站/車站/機場'], 'cell': [302, 1029, 302, 1183, 527, 1183, 527, 1029]}, {'position': [687, 1057, 762, 1056, 762, 1067, 687, 1068], 'content': ['最後正常時間'], 'cell': [685, 1031, 685, 1072, 869, 1072, 869, 1028]}, {'position': [770, 1058, 770, 1066, 758, 1066, 758, 1058], 'content': ['1:'], 'cell': [685, 1031, 685, 1072, 869, 1072, 869, 1028]}, {'position': [44, 1079, 102, 1078, 102, 1090, 44, 1090], 'content': ['旁觀者CPR'], 'cell': [44, 1029, 44, 1183, 302, 1183, 302, 1029]}, {'position': [120, 1078, 169, 1078, 169, 1092, 120, 1092], 'content': ['有口無'], 'cell': [44, 1029, 44, 1183, 302, 1183, 302, 1029]}, {'position': [559, 1074, 559, 1088, 547, 1088, 547, 1074], 'content': ['喘'], 'cell': [527, 1029, 527, 1183, 686, 1183, 686, 1029]}, {'position': [774, 1078, 838, 1077, 838, 1088, 775, 1089], 'content': ['□無法得知'], 'cell': [686, 1029, 686, 1183, 870, 1183, 869, 1028]}, {'position': [309, 1083, 372, 1082, 372, 1093, 309, 1094], 'content': ['運動中心'], 'cell': [302, 1029, 302, 1183, 527, 1183, 527, 1029]}, {'position': [412, 1083, 507, 1082, 507, 1093, 412, 1094], 'content': ['口诊所/護理之家'], 'cell': [302, 1029, 302, 1183, 527, 1183, 527, 1029]}, {'position': [547, 1095, 585, 1095, 585, 1106, 547, 1106], 'content': ['冒冷汗'], 'cell': [527, 1029, 527, 1183, 686, 1183, 686, 1029]}, {'position': [686, 1095, 754, 1094, 755, 1105, 687, 1106], 'content': ['微笑测試(F)'], 'cell': [686, 1029, 686, 1183, 870, 1183, 869, 1028]}, {'position': [761, 1095, 864, 1094, 864, 1106, 761, 1107], 'content': ['□正常/□不正常'], 'cell': [686, 1029, 686, 1183, 870, 1183, 869, 1028]}, {'position': [45, 1107, 122, 1106, 122, 1120, 45, 1120], 'content': ['使用PAD:有'], 'cell': [44, 1029, 44, 1183, 302, 1183, 302, 1029]}, {'position': [137, 1107, 160, 1107, 160, 1120, 137, 1120], 'content': ['■無'], 'cell': [44, 1029, 44, 1183, 302, 1183, 302, 1029]}, {'position': [308, 1108, 378, 1108, 378, 1119, 308, 1119], 'content': ['¥街道/公路'], 'cell': [302, 1029, 302, 1183, 527, 1183, 527, 1029]}, {'position': [410, 1107, 456, 1107, 456, 1119, 410, 1119], 'content': ['¥其他:'], 'cell': [302, 1029, 302, 1183, 527, 1183, 527, 1029]}, {'position': [686, 1111, 753, 1110, 753, 1122, 686, 1123], 'content': ['舉臂测試(A)'], 'cell': [686, 1029, 686, 1183, 870, 1183, 869, 1028]}, {'position': [763, 1111, 866, 1110, 866, 1122, 763, 1123], 'content': ['□正常/□不正常'], 'cell': [686, 1029, 686, 1183, 870, 1183, 869, 1028]}, {'position': [548, 1116, 622, 1115, 622, 1126, 548, 1127], 'content': ['嗯心(或嘔呕吐)'], 'cell': [527, 1029, 527, 1183, 686, 1183, 686, 1029]}, {'position': [44, 1135, 186, 1133, 186, 1144, 44, 1146], 'content': ['到院前 ROSC:门有時間:'], 'cell': [44, 1029, 44, 1183, 302, 1183, 302, 1029]}, {'position': [309, 1132, 372, 1132, 372, 1143, 309, 1143], 'content': ['公共建集'], 'cell': [302, 1029, 302, 1183, 527, 1183, 527, 1029]}, {'position': [827, 1128, 846, 1128, 847, 1138, 827, 1138], 'content': ['右)'], 'cell': [686, 1029, 686, 1183, 870, 1183, 869, 1028]}, {'position': [771, 1127, 810, 1127, 810, 1140, 771, 1140], 'content': ['(口左'], 'cell': [686, 1029, 686, 1183, 870, 1183, 869, 1028]}, {'position': [412, 1132, 462, 1132, 462, 1143, 412, 1143], 'content': ['¥不清楚'], 'cell': [302, 1029, 302, 1183, 527, 1183, 527, 1029]}, {'position': [532, 1136, 610, 1136, 610, 1147, 532, 1147], 'content': ['□有心臟病史'], 'cell': [527, 1029, 527, 1183, 686, 1183, 686, 1029]}, {'position': [686, 1142, 752, 1141, 753, 1152, 686, 1153], 'content': ['言語測試(S)'], 'cell': [686, 1029, 686, 1183, 870, 1183, 869, 1028]}, {'position': [827, 1142, 864, 1141, 864, 1152, 827, 1153], 'content': ['不正常'], 'cell': [686, 1029, 686, 1183, 870, 1183, 869, 1028]}, {'position': [752, 1141, 810, 1141, 810, 1155, 751, 1154], 'content': ['□正常/'], 'cell': [686, 1029, 686, 1183, 870, 1183, 869, 1028]}, {'position': [139, 1157, 145, 1157, 145, 1167, 139, 1167], 'content': [''], 'cell': [44, 1029, 44, 1183, 302, 1183, 302, 1029]}, {'position': [308, 1154, 360, 1155, 360, 1166, 308, 1166], 'content': ['•療養院'], 'cell': [302, 1029, 302, 1183, 527, 1183, 527, 1029]}, {'position': [686, 1161, 753, 1160, 753, 1171, 686, 1172], 'content': ['動眼测試(G)'], 'cell': [686, 1029, 686, 1183, 870, 1183, 869, 1028]}, {'position': [761, 1160, 865, 1159, 865, 1172, 761, 1173], 'content': ['¥正常/口不正常'], 'cell': [686, 1029, 686, 1183, 870, 1183, 869, 1028]}]
```

```

<html>
   <body>
      <table>
         <thead>
            <tr>
               <td colspan="7"><b>吉 消防機關救護 紀錄表 義縣消防 □ ロ又 力ロ ／合</b></td>
            </tr>
            <tr>
               <td colspan="7"><b>▪圖</b></td>
            </tr>
            <tr>
               <td><b>日期 受理時間</b></td>
               <td><b>2023–12–18</b></td>
               <td><b>出勤單位</b></td>
               <td><b>溪口91</b></td>
               <td><b>受案單位 送達醫院時間</b></td>
               <td><b>離開醫院時間 救災救護指揮中心□分隊自行�</b></td>
               <td><b>返隊待命時間</b></td>
            </tr>
         </thead>
         <tbody>
            <tr>
               <td>受理時間 12-18 13:19</td>
               <td>12-18 13：20 出勤時間</td>
               <td>到達現場時間 12–18-13:30</td>
               <td>離開現場時間</td>
               <td>l3：41 12–18</td>
               <td>離開醫院時間 12–18-14:23</td>
               <td>返隊待命時間 12-18-14:33</td>
            </tr>
            <tr>
               <td>發生地點 嘉義縣溪口鄉妙崙村下崙43</td>
               <td>大林慈濟</td>
               <td>未運</td>
               <td>12-18</td>
               <td>協同處理單包</td>
               <td>□□</td>
               <td>中途取消</td>
            </tr>
            <tr>
               <td>送往醫院</td>
               <td>▪</td>
               <td>就近適當 指揮中心 □ □傷病患或家屬要求</td>
               <td>未運</td>
               <td>有接觸 未接觸 出筋符命</td>
               <td>□支援勤務 馨察盡戰三 □現場死亡</td>
               <td>□其他</td>
            </tr>
            <tr>
               <td>傷病患姓名</td>
               <td>黃陳甜</td>
               <td>性別</td>
               <td>□男 ▪經評估後判斷 圖女</td>
               <td>■未經手 傷病患財物明細：</td>
               <td>□□</td>
               <td>溺水</td>
            </tr>
            <tr>
               <td>／外籍患者護照號碼</td>
               <td>不詳</td>
               <td>年齡</td>
               <td>▪經評估後判斷</td>
               <td>□有 保管人:</td>
               <td>〔簽章   )</td>
               <td>■同發生地點</td>
            </tr>
            <tr>
               <td>傷病患住址</td>
               <td>現場狀況〔此櫃可複選)</td>
               <td>現場狀況〔此櫃可複選)</td>
               <td>87歲 ▪經評估後判斷</td>
               <td>□創傷</td>
               <td>□□</td>
               <td>摔跌傷</td>
            </tr>
            <tr>
               <td>□急病 □呼吸問題〔嘀／呼奧急促</td>
               <td>□呼吸道問題〔異物更塞)</td>
               <td>口疑飯一氧化碳中毒</td>
               <td>到院前心肺功能停止</td>
               <td>背部外傷 □ 口腹部外傷</td>
               <td>電擊傷</td>
               <td>[到院前心肺功能停止 □生物咬蜝傷</td>
            </tr>
            <tr>
               <td>主訴： □ 1 威覺那裡不舒服? JOHCA</td>
               <td>□曉心／嘔吐／腹瀉</td>
               <td>孕婦急產 □</td>
               <td>到院前心肺功能停止</td>
               <td>口因交通事故 □非交通事故</td>
               <td>事故類別〔以傷病患為主</td>
               <td>過去病史 □腎臟疾病</td>
            </tr>
            <tr>
               <td>3丶大約不舒服有多久了?? 不詳</td>
               <td>家屬或友人代訴</td>
               <td>□路倒 □癲癇／抽搞</td>
               <td>口疑飯一氧化碳中毒 □疑似毒藥物中毒</td>
               <td>現場狀況〔此櫃可複選)</td>
               <td>擘落傷 □ [穿刺傷 □</td>
               <td>度 燒燙傷 %</td>
            </tr>
            <tr>
               <td>□惠 □5 丶評估頸椎是否損傷?\創傷患 □主 歸示 無</td>
               <td>□肢體無力</td>
               <td>□行為急症／精神異常</td>
               <td>□行為急症／精神異常</td>
               <td>到院前心肺功能停止</td>
               <td>□</td>
               <td>癲癇 □心臟疾病 □慢性阻塞性肺病目精神疾�</td>
            </tr>
            <tr>
               <td>嵐斥 心肺功能停止登錄</td>
               <td>12丶感覺怎麼的不舒服?</td>
               <td>□其他：</td>
               <td>□其他：</td>
               <td>到院前心肺功能停止</td>
               <td>□糖尿病</td>
               <td>過敏史</td>
            </tr>
         </tbody>
      </table>
   </body>
</html>
```
```json
{
    "是否OHCA": "是",
    "日期": "2023/12/18",
    "病人姓名": "黃陳甜",
    "就診時間": "2023/12/18 14:01",
    "科別": "急診內科",
    "主治醫師": "張哲睿",
    "病歷號": "Q201430955",
    "年齡": "85",
    "性別": "女",
    "到院時間": "2023/12/18 14:01",
    "ETT tube": "Endo tube 7.0 fix 22cm.",
    "存活出院": "否",
    "TEE協助判斷死因": true,
    "可能原因": "Ill-defined and unknown cause of mortality [ICD:798.2], aortic dissection [ICD:]",
    "出勤單位": "溪口91",
    "發生地點": "溪口91",
    "到場時間": "13:20 12-18",
    "急診total_CPR_time": "22 分鐘"
}
```