The trained model does not work very well...

[ScottishFold007]

Hello! Your open source project is great and is a great benefit! When I was testing the Chinese dataset, I found that I ran a few epochs and the results were not very good. Can you tell me what might be the cause of this? Train data:

Example prediction：

import json
from fsner import FSNERModel, FSNERTokenizerUtils, pretty_embed

query_texts = [
    "阿贵住在户部巷吗？",
    "我不喜欢看《人鱼传说》",
    "我喜欢李柏林的'天空之城'，写的很好"
]


support_texts = {
    "地址": [
		    "彭小军认为，国内银行现在走的是[E]台湾[/E]的发卡模式，先通过跑马圈地再在圈的地里面选择客户，", 
        "郑阿姨就赶到[E]文汇路[/E]排队拿钱，希望能将缴纳的一万余元学费拿回来，顺便找校方或者教委要个说法。", 
        "如今着整个[E]潮白河[/E]区域环境的巨大变化和环首都经济圈的快速推进，夏威夷水岸1号的稀缺价值越来越明显，", 
        "如今着整个潮白河区域环境的巨大变化和环首都经济圈的快速推进，[E]夏威夷水岸1号[/E]的稀缺价值越来越明显，",
         "这也让很多业主据此认为，[E]雅清苑[/E]是政府公务员挤对了国家的经适房政策。"
                  ],
    "书籍": [
         "除了冠军外有7个名额的入围奖，奖品是[E]《暗黑破坏神》全套小说[/E]、《魔兽争霸》全套小说", 
         "除了冠军外有7个名额的入围奖，奖品是《暗黑破坏神》全套小说、[E]《魔兽争霸》全套小说[/E]", 
         "本次促销活动赠送的周边产品全部都是限量版啊！值得一提的是[E]《红楼梦》[/E]精美人物主题书签A组、", 
         "“去年银监会下发[E]《关于信用卡套现活跃风险提示的通知》[/E]要求：严格禁止将pos机发放在个人名下，",
    ]
          }

device = 'cpu'

model_path = '/content/checkpoints/model'
tokenizer = FSNERTokenizerUtils(model_path)
queries = tokenizer.tokenize(query_texts).to(device)
supports = tokenizer.tokenize(list(support_texts.values())).to(device)

model = FSNERModel(model_path)
model.to(device)

p_starts, p_ends = model.predict(queries, supports)

# One can prepare supports once and reuse  multiple times with different queries
# ------------------------------------------------------------------------------
# start_token_embeddings, end_token_embeddings = model.prepare_supports(supports)
# p_starts, p_ends = model.predict(queries, start_token_embeddings=start_token_embeddings,
#                                  end_token_embeddings=end_token_embeddings)

output = tokenizer.extract_entity_from_scores(query_texts, queries, p_starts, p_ends,
                        entity_keys=list(support_texts.keys()), thresh=0.010)

print(json.dumps(output, indent=2,ensure_ascii=False))

# install displacy for pretty embed
pretty_embed(query_texts, output, list(support_texts.keys()))

[sayef]

Thanks for trying it out. This will be very helpful to fix bugs and make the library more usable.

Which pretrained-model did you use for training? For English, I used bert-base-uncased.

[ScottishFold007]

Because I am using a Chinese dataset, the model Langboat/mengzi-bert-base, which is also based on the Chinese corpus, is used for training

[ScottishFold007]

I don't know if it would have anything to do with the language, but East Asian languages like Chinese, Korean, and Japanese all require word segmentation.

[ScottishFold007]

This is the style of the training corpus： {"address": ["彭小军认为，国内银行现在走的是[E]台湾[/E]的发卡模式，先通过跑马圈地再在圈的地里面选择客户，", "郑阿姨就赶到[E]文汇路[/E]排队拿钱，希望能将缴纳的一万余元学费拿回来，顺便找校方或者教委要个说法。", "如今着整个[E]潮白河[/E]区域环境的巨大变化和环首都经济圈的快速推进，夏威夷水岸1号的稀缺价值越来越明显，", "如今着整个潮白河区域环境的巨大变化和环首都经济圈的快速推进，[E]夏威夷水岸1号[/E]的稀缺价值越来越明显，", "这也让很多业主据此认为，[E]雅清苑[/E]是政府公务员挤对了国家的经适房政策。", "陈艳萍：买[E]西山[/E]的人的购房需求，主要有两种，一种是养老型的需求，很多人认为在西山是能够颐养天年的"], "name": ["[E]彭小军[/E]认为，国内银行现在走的是台湾的发卡模式，先通过跑马圈地再在圈的地里面选择客户，", "[E]温格[/E]的球队终于又踢了一场经典的比赛，2比1战胜曼联之后枪手仍然留在了夺冠集团之内，", "突袭黑暗雅典娜》中[E]Riddick[/E]发现之前抓住他的赏金猎人Johns，", "突袭黑暗雅典娜》中Riddick发现之前抓住他的赏金猎人[E]Johns[/E]，", "吴三桂演义》小说的想像，说是为[E]牛金星[/E]所毒杀。……在小说中加插一些历史背景，", "市场仍存在对网络销售形式的需求，网络购彩前景如何？为此此我们采访业内专家[E]程阳[/E]先生。", "本报讯（记者[E]王吉瑛[/E]）双色球即将出台新规，一等奖最高奖金可达到1000万元。昨天，中彩中心透露，", "价格高昂的大钻和翡翠消费为何如此火？通灵珠宝总裁[E]沈东军[/E]认为，这与原料稀缺有直接关系。“", "是目前表现最好的锋线组合之一，而[E]沃尔科特[/E]往往能够让对手的整个左边肋疲于防守，以目前枪手的能力和状态，", "[E]Svensson[/E]在接受媒体采访时表示，CAPCOM并没有放弃《街霸》电影系列，将推出新的《", "证券时报记者[E]唐曜华[/E]", "现役DotA明星选手，担任SOLO位的世界第一影魔[E]Pis（卜严骏）[/E]，", "[E]郭庆祥[/E]：我们看画廊如果有好的艺术家，好的作品进去，我们是真正想去买好的艺术作品，而不是投资，", "腾讯新闻昨天[E]金庸[/E]逝世江湖再无金大侠订阅号消息昨天【15条】王者荣耀福利抢鲜看!队友的京东京东jd.", "[E]陈艳萍[/E]：买西山的人的购房需求，主要有两种，一种是养老型的需求，很多人认为在西山是能够颐养天年的，"]

[sayef]

Dataset preparation and pre-trained model selection seem fine. How was the val_loss_epoch and val_acc_epoch after the first few epochs?

[ScottishFold007]

Dataset preparation and pre-trained model selection seem fine. How was the val_loss_epoch and val_acc_epoch after the first few epochs?

[sayef]

Great, Please continue training for at least 20 epochs. It should get better. It was also not great for me too at the first few epochs.

[ScottishFold007]

Ok, I will continue my training and I will give you feedback later, thank you for your careful reply!

[sayef]

@ScottishFold007 Hi again! Was your training successful?

[ScottishFold007]

@ScottishFold007 Hi again! Was your training successful?

I have tried many methods, but the results are still poor, I wonder why? Can I give you the data and trouble you to test it?

[sayef]

I can try. Please send a link to your dataset to [email protected]

[ScottishFold007]

I can try. Please send a link to your dataset to [email protected]

Hello, I've sent you the processed training set and test set via [email protected], thanks for your help!

[polodealvarado]

Hi @ScottishFold007 ! Could you get better results ?

[ScottishFold007]

Hi @ScottishFold007 ! Could you get better results ?

still very bad~