syuoni / eznlp Goto Github PK

View Code? Open in Web Editor NEW

129.0 129.0 20.0 3.1 MB

Easy Natural Language Processing

License: Apache License 2.0

Python 97.63% Scheme 2.37%

information-extraction named-entity-recognition natural-language-processing nlp pytorch

eznlp's People

Contributors

Stargazers

Watchers

Forkers

lanyangyang93 hhx1999 cwlseu weinixuehao mtt1998 sukeey tomas-lilingfeng qznan bibabo-bupt wongself tong990226 techthiyanes aqhali inno-inspire rickyshaw999 emvista npcccccccccccccc gitvbu huisw

eznlp's Issues

将Boundary Smoothing应用于其他模型的问题

老师您好，想将Boundary Smoothing应用于其他模型，模型计算交叉熵时traget是一维张量代表若干个span的类别，Boundary Smoothing得到的label_ids怎么用于计算loss呢，请问老师这部分在源码的哪里，恳请老师解惑，不胜感激。

您好，现在我运行的您的eznlp代码的F1值过低，想请教您一些问题，您方便留个联系方式吗

我使用的数据集是根据您代码中的demo处理后的，只有49%的F1值，您有空的话可以给我的邮箱留个言吗，我的邮箱是[email protected]，如果方便的话，还想找您拿一下您当时运行的数据集

Can't Install eznlp version 0.2.3

Hi.
I got this problem when I run install

pip install dist/eznlp-{version}.tar.gz

ERROR: Cannot install eznlp and eznlp==0.2.3 because these package versions have conflicting dependencies.

The conflict is caused by:
eznlp 0.2.3 depends on transformers==4.3.2
allennlp 2.0.1 depends on transformers<4.3 and >=4.1

To fix this you could try to:

loosen the range of package versions you've specified
remove package versions to allow pip attempt to solve the dependency conflict

Can u help me? Tks

关于decoder的参数'--ck_decoder'设置问题

你好，我想请教您：
'--ck_decoder'参数有四个不同的选择：['sequence_tagging', 'span_classification', 'boundary_selection', 'specific_span']，
我发现对这三个参数（'--ck_decoder'、'--use_interm1'、'--use_interm2'）进行不同的设置会产生不同的模型组合，我想请问这三个参数如何设置可以让模型分别成为 Baseline 和 Baseline+BS。我不懂'span_classification'、'specific_span'有什么区别。我目前在conll2003数据集下尝试。

'--bert_arch'：'RoBERTa_base'、'--ck_decoder'： 'specific_span'、'--use_interm1'：True、'--use_interm2'：False、
'--enc_arch'：‘LSTM’。这些参数的组合是Baseline吗？

还有一个问题是，对于数据集conll2003，我将'--doc_level'参数设置为True，并进行了截断之后，数据变成了文档级别的，每条数据很长，我的显卡不支持我将batch_size设置为48，因为一个batch的数据太多了，然后我就减小了batch，请问对于conll2003数据集，得到您的论文中的相应的结果，此时的实验的batch_size是48吗？
抱歉问题有点多。

Potential label leakage for over-long document

Interesting framework for NLP. The processing for the over-long document may leak the label information in

eznlp/eznlp/model/bert_like.py

Line 415 in 5d7b7c2

while not is_segmentable[span_end]:

if the document is too long, this line of code will force the truncation to happen right before the entity (for NER task). Although this will not affect too many samples, I believe we should not use any information from the label.

OntoNotes 5 ENG data

Where is the OntoNotes5.0-Eng data demo and loading code as it has been reported in the Boundary Smooth paper

DocRED Joint extraction (sequence length problem, subtokens)

Hi,

Thanks a lot for this amazing framework.

I'm working on the deep span representations from ACL2023. I have already adapted to conll2004.
I'm trying to adapt the model on docred dataset. I'm facing to a sequence length problem.

/miniconda3/envs/eznlp/lib/python3.8/site-packages/eznlp/model/bert_like.py", line 121, in _token_ids_from_tokenized assert len(sub_tokens) <= self.tokenizer.model_max_length - 2 AssertionError
model use : distilroberta-base
Do you have an idea for solving this problem?

Best regards,

Sylvain

span-based 方法，为什么有些数据集是BIO类标签标注的？

大佬，你好，有个问题，如题。span-based 方法，为什么有些数据集是BIO类标签标注的？比如Resume？

你好，请问如果想将Boundary Smoothing应用于其他模型，应该提取哪一部分的代码以应用

是只需要修改交叉熵损失哪里么，怎么能保证Boundary Smoothing的正常运行，期待您的回复，谢谢

请问我想在您的boundary smoothing 实体抽取代码上进行修改并且重新训练我应该在命令行中输入什么指令重新训练模型？

数据发布时间问题

你好看您论文：A Unified Framework of Medical Information Annotation and Extraction for Chinese Clinical Text 受益匪浅，非常感谢。
在论文中，有提到会发布相关数据，请问是预计什么时候发布呢。
For researchers, we provide a benchmark to evaluate their information extraction algorithms. The corpus is from real EMRs, and carefully annotated by experienced physicians, covering a range of medical departments and EMR sections.

缺少文件

我在运行您的代码时，会报错 FileNotFoundError: [Errno 2] No such file or directory: 'assets/vectors/glove.6B.100d.txt''，找了半天也没有看到有这个文件。希望在您方便的时候回复下，感谢您的工作。

Python版本问题

你好，请问运行的python版本要求是多少？谢谢

how to process the ACE2005 data in "ace-lu2015emnlp"

Hi, I have the original ACE2005 dataset. But I found it's hard to process them to "*.data". I
read the code in your paper "Joint mention extraction and classification with mention hypergraphs" which is mentioned in the your paper. I could not find the code about processing. If you can tell me a clear way, I would appreciate it. Thanks and regards.

请问复现boundary smoothing时的python版本、pytorch版本、cudatoolkit版本可以更高吗

机器 cuda version 12.0，使用readme配置环境导入torch会报错/lib/../../../../libcublas.so.11: symbol free_gemm_select version libcublasLt.so.11 not defined in file libcublasLt.so.11 with link time reference

有关Boundary smoothing

您好，我想问一下代码里边有关Boundary smoothing的部分，是对于损失函数的改进么，我看了代码，感觉是对损失函数上边的改进，您能否帮我解答一下，这个Boundary smoothing部分的代码，谢谢您了！

ERROR: ResolutionImpossible

INFO: pip is looking at multiple versions of to determine which version is compatible with other requirements. This could take a while.
INFO: pip is looking at multiple versions of eznlp to determine which version is compatible with other requirements. This could take a while.
ERROR: Cannot install eznlp and eznlp==0.2.0 because these package versions have conflicting dependencies.

The conflict is caused by:
eznlp 0.2.0 depends on transformers==4.3.2
allennlp 2.0.1 depends on transformers<4.3 and >=4.1

To fix this you could try to:

loosen the range of package versions you've specified
remove package versions to allow pip attempt to solve the dependency conflict

ERROR: ResolutionImpossible: for help visit https://pip.pypa.io/en/latest/user_guide/#fixing-conflicting-dependencies

The hyper-parameters for reproducing results for Conll2003 and OntoNotes 5

Hi, after reading your paper "Boundary Smoothing for Named Entity Recognition", I was glad to see that the performance was further promoted by your method. I want to reproduce these results, is there any detailed introduction to rerun the experiments by the eznlp framework, especially the specific hyper-parameters used.

请问用Flair进行embedding时的flair/news-forward-0.4.1.pt与flair/news-backward-0.4.1.pt这两个文件从哪里获取？

wrong about "GENIA" process method

hi, writer! i have read paper 《Deep Span Representations for Named Entity Recognition》。but the dataset GENIA which Process following Lu and Roth (2015) is wrong. Because Lu and Roth (2015) dont use genia in this paper. So they dont have the process method. can you provide the propcessed "genia" dataset ? thanks a lot

Boundary smoothing 时候，两个entity正好在旁边怎么办

你好，我想请教一个理论性的问题

我假如有一个entity，class是entity A的，他的位置是句子中的第3个字，span position就是（3，3）。旁边又有另外一个entity，是entity type B，位置是（4，4）

然后我现在做boundary smoothing（of distance 2， epsilon=0.2），那entity A的probability就是1-0.2=0.8，旁边的，譬如（4，4）就会被分到一些些，epsilon/num_of_surrounding_spans
这样就撞了（4，4）的entity B，这种情况会如何处理？（同理对于这个entity b，做smoothing的时候他也会撞到（3，3）的entity A

关于复现Boundary Smoothing时超参数的一些问题

您好，我最近在re-implement您的《Boundary Smoothing for Named Entity Recognition》这篇论文提出的BS方法。
我发现按照您提供的超参数设置，在Ontonotes5和Conll2003这两个数据集上，不论是BiaffineNER的baseline还是加上BS的实验结果都比论文中报告的要低。但是Biaffine+BS的确比Biaffine Baseline的结果要好。根据我的调查，我推测原因可能在于数据处理：
我发现您的运行脚本中有个叫doc-level的arg，请问这个arg与《Named Entity Recognition as Dependency Parsing》也就是baseline对应的这篇论文中的“context dependent embeddings for a target token with 64 surroundings tokens each side ”说的是一回事吗？这一部处理是否对模型的性能产生较大的提升，以至于我没有进行这一步处理而直接采用BERT Encoder的输出会导致与论文汇报的结果有较大的差距。
目前我在OntoNotes5上bs_epsilon采用的是0.2， smoothing_size采用的是1； Conll2003上bs_epsilon采用的是0.3， smoothing_size采用的是1

WeiboNER 评估

您好，我跑了你的代码，没做任何修改，就用公共数据集 Weibo NER 评估，发现指标很低. 命令如下：

python scripts/entity_recognition.py --dataset WeiboNER --ck_decoder boundary_selection

得到的结果只有 40% 多，参数没做任何变动，日志如下：


(eznlp) root@341149:/data3/min/eznlp# python scripts/entity_recognition.py --dataset WeiboNER --ck_decoder boundary_selection
[2022-07-16 10:03:33 INFO] ============================================= Starting =============================================
[2022-07-16 10:03:33 INFO] scripts/entity_recognition.py --dataset WeiboNER --ck_decoder boundary_selection
[2022-07-16 10:03:33 INFO] {'affine_arch': 'FFN',
 'agg_mode': 'max_pooling',
 'batch_size': 64,
 'bert_arch': 'None',
 'bert_drop_rate': 0.2,
 'char_arch': 'None',
 'ck_decoder': 'boundary_selection',
 'ck_size_emb_dim': 25,
 'corrupt_rate': 0.0,
 'dataset': 'WeiboNER',
 'doc_level': False,
 'drop_rate': 0.5,
 'emb_dim': 100,
 'emb_freeze': False,
 'enc_arch': 'LSTM',
 'finetune_lr': 2e-05,
 'fl_gamma': 0.0,
 'grad_clip': 5.0,
 'hard_neg_sampling_rate': 1.0,
 'hard_neg_sampling_size': 5,
 'hid_dim': 200,
 'log_terminal': True,
 'lr': 0.001,
 'max_span_size': 10,
 'neg_sampling_rate': 1.0,
 'num_epochs': 100,
 'num_grad_acc_steps': 1,
 'num_layers': 1,
 'num_neg_chunks': 100,
 'optimizer': 'AdamW',
 'pdb': False,
 'pipeline': False,
 'profile': False,
 'save_preds': False,
 'sb_adj_factor': 1.0,
 'sb_epsilon': 0.0,
 'sb_size': 1,
 'scheduler': 'None',
 'scheme': 'BIOES',
 'seed': 515,
 'sl_epsilon': 0.0,
 'train_with_dev': False,
 'use_amp': False,
 'use_biaffine': True,
 'use_bigram': False,
 'use_crf': True,
 'use_elmo': False,
 'use_flair': False,
 'use_interm1': False,
 'use_interm2': False,
 'use_locked_drop': False,
 'use_softlexicon': False,
 'use_softword': False}
[2022-07-16 10:03:33 INFO] -------------------------------------------- Preparing ---------------------------------------------
[2022-07-16 10:03:33 INFO] Automatically allocating device...
[2022-07-16 10:03:33 INFO] Cuda device `cuda:2` with free memory 28737 MiB successfully allocated, device `cuda:2` returned
[2022-07-16 10:03:37 INFO] No nested chunks detected, only flat chunks are allowed in decoding...
[2022-07-16 10:03:37 INFO] The dataset consists 1,350 sequences
The average `tokens` length is 54.7
The maximum `tokens` length is 175
The dataset has 1,895 chunks of 8 types
[2022-07-16 10:03:37 INFO] --------------------------------------------- Building ---------------------------------------------
[2022-07-16 10:03:37 INFO] Embeddings initialized with randomized vectors 
Vector average absolute value: 0.0866
[2022-07-16 10:03:37 INFO] Embeddings initialized with randomized vectors 
Vector average absolute value: 0.1732
[2022-07-16 10:03:37 INFO] The model has 573,484 parameters, in which 573,484 are trainable and 0 are frozen.
[2022-07-16 10:03:37 INFO] --------------------------------------------- Training ---------------------------------------------
[2022-07-16 10:03:37 INFO] Grouped parameters (573,484) == Model parameters (573,484)
[2022-07-16 10:03:59 INFO] Epoch: 1 | Step: 22 | LR: (0.000020/0.001000)
[2022-07-16 10:03:59 INFO] 	Train Loss: 1301.205 | Train Metrics: 0.16% | Elapsed Time: 0m 22s
[2022-07-16 10:04:02 INFO] 	Dev.  Loss: 28.731 | Dev.  Metrics: 0.00% | Elapsed Time: 0m 2s
[2022-07-16 10:04:23 INFO] Epoch: 2 | Step: 44 | LR: (0.000020/0.001000)
[2022-07-16 10:04:23 INFO] 	Train Loss: 15.072 | Train Metrics: 0.00% | Elapsed Time: 0m 21s
[2022-07-16 10:04:26 INFO] 	Dev.  Loss: 17.105 | Dev.  Metrics: 0.00% | Elapsed Time: 0m 2s
[2022-07-16 10:04:47 INFO] Epoch: 3 | Step: 66 | LR: (0.000020/0.001000)
[2022-07-16 10:04:47 INFO] 	Train Loss: 11.948 | Train Metrics: 0.00% | Elapsed Time: 0m 20s
[2022-07-16 10:04:50 INFO] 	Dev.  Loss: 13.938 | Dev.  Metrics: 0.00% | Elapsed Time: 0m 2s
[2022-07-16 10:05:11 INFO] Epoch: 4 | Step: 88 | LR: (0.000020/0.001000)
[2022-07-16 10:05:11 INFO] 	Train Loss: 9.826 | Train Metrics: 0.00% | Elapsed Time: 0m 20s
[2022-07-16 10:05:14 INFO] 	Dev.  Loss: 11.808 | Dev.  Metrics: 0.00% | Elapsed Time: 0m 2s
[2022-07-16 10:05:35 INFO] Epoch: 5 | Step: 110 | LR: (0.000020/0.001000)
[2022-07-16 10:05:35 INFO] 	Train Loss: 8.653 | Train Metrics: 0.42% | Elapsed Time: 0m 21s
[2022-07-16 10:05:38 INFO] 	Dev.  Loss: 10.595 | Dev.  Metrics: 19.86% | Elapsed Time: 0m 2s
[2022-07-16 10:05:59 INFO] Epoch: 6 | Step: 132 | LR: (0.000020/0.001000)
[2022-07-16 10:05:59 INFO] 	Train Loss: 7.556 | Train Metrics: 2.19% | Elapsed Time: 0m 21s
[2022-07-16 10:06:02 INFO] 	Dev.  Loss: 9.792 | Dev.  Metrics: 35.02% | Elapsed Time: 0m 2s
[2022-07-16 10:06:23 INFO] Epoch: 7 | Step: 154 | LR: (0.000020/0.001000)
[2022-07-16 10:06:23 INFO] 	Train Loss: 6.821 | Train Metrics: 7.59% | Elapsed Time: 0m 21s
[2022-07-16 10:06:26 INFO] 	Dev.  Loss: 8.919 | Dev.  Metrics: 43.63% | Elapsed Time: 0m 2s
[2022-07-16 10:06:48 INFO] Epoch: 8 | Step: 176 | LR: (0.000020/0.001000)
[2022-07-16 10:06:48 INFO] 	Train Loss: 6.231 | Train Metrics: 14.56% | Elapsed Time: 0m 21s
[2022-07-16 10:06:50 INFO] 	Dev.  Loss: 8.451 | Dev.  Metrics: 47.48% | Elapsed Time: 0m 2s
[2022-07-16 10:07:11 INFO] Epoch: 9 | Step: 198 | LR: (0.000020/0.001000)
[2022-07-16 10:07:11 INFO] 	Train Loss: 5.949 | Train Metrics: 22.35% | Elapsed Time: 0m 20s
[2022-07-16 10:07:14 INFO] 	Dev.  Loss: 8.130 | Dev.  Metrics: 47.00% | Elapsed Time: 0m 2s
[2022-07-16 10:07:35 INFO] Epoch: 10 | Step: 220 | LR: (0.000020/0.001000)
[2022-07-16 10:07:35 INFO] 	Train Loss: 5.569 | Train Metrics: 25.16% | Elapsed Time: 0m 21s
[2022-07-16 10:07:38 INFO] 	Dev.  Loss: 7.772 | Dev.  Metrics: 49.85% | Elapsed Time: 0m 2s
[2022-07-16 10:07:59 INFO] Epoch: 11 | Step: 242 | LR: (0.000020/0.001000)
[2022-07-16 10:07:59 INFO] 	Train Loss: 5.346 | Train Metrics: 25.99% | Elapsed Time: 0m 21s
[2022-07-16 10:08:02 INFO] 	Dev.  Loss: 7.443 | Dev.  Metrics: 51.42% | Elapsed Time: 0m 2s
[2022-07-16 10:08:23 INFO] Epoch: 12 | Step: 264 | LR: (0.000020/0.001000)
[2022-07-16 10:08:23 INFO] 	Train Loss: 5.321 | Train Metrics: 28.73% | Elapsed Time: 0m 21s
[2022-07-16 10:08:26 INFO] 	Dev.  Loss: 7.445 | Dev.  Metrics: 50.94% | Elapsed Time: 0m 2s
[2022-07-16 10:08:48 INFO] Epoch: 13 | Step: 286 | LR: (0.000020/0.001000)
[2022-07-16 10:08:48 INFO] 	Train Loss: 4.802 | Train Metrics: 33.44% | Elapsed Time: 0m 21s
[2022-07-16 10:08:51 INFO] 	Dev.  Loss: 7.038 | Dev.  Metrics: 51.24% | Elapsed Time: 0m 2s
[2022-07-16 10:09:12 INFO] Epoch: 14 | Step: 308 | LR: (0.000020/0.001000)
[2022-07-16 10:09:12 INFO] 	Train Loss: 4.676 | Train Metrics: 36.31% | Elapsed Time: 0m 20s
[2022-07-16 10:09:15 INFO] 	Dev.  Loss: 7.063 | Dev.  Metrics: 51.59% | Elapsed Time: 0m 2s
[2022-07-16 10:09:36 INFO] Epoch: 15 | Step: 330 | LR: (0.000020/0.001000)
[2022-07-16 10:09:36 INFO] 	Train Loss: 4.749 | Train Metrics: 38.52% | Elapsed Time: 0m 20s
[2022-07-16 10:09:38 INFO] 	Dev.  Loss: 6.929 | Dev.  Metrics: 51.86% | Elapsed Time: 0m 2s
[2022-07-16 10:10:00 INFO] Epoch: 16 | Step: 352 | LR: (0.000020/0.001000)
[2022-07-16 10:10:00 INFO] 	Train Loss: 4.670 | Train Metrics: 40.03% | Elapsed Time: 0m 21s
[2022-07-16 10:10:03 INFO] 	Dev.  Loss: 6.693 | Dev.  Metrics: 53.56% | Elapsed Time: 0m 2s
[2022-07-16 10:10:24 INFO] Epoch: 17 | Step: 374 | LR: (0.000020/0.001000)
[2022-07-16 10:10:24 INFO] 	Train Loss: 4.484 | Train Metrics: 41.32% | Elapsed Time: 0m 21s
[2022-07-16 10:10:27 INFO] 	Dev.  Loss: 6.942 | Dev.  Metrics: 53.20% | Elapsed Time: 0m 3s
[2022-07-16 10:10:48 INFO] Epoch: 18 | Step: 396 | LR: (0.000020/0.001000)
[2022-07-16 10:10:48 INFO] 	Train Loss: 4.276 | Train Metrics: 43.86% | Elapsed Time: 0m 20s
[2022-07-16 10:10:51 INFO] 	Dev.  Loss: 6.542 | Dev.  Metrics: 53.64% | Elapsed Time: 0m 3s
[2022-07-16 10:11:12 INFO] Epoch: 19 | Step: 418 | LR: (0.000020/0.001000)
[2022-07-16 10:11:12 INFO] 	Train Loss: 4.042 | Train Metrics: 43.79% | Elapsed Time: 0m 21s
[2022-07-16 10:11:15 INFO] 	Dev.  Loss: 6.566 | Dev.  Metrics: 53.28% | Elapsed Time: 0m 2s
[2022-07-16 10:11:36 INFO] Epoch: 20 | Step: 440 | LR: (0.000020/0.001000)
[2022-07-16 10:11:36 INFO] 	Train Loss: 3.863 | Train Metrics: 45.25% | Elapsed Time: 0m 20s
[2022-07-16 10:11:39 INFO] 	Dev.  Loss: 6.259 | Dev.  Metrics: 54.21% | Elapsed Time: 0m 2s
[2022-07-16 10:12:00 INFO] Epoch: 21 | Step: 462 | LR: (0.000020/0.001000)
[2022-07-16 10:12:00 INFO] 	Train Loss: 3.860 | Train Metrics: 47.51% | Elapsed Time: 0m 21s
[2022-07-16 10:12:03 INFO] 	Dev.  Loss: 6.482 | Dev.  Metrics: 54.20% | Elapsed Time: 0m 2s
[2022-07-16 10:12:24 INFO] Epoch: 22 | Step: 484 | LR: (0.000020/0.001000)
[2022-07-16 10:12:24 INFO] 	Train Loss: 3.939 | Train Metrics: 49.15% | Elapsed Time: 0m 21s
[2022-07-16 10:12:27 INFO] 	Dev.  Loss: 6.268 | Dev.  Metrics: 54.60% | Elapsed Time: 0m 2s
[2022-07-16 10:12:48 INFO] Epoch: 23 | Step: 506 | LR: (0.000020/0.001000)
[2022-07-16 10:12:48 INFO] 	Train Loss: 3.862 | Train Metrics: 49.88% | Elapsed Time: 0m 21s
[2022-07-16 10:12:51 INFO] 	Dev.  Loss: 6.171 | Dev.  Metrics: 54.83% | Elapsed Time: 0m 2s
[2022-07-16 10:13:12 INFO] Epoch: 24 | Step: 528 | LR: (0.000020/0.001000)
[2022-07-16 10:13:12 INFO] 	Train Loss: 3.753 | Train Metrics: 48.67% | Elapsed Time: 0m 20s
[2022-07-16 10:13:15 INFO] 	Dev.  Loss: 6.303 | Dev.  Metrics: 54.37% | Elapsed Time: 0m 2s
[2022-07-16 10:13:36 INFO] Epoch: 25 | Step: 550 | LR: (0.000020/0.001000)
[2022-07-16 10:13:36 INFO] 	Train Loss: 3.477 | Train Metrics: 48.90% | Elapsed Time: 0m 21s
[2022-07-16 10:13:39 INFO] 	Dev.  Loss: 6.153 | Dev.  Metrics: 55.25% | Elapsed Time: 0m 2s
[2022-07-16 10:14:00 INFO] Epoch: 26 | Step: 572 | LR: (0.000020/0.001000)
[2022-07-16 10:14:00 INFO] 	Train Loss: 3.443 | Train Metrics: 49.56% | Elapsed Time: 0m 21s
[2022-07-16 10:14:03 INFO] 	Dev.  Loss: 6.249 | Dev.  Metrics: 53.99% | Elapsed Time: 0m 2s
[2022-07-16 10:14:24 INFO] Epoch: 27 | Step: 594 | LR: (0.000020/0.001000)
[2022-07-16 10:14:24 INFO] 	Train Loss: 3.445 | Train Metrics: 51.67% | Elapsed Time: 0m 21s
[2022-07-16 10:14:27 INFO] 	Dev.  Loss: 6.230 | Dev.  Metrics: 54.82% | Elapsed Time: 0m 2s
[2022-07-16 10:14:48 INFO] Epoch: 28 | Step: 616 | LR: (0.000020/0.001000)
[2022-07-16 10:14:48 INFO] 	Train Loss: 3.573 | Train Metrics: 52.76% | Elapsed Time: 0m 20s
[2022-07-16 10:14:51 INFO] 	Dev.  Loss: 6.234 | Dev.  Metrics: 55.97% | Elapsed Time: 0m 2s
[2022-07-16 10:15:12 INFO] Epoch: 29 | Step: 638 | LR: (0.000020/0.001000)
[2022-07-16 10:15:12 INFO] 	Train Loss: 3.328 | Train Metrics: 55.12% | Elapsed Time: 0m 21s
[2022-07-16 10:15:15 INFO] 	Dev.  Loss: 6.255 | Dev.  Metrics: 55.63% | Elapsed Time: 0m 2s
[2022-07-16 10:15:35 INFO] Epoch: 30 | Step: 660 | LR: (0.000020/0.001000)
[2022-07-16 10:15:35 INFO] 	Train Loss: 3.313 | Train Metrics: 53.71% | Elapsed Time: 0m 20s
[2022-07-16 10:15:38 INFO] 	Dev.  Loss: 6.213 | Dev.  Metrics: 55.51% | Elapsed Time: 0m 2s
[2022-07-16 10:16:00 INFO] Epoch: 31 | Step: 682 | LR: (0.000020/0.001000)
[2022-07-16 10:16:00 INFO] 	Train Loss: 3.158 | Train Metrics: 55.92% | Elapsed Time: 0m 21s
[2022-07-16 10:16:02 INFO] 	Dev.  Loss: 6.180 | Dev.  Metrics: 55.45% | Elapsed Time: 0m 2s
[2022-07-16 10:16:23 INFO] Epoch: 32 | Step: 704 | LR: (0.000020/0.001000)
[2022-07-16 10:16:23 INFO] 	Train Loss: 3.223 | Train Metrics: 56.11% | Elapsed Time: 0m 21s
[2022-07-16 10:16:26 INFO] 	Dev.  Loss: 6.209 | Dev.  Metrics: 56.02% | Elapsed Time: 0m 2s
[2022-07-16 10:16:47 INFO] Epoch: 33 | Step: 726 | LR: (0.000020/0.001000)
[2022-07-16 10:16:47 INFO] 	Train Loss: 3.114 | Train Metrics: 56.55% | Elapsed Time: 0m 20s
[2022-07-16 10:16:50 INFO] 	Dev.  Loss: 6.277 | Dev.  Metrics: 55.26% | Elapsed Time: 0m 3s
[2022-07-16 10:17:12 INFO] Epoch: 34 | Step: 748 | LR: (0.000020/0.001000)
[2022-07-16 10:17:12 INFO] 	Train Loss: 2.960 | Train Metrics: 57.34% | Elapsed Time: 0m 21s
[2022-07-16 10:17:14 INFO] 	Dev.  Loss: 6.176 | Dev.  Metrics: 55.03% | Elapsed Time: 0m 2s
[2022-07-16 10:17:35 INFO] Epoch: 35 | Step: 770 | LR: (0.000020/0.001000)
[2022-07-16 10:17:35 INFO] 	Train Loss: 3.043 | Train Metrics: 56.51% | Elapsed Time: 0m 20s
[2022-07-16 10:17:38 INFO] 	Dev.  Loss: 6.106 | Dev.  Metrics: 56.52% | Elapsed Time: 0m 2s
[2022-07-16 10:18:00 INFO] Epoch: 36 | Step: 792 | LR: (0.000020/0.001000)
[2022-07-16 10:18:00 INFO] 	Train Loss: 2.964 | Train Metrics: 56.14% | Elapsed Time: 0m 21s
[2022-07-16 10:18:02 INFO] 	Dev.  Loss: 6.239 | Dev.  Metrics: 56.06% | Elapsed Time: 0m 2s
[2022-07-16 10:18:24 INFO] Epoch: 37 | Step: 814 | LR: (0.000020/0.001000)
[2022-07-16 10:18:24 INFO] 	Train Loss: 3.106 | Train Metrics: 57.23% | Elapsed Time: 0m 21s
[2022-07-16 10:18:27 INFO] 	Dev.  Loss: 6.295 | Dev.  Metrics: 56.07% | Elapsed Time: 0m 2s
[2022-07-16 10:18:48 INFO] Epoch: 38 | Step: 836 | LR: (0.000020/0.001000)
[2022-07-16 10:18:48 INFO] 	Train Loss: 2.903 | Train Metrics: 59.61% | Elapsed Time: 0m 21s
[2022-07-16 10:18:51 INFO] 	Dev.  Loss: 6.185 | Dev.  Metrics: 56.91% | Elapsed Time: 0m 2s
[2022-07-16 10:19:11 INFO] Epoch: 39 | Step: 858 | LR: (0.000020/0.001000)
[2022-07-16 10:19:11 INFO] 	Train Loss: 2.865 | Train Metrics: 59.19% | Elapsed Time: 0m 20s
[2022-07-16 10:19:14 INFO] 	Dev.  Loss: 6.312 | Dev.  Metrics: 56.49% | Elapsed Time: 0m 2s
[2022-07-16 10:19:35 INFO] Epoch: 40 | Step: 880 | LR: (0.000020/0.001000)
[2022-07-16 10:19:35 INFO] 	Train Loss: 2.830 | Train Metrics: 60.04% | Elapsed Time: 0m 21s
[2022-07-16 10:19:38 INFO] 	Dev.  Loss: 6.221 | Dev.  Metrics: 57.10% | Elapsed Time: 0m 2s
[2022-07-16 10:19:59 INFO] Epoch: 41 | Step: 902 | LR: (0.000020/0.001000)
[2022-07-16 10:19:59 INFO] 	Train Loss: 2.834 | Train Metrics: 59.83% | Elapsed Time: 0m 21s
[2022-07-16 10:20:02 INFO] 	Dev.  Loss: 6.211 | Dev.  Metrics: 56.99% | Elapsed Time: 0m 2s
[2022-07-16 10:20:23 INFO] Epoch: 42 | Step: 924 | LR: (0.000020/0.001000)
[2022-07-16 10:20:23 INFO] 	Train Loss: 2.842 | Train Metrics: 59.99% | Elapsed Time: 0m 20s
[2022-07-16 10:20:26 INFO] 	Dev.  Loss: 6.235 | Dev.  Metrics: 56.00% | Elapsed Time: 0m 2s
[2022-07-16 10:20:47 INFO] Epoch: 43 | Step: 946 | LR: (0.000020/0.001000)
[2022-07-16 10:20:47 INFO] 	Train Loss: 2.693 | Train Metrics: 61.06% | Elapsed Time: 0m 21s
[2022-07-16 10:20:50 INFO] 	Dev.  Loss: 6.214 | Dev.  Metrics: 56.48% | Elapsed Time: 0m 2s
[2022-07-16 10:21:11 INFO] Epoch: 44 | Step: 968 | LR: (0.000020/0.001000)
[2022-07-16 10:21:11 INFO] 	Train Loss: 2.590 | Train Metrics: 61.71% | Elapsed Time: 0m 21s
[2022-07-16 10:21:14 INFO] 	Dev.  Loss: 6.192 | Dev.  Metrics: 56.55% | Elapsed Time: 0m 2s
[2022-07-16 10:21:35 INFO] Epoch: 45 | Step: 990 | LR: (0.000020/0.001000)
[2022-07-16 10:21:35 INFO] 	Train Loss: 2.672 | Train Metrics: 60.78% | Elapsed Time: 0m 21s
[2022-07-16 10:21:38 INFO] 	Dev.  Loss: 6.269 | Dev.  Metrics: 55.63% | Elapsed Time: 0m 2s
[2022-07-16 10:21:59 INFO] Epoch: 46 | Step: 1012 | LR: (0.000020/0.001000)
[2022-07-16 10:21:59 INFO] 	Train Loss: 2.705 | Train Metrics: 61.97% | Elapsed Time: 0m 21s
[2022-07-16 10:22:02 INFO] 	Dev.  Loss: 6.377 | Dev.  Metrics: 56.11% | Elapsed Time: 0m 2s
[2022-07-16 10:22:23 INFO] Epoch: 47 | Step: 1034 | LR: (0.000020/0.001000)
[2022-07-16 10:22:23 INFO] 	Train Loss: 2.740 | Train Metrics: 63.14% | Elapsed Time: 0m 20s
[2022-07-16 10:22:26 INFO] 	Dev.  Loss: 6.294 | Dev.  Metrics: 56.55% | Elapsed Time: 0m 2s
[2022-07-16 10:22:47 INFO] Epoch: 48 | Step: 1056 | LR: (0.000020/0.001000)
[2022-07-16 10:22:47 INFO] 	Train Loss: 2.670 | Train Metrics: 63.13% | Elapsed Time: 0m 21s
[2022-07-16 10:22:50 INFO] 	Dev.  Loss: 6.371 | Dev.  Metrics: 56.56% | Elapsed Time: 0m 2s
[2022-07-16 10:23:11 INFO] Epoch: 49 | Step: 1078 | LR: (0.000020/0.001000)
[2022-07-16 10:23:11 INFO] 	Train Loss: 2.502 | Train Metrics: 63.05% | Elapsed Time: 0m 20s
[2022-07-16 10:23:14 INFO] 	Dev.  Loss: 6.335 | Dev.  Metrics: 56.91% | Elapsed Time: 0m 2s
[2022-07-16 10:23:35 INFO] Epoch: 50 | Step: 1100 | LR: (0.000020/0.001000)
[2022-07-16 10:23:35 INFO] 	Train Loss: 2.510 | Train Metrics: 63.35% | Elapsed Time: 0m 21s
[2022-07-16 10:23:38 INFO] 	Dev.  Loss: 6.433 | Dev.  Metrics: 56.42% | Elapsed Time: 0m 2s
[2022-07-16 10:23:59 INFO] Epoch: 51 | Step: 1122 | LR: (0.000020/0.001000)
[2022-07-16 10:23:59 INFO] 	Train Loss: 2.498 | Train Metrics: 62.77% | Elapsed Time: 0m 21s
[2022-07-16 10:24:02 INFO] 	Dev.  Loss: 6.462 | Dev.  Metrics: 56.16% | Elapsed Time: 0m 2s
[2022-07-16 10:24:23 INFO] Epoch: 52 | Step: 1144 | LR: (0.000020/0.001000)
[2022-07-16 10:24:23 INFO] 	Train Loss: 2.385 | Train Metrics: 65.18% | Elapsed Time: 0m 21s
[2022-07-16 10:24:26 INFO] 	Dev.  Loss: 6.312 | Dev.  Metrics: 56.17% | Elapsed Time: 0m 2s
[2022-07-16 10:24:48 INFO] Epoch: 53 | Step: 1166 | LR: (0.000020/0.001000)
[2022-07-16 10:24:48 INFO] 	Train Loss: 2.470 | Train Metrics: 63.45% | Elapsed Time: 0m 21s
[2022-07-16 10:24:50 INFO] 	Dev.  Loss: 6.366 | Dev.  Metrics: 56.56% | Elapsed Time: 0m 2s
[2022-07-16 10:25:11 INFO] Epoch: 54 | Step: 1188 | LR: (0.000020/0.001000)
[2022-07-16 10:25:11 INFO] 	Train Loss: 2.436 | Train Metrics: 65.99% | Elapsed Time: 0m 20s
[2022-07-16 10:25:14 INFO] 	Dev.  Loss: 6.447 | Dev.  Metrics: 55.93% | Elapsed Time: 0m 2s
[2022-07-16 10:25:36 INFO] Epoch: 55 | Step: 1210 | LR: (0.000020/0.001000)
[2022-07-16 10:25:36 INFO] 	Train Loss: 2.386 | Train Metrics: 65.98% | Elapsed Time: 0m 21s
[2022-07-16 10:25:38 INFO] 	Dev.  Loss: 6.478 | Dev.  Metrics: 56.84% | Elapsed Time: 0m 2s
[2022-07-16 10:26:00 INFO] Epoch: 56 | Step: 1232 | LR: (0.000020/0.001000)
[2022-07-16 10:26:00 INFO] 	Train Loss: 2.431 | Train Metrics: 63.24% | Elapsed Time: 0m 21s
[2022-07-16 10:26:02 INFO] 	Dev.  Loss: 6.480 | Dev.  Metrics: 56.27% | Elapsed Time: 0m 2s
[2022-07-16 10:26:24 INFO] Epoch: 57 | Step: 1254 | LR: (0.000020/0.001000)
[2022-07-16 10:26:24 INFO] 	Train Loss: 2.452 | Train Metrics: 66.91% | Elapsed Time: 0m 21s
[2022-07-16 10:26:27 INFO] 	Dev.  Loss: 6.421 | Dev.  Metrics: 56.55% | Elapsed Time: 0m 2s
[2022-07-16 10:26:48 INFO] Epoch: 58 | Step: 1276 | LR: (0.000020/0.001000)
[2022-07-16 10:26:48 INFO] 	Train Loss: 2.371 | Train Metrics: 65.19% | Elapsed Time: 0m 21s
[2022-07-16 10:26:51 INFO] 	Dev.  Loss: 6.527 | Dev.  Metrics: 56.02% | Elapsed Time: 0m 2s
[2022-07-16 10:27:12 INFO] Epoch: 59 | Step: 1298 | LR: (0.000020/0.001000)
[2022-07-16 10:27:12 INFO] 	Train Loss: 2.356 | Train Metrics: 68.34% | Elapsed Time: 0m 21s
[2022-07-16 10:27:15 INFO] 	Dev.  Loss: 6.513 | Dev.  Metrics: 56.45% | Elapsed Time: 0m 2s
[2022-07-16 10:27:36 INFO] Epoch: 60 | Step: 1320 | LR: (0.000020/0.001000)
[2022-07-16 10:27:36 INFO] 	Train Loss: 2.371 | Train Metrics: 66.42% | Elapsed Time: 0m 20s
[2022-07-16 10:27:39 INFO] 	Dev.  Loss: 6.597 | Dev.  Metrics: 56.42% | Elapsed Time: 0m 2s
[2022-07-16 10:27:59 INFO] Epoch: 61 | Step: 1342 | LR: (0.000020/0.001000)
[2022-07-16 10:27:59 INFO] 	Train Loss: 2.474 | Train Metrics: 65.50% | Elapsed Time: 0m 20s
[2022-07-16 10:28:02 INFO] 	Dev.  Loss: 6.492 | Dev.  Metrics: 57.06% | Elapsed Time: 0m 2s
[2022-07-16 10:28:23 INFO] Epoch: 62 | Step: 1364 | LR: (0.000020/0.001000)
[2022-07-16 10:28:23 INFO] 	Train Loss: 2.238 | Train Metrics: 66.65% | Elapsed Time: 0m 20s
[2022-07-16 10:28:26 INFO] 	Dev.  Loss: 6.514 | Dev.  Metrics: 57.42% | Elapsed Time: 0m 2s
[2022-07-16 10:28:48 INFO] Epoch: 63 | Step: 1386 | LR: (0.000020/0.001000)
[2022-07-16 10:28:48 INFO] 	Train Loss: 2.465 | Train Metrics: 68.83% | Elapsed Time: 0m 21s
[2022-07-16 10:28:51 INFO] 	Dev.  Loss: 6.601 | Dev.  Metrics: 57.10% | Elapsed Time: 0m 2s
[2022-07-16 10:29:12 INFO] Epoch: 64 | Step: 1408 | LR: (0.000020/0.001000)
[2022-07-16 10:29:12 INFO] 	Train Loss: 2.205 | Train Metrics: 67.29% | Elapsed Time: 0m 21s
[2022-07-16 10:29:15 INFO] 	Dev.  Loss: 6.528 | Dev.  Metrics: 57.06% | Elapsed Time: 0m 2s
[2022-07-16 10:29:36 INFO] Epoch: 65 | Step: 1430 | LR: (0.000020/0.001000)
[2022-07-16 10:29:36 INFO] 	Train Loss: 2.245 | Train Metrics: 65.63% | Elapsed Time: 0m 21s
[2022-07-16 10:29:39 INFO] 	Dev.  Loss: 6.595 | Dev.  Metrics: 56.95% | Elapsed Time: 0m 2s
[2022-07-16 10:30:00 INFO] Epoch: 66 | Step: 1452 | LR: (0.000020/0.001000)
[2022-07-16 10:30:00 INFO] 	Train Loss: 2.277 | Train Metrics: 67.15% | Elapsed Time: 0m 20s
[2022-07-16 10:30:03 INFO] 	Dev.  Loss: 6.653 | Dev.  Metrics: 57.38% | Elapsed Time: 0m 2s
[2022-07-16 10:30:24 INFO] Epoch: 67 | Step: 1474 | LR: (0.000020/0.001000)
[2022-07-16 10:30:24 INFO] 	Train Loss: 2.320 | Train Metrics: 67.60% | Elapsed Time: 0m 21s
[2022-07-16 10:30:27 INFO] 	Dev.  Loss: 6.530 | Dev.  Metrics: 56.99% | Elapsed Time: 0m 2s
[2022-07-16 10:30:49 INFO] Epoch: 68 | Step: 1496 | LR: (0.000020/0.001000)
[2022-07-16 10:30:49 INFO] 	Train Loss: 2.272 | Train Metrics: 68.62% | Elapsed Time: 0m 21s
[2022-07-16 10:30:51 INFO] 	Dev.  Loss: 6.672 | Dev.  Metrics: 56.64% | Elapsed Time: 0m 2s
[2022-07-16 10:31:13 INFO] Epoch: 69 | Step: 1518 | LR: (0.000020/0.001000)
[2022-07-16 10:31:13 INFO] 	Train Loss: 2.197 | Train Metrics: 70.27% | Elapsed Time: 0m 21s
[2022-07-16 10:31:16 INFO] 	Dev.  Loss: 6.659 | Dev.  Metrics: 56.91% | Elapsed Time: 0m 2s
[2022-07-16 10:31:36 INFO] Epoch: 70 | Step: 1540 | LR: (0.000020/0.001000)
[2022-07-16 10:31:36 INFO] 	Train Loss: 2.333 | Train Metrics: 68.11% | Elapsed Time: 0m 20s
[2022-07-16 10:31:39 INFO] 	Dev.  Loss: 6.881 | Dev.  Metrics: 56.88% | Elapsed Time: 0m 2s
[2022-07-16 10:32:00 INFO] Epoch: 71 | Step: 1562 | LR: (0.000020/0.001000)
[2022-07-16 10:32:00 INFO] 	Train Loss: 2.146 | Train Metrics: 68.20% | Elapsed Time: 0m 21s
[2022-07-16 10:32:03 INFO] 	Dev.  Loss: 6.757 | Dev.  Metrics: 56.83% | Elapsed Time: 0m 2s
[2022-07-16 10:32:25 INFO] Epoch: 72 | Step: 1584 | LR: (0.000020/0.001000)
[2022-07-16 10:32:25 INFO] 	Train Loss: 2.102 | Train Metrics: 69.26% | Elapsed Time: 0m 21s
[2022-07-16 10:32:28 INFO] 	Dev.  Loss: 6.784 | Dev.  Metrics: 57.22% | Elapsed Time: 0m 2s
[2022-07-16 10:32:49 INFO] Epoch: 73 | Step: 1606 | LR: (0.000020/0.001000)
[2022-07-16 10:32:49 INFO] 	Train Loss: 2.229 | Train Metrics: 69.22% | Elapsed Time: 0m 21s
[2022-07-16 10:32:52 INFO] 	Dev.  Loss: 6.727 | Dev.  Metrics: 57.14% | Elapsed Time: 0m 2s
[2022-07-16 10:33:13 INFO] Epoch: 74 | Step: 1628 | LR: (0.000020/0.001000)
[2022-07-16 10:33:13 INFO] 	Train Loss: 2.092 | Train Metrics: 69.13% | Elapsed Time: 0m 21s
[2022-07-16 10:33:16 INFO] 	Dev.  Loss: 6.760 | Dev.  Metrics: 57.65% | Elapsed Time: 0m 2s
[2022-07-16 10:33:37 INFO] Epoch: 75 | Step: 1650 | LR: (0.000020/0.001000)
[2022-07-16 10:33:37 INFO] 	Train Loss: 2.120 | Train Metrics: 70.61% | Elapsed Time: 0m 21s
[2022-07-16 10:33:40 INFO] 	Dev.  Loss: 6.841 | Dev.  Metrics: 56.91% | Elapsed Time: 0m 2s
[2022-07-16 10:34:01 INFO] Epoch: 76 | Step: 1672 | LR: (0.000020/0.001000)
[2022-07-16 10:34:01 INFO] 	Train Loss: 2.128 | Train Metrics: 70.90% | Elapsed Time: 0m 20s
[2022-07-16 10:34:04 INFO] 	Dev.  Loss: 6.822 | Dev.  Metrics: 56.40% | Elapsed Time: 0m 2s
[2022-07-16 10:34:25 INFO] Epoch: 77 | Step: 1694 | LR: (0.000020/0.001000)
[2022-07-16 10:34:25 INFO] 	Train Loss: 2.261 | Train Metrics: 69.97% | Elapsed Time: 0m 21s
[2022-07-16 10:34:28 INFO] 	Dev.  Loss: 6.827 | Dev.  Metrics: 56.43% | Elapsed Time: 0m 2s
[2022-07-16 10:34:48 INFO] Epoch: 78 | Step: 1716 | LR: (0.000020/0.001000)
[2022-07-16 10:34:48 INFO] 	Train Loss: 2.051 | Train Metrics: 71.30% | Elapsed Time: 0m 20s
[2022-07-16 10:34:51 INFO] 	Dev.  Loss: 6.900 | Dev.  Metrics: 56.60% | Elapsed Time: 0m 2s
[2022-07-16 10:35:12 INFO] Epoch: 79 | Step: 1738 | LR: (0.000020/0.001000)
[2022-07-16 10:35:12 INFO] 	Train Loss: 2.042 | Train Metrics: 72.14% | Elapsed Time: 0m 21s
[2022-07-16 10:35:15 INFO] 	Dev.  Loss: 6.915 | Dev.  Metrics: 55.04% | Elapsed Time: 0m 2s
[2022-07-16 10:35:36 INFO] Epoch: 80 | Step: 1760 | LR: (0.000020/0.001000)
[2022-07-16 10:35:36 INFO] 	Train Loss: 2.104 | Train Metrics: 72.53% | Elapsed Time: 0m 21s
[2022-07-16 10:35:39 INFO] 	Dev.  Loss: 6.937 | Dev.  Metrics: 55.42% | Elapsed Time: 0m 2s
[2022-07-16 10:36:00 INFO] Epoch: 81 | Step: 1782 | LR: (0.000020/0.001000)
[2022-07-16 10:36:00 INFO] 	Train Loss: 2.054 | Train Metrics: 70.77% | Elapsed Time: 0m 21s
[2022-07-16 10:36:03 INFO] 	Dev.  Loss: 6.890 | Dev.  Metrics: 55.69% | Elapsed Time: 0m 2s
[2022-07-16 10:36:25 INFO] Epoch: 82 | Step: 1804 | LR: (0.000020/0.001000)
[2022-07-16 10:36:25 INFO] 	Train Loss: 2.008 | Train Metrics: 70.87% | Elapsed Time: 0m 21s
[2022-07-16 10:36:28 INFO] 	Dev.  Loss: 6.916 | Dev.  Metrics: 55.63% | Elapsed Time: 0m 2s
[2022-07-16 10:36:49 INFO] Epoch: 83 | Step: 1826 | LR: (0.000020/0.001000)
[2022-07-16 10:36:49 INFO] 	Train Loss: 2.021 | Train Metrics: 71.09% | Elapsed Time: 0m 21s
[2022-07-16 10:36:52 INFO] 	Dev.  Loss: 7.085 | Dev.  Metrics: 54.52% | Elapsed Time: 0m 2s
[2022-07-16 10:37:13 INFO] Epoch: 84 | Step: 1848 | LR: (0.000020/0.001000)
[2022-07-16 10:37:13 INFO] 	Train Loss: 2.032 | Train Metrics: 73.35% | Elapsed Time: 0m 21s
[2022-07-16 10:37:16 INFO] 	Dev.  Loss: 7.043 | Dev.  Metrics: 54.91% | Elapsed Time: 0m 2s
[2022-07-16 10:37:37 INFO] Epoch: 85 | Step: 1870 | LR: (0.000020/0.001000)
[2022-07-16 10:37:37 INFO] 	Train Loss: 2.176 | Train Metrics: 72.74% | Elapsed Time: 0m 21s
[2022-07-16 10:37:40 INFO] 	Dev.  Loss: 6.886 | Dev.  Metrics: 56.19% | Elapsed Time: 0m 2s
[2022-07-16 10:38:01 INFO] Epoch: 86 | Step: 1892 | LR: (0.000020/0.001000)
[2022-07-16 10:38:01 INFO] 	Train Loss: 1.920 | Train Metrics: 72.71% | Elapsed Time: 0m 21s
[2022-07-16 10:38:04 INFO] 	Dev.  Loss: 6.930 | Dev.  Metrics: 55.85% | Elapsed Time: 0m 2s
[2022-07-16 10:38:25 INFO] Epoch: 87 | Step: 1914 | LR: (0.000020/0.001000)
[2022-07-16 10:38:25 INFO] 	Train Loss: 1.911 | Train Metrics: 72.68% | Elapsed Time: 0m 21s
[2022-07-16 10:38:28 INFO] 	Dev.  Loss: 6.982 | Dev.  Metrics: 56.24% | Elapsed Time: 0m 2s
[2022-07-16 10:38:49 INFO] Epoch: 88 | Step: 1936 | LR: (0.000020/0.001000)
[2022-07-16 10:38:49 INFO] 	Train Loss: 2.043 | Train Metrics: 73.03% | Elapsed Time: 0m 21s
[2022-07-16 10:38:52 INFO] 	Dev.  Loss: 7.019 | Dev.  Metrics: 55.97% | Elapsed Time: 0m 2s
[2022-07-16 10:39:14 INFO] Epoch: 89 | Step: 1958 | LR: (0.000020/0.001000)
[2022-07-16 10:39:14 INFO] 	Train Loss: 1.954 | Train Metrics: 73.31% | Elapsed Time: 0m 21s
[2022-07-16 10:39:17 INFO] 	Dev.  Loss: 7.031 | Dev.  Metrics: 56.20% | Elapsed Time: 0m 3s
[2022-07-16 10:39:38 INFO] Epoch: 90 | Step: 1980 | LR: (0.000020/0.001000)
[2022-07-16 10:39:38 INFO] 	Train Loss: 1.947 | Train Metrics: 72.35% | Elapsed Time: 0m 21s
[2022-07-16 10:39:41 INFO] 	Dev.  Loss: 7.067 | Dev.  Metrics: 55.75% | Elapsed Time: 0m 2s
[2022-07-16 10:40:02 INFO] Epoch: 91 | Step: 2002 | LR: (0.000020/0.001000)
[2022-07-16 10:40:02 INFO] 	Train Loss: 2.131 | Train Metrics: 73.43% | Elapsed Time: 0m 20s
[2022-07-16 10:40:04 INFO] 	Dev.  Loss: 7.096 | Dev.  Metrics: 56.24% | Elapsed Time: 0m 2s
[2022-07-16 10:40:26 INFO] Epoch: 92 | Step: 2024 | LR: (0.000020/0.001000)
[2022-07-16 10:40:26 INFO] 	Train Loss: 1.896 | Train Metrics: 73.53% | Elapsed Time: 0m 21s
[2022-07-16 10:40:29 INFO] 	Dev.  Loss: 7.108 | Dev.  Metrics: 55.46% | Elapsed Time: 0m 2s
[2022-07-16 10:40:50 INFO] Epoch: 93 | Step: 2046 | LR: (0.000020/0.001000)
[2022-07-16 10:40:50 INFO] 	Train Loss: 2.044 | Train Metrics: 74.36% | Elapsed Time: 0m 20s
[2022-07-16 10:40:53 INFO] 	Dev.  Loss: 7.164 | Dev.  Metrics: 55.38% | Elapsed Time: 0m 2s
[2022-07-16 10:41:14 INFO] Epoch: 94 | Step: 2068 | LR: (0.000020/0.001000)
[2022-07-16 10:41:14 INFO] 	Train Loss: 1.923 | Train Metrics: 73.56% | Elapsed Time: 0m 21s
[2022-07-16 10:41:17 INFO] 	Dev.  Loss: 7.083 | Dev.  Metrics: 56.47% | Elapsed Time: 0m 2s
[2022-07-16 10:41:38 INFO] Epoch: 95 | Step: 2090 | LR: (0.000020/0.001000)
[2022-07-16 10:41:38 INFO] 	Train Loss: 1.827 | Train Metrics: 73.45% | Elapsed Time: 0m 21s
[2022-07-16 10:41:41 INFO] 	Dev.  Loss: 7.104 | Dev.  Metrics: 56.17% | Elapsed Time: 0m 2s
[2022-07-16 10:42:02 INFO] Epoch: 96 | Step: 2112 | LR: (0.000020/0.001000)
[2022-07-16 10:42:02 INFO] 	Train Loss: 1.849 | Train Metrics: 73.83% | Elapsed Time: 0m 21s
[2022-07-16 10:42:05 INFO] 	Dev.  Loss: 7.202 | Dev.  Metrics: 55.13% | Elapsed Time: 0m 2s
[2022-07-16 10:42:27 INFO] Epoch: 97 | Step: 2134 | LR: (0.000020/0.001000)
[2022-07-16 10:42:27 INFO] 	Train Loss: 1.930 | Train Metrics: 72.34% | Elapsed Time: 0m 21s
[2022-07-16 10:42:30 INFO] 	Dev.  Loss: 7.176 | Dev.  Metrics: 56.25% | Elapsed Time: 0m 2s
[2022-07-16 10:42:51 INFO] Epoch: 98 | Step: 2156 | LR: (0.000020/0.001000)
[2022-07-16 10:42:51 INFO] 	Train Loss: 2.010 | Train Metrics: 74.25% | Elapsed Time: 0m 20s
[2022-07-16 10:42:53 INFO] 	Dev.  Loss: 7.156 | Dev.  Metrics: 56.37% | Elapsed Time: 0m 2s
[2022-07-16 10:43:15 INFO] Epoch: 99 | Step: 2178 | LR: (0.000020/0.001000)
[2022-07-16 10:43:15 INFO] 	Train Loss: 1.966 | Train Metrics: 72.51% | Elapsed Time: 0m 21s
[2022-07-16 10:43:18 INFO] 	Dev.  Loss: 7.150 | Dev.  Metrics: 56.40% | Elapsed Time: 0m 2s
[2022-07-16 10:43:39 INFO] Epoch: 100 | Step: 2200 | LR: (0.000020/0.001000)
[2022-07-16 10:43:39 INFO] 	Train Loss: 1.963 | Train Metrics: 73.62% | Elapsed Time: 0m 21s
[2022-07-16 10:43:42 INFO] 	Dev.  Loss: 7.176 | Dev.  Metrics: 56.83% | Elapsed Time: 0m 2s
[2022-07-16 10:43:42 INFO] -------------------------------------------- Evaluating --------------------------------------------
[2022-07-16 10:43:42 INFO] Evaluating on dev-set
[2022-07-16 10:43:44 INFO] ER | Micro Precision: 60.857%
[2022-07-16 10:43:44 INFO] ER | Micro Recall: 54.756%
[2022-07-16 10:43:44 INFO] ER | Micro F1-score: 57.645%
[2022-07-16 10:43:44 INFO] ER | Macro Precision: 41.484%
[2022-07-16 10:43:44 INFO] ER | Macro Recall: 53.931%
[2022-07-16 10:43:44 INFO] ER | Macro F1-score: 42.616%
[2022-07-16 10:43:44 INFO] Evaluating on test-set
[2022-07-16 10:43:46 INFO] ER | Micro Precision: 59.062%
[2022-07-16 10:43:46 INFO] ER | Micro Recall: 45.215%
[2022-07-16 10:43:46 INFO] ER | Micro F1-score: 51.220%
[2022-07-16 10:43:46 INFO] ER | Macro Precision: 55.158%
[2022-07-16 10:43:46 INFO] ER | Macro Recall: 37.900%
[2022-07-16 10:43:46 INFO] ER | Macro F1-score: 44.274%
[2022-07-16 10:43:46 INFO] scripts/entity_recognition.py --dataset WeiboNER --ck_decoder boundary_selection
[2022-07-16 10:43:46 INFO] {'affine_arch': 'FFN',
 'agg_mode': 'max_pooling',
 'batch_size': 64,
 'bert_arch': 'None',
 'bert_drop_rate': 0.2,
 'char_arch': 'None',
 'ck_decoder': 'boundary_selection',
 'ck_size_emb_dim': 25,
 'corrupt_rate': 0.0,
 'dataset': 'WeiboNER',
 'doc_level': False,
 'drop_rate': 0.5,
 'emb_dim': 100,
 'emb_freeze': False,
 'enc_arch': 'LSTM',
 'finetune_lr': 2e-05,
 'fl_gamma': 0.0,
 'grad_clip': 5.0,
 'hard_neg_sampling_rate': 1.0,
 'hard_neg_sampling_size': 5,
 'hid_dim': 200,
 'language': 'Chinese',
 'log_terminal': True,
 'lr': 0.001,
 'max_span_size': 10,
 'neg_sampling_rate': 1.0,
 'num_epochs': 100,
 'num_grad_acc_steps': 1,
 'num_layers': 1,
 'num_neg_chunks': 100,
 'optimizer': 'AdamW',
 'pdb': False,
 'pipeline': False,
 'profile': False,
 'save_preds': False,
 'sb_adj_factor': 1.0,
 'sb_epsilon': 0.0,
 'sb_size': 1,
 'scheduler': 'None',
 'scheme': 'BIOES',
 'seed': 515,
 'sl_epsilon': 0.0,
 'train_with_dev': False,
 'use_amp': False,
 'use_biaffine': True,
 'use_bigram': False,
 'use_crf': True,
 'use_elmo': False,
 'use_flair': False,
 'use_interm1': False,
 'use_interm2': False,
 'use_locked_drop': False,
 'use_softlexicon': False,
 'use_softword': False}
[2022-07-16 10:43:46 INFO] ============================================== Ending ==============================================

A question about the second item of biaffine formula.

@syuoni - Hi, thanks for your works about the special regularization technique for span-based NER in advance. I'm confused why the biaffine formula is xWy+xyU+b not xWy+b or [x;1]W[y;1]. What is the purpose of the second item? I think it may make a interaction between tokens in pairs. Do you have some insights about it. Any reply will be appreciated.

您的代码中关于预期校准误差的部分

你好，请问论文Bounndary Smoothing中关于如何得出Expect Calibration Error（ECE）的结果在代码的哪个部分可以参考呢？

Recommend Projects

React

A declarative, efficient, and flexible JavaScript library for building user interfaces.
Vue.js

🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
Typescript

TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
TensorFlow

An Open Source Machine Learning Framework for Everyone
Django

The Web framework for perfectionists with deadlines.
Laravel

A PHP framework for web artisans
D3

Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

javascript

JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
web

Some thing interesting about web. New door for the world.
server

A server is a program made to process requests and deliver data to clients.
Machine learning

Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Visualization

Some thing interesting about visualization, use data art
Game

Some thing interesting about game, make everyone happy.

Recommend Org

Facebook

We are working to build community through open source technology. NB: members must have two-factor auth.
Microsoft

Open source projects and samples from Microsoft.
Google

Google ❤️ Open Source for everyone.
Alibaba

Alibaba Open Source for everyone
D3

Data-Driven Documents codes.
Tencent

China tencent open source team.