Merge pull request #190 from brxx122/master

[new] Update Readme.md and repair bugs with the modification of fastnlp 0.4.5

reproduction/Summarization/Baseline/data/dataloader.py

@@ -56,7 +56,7 @@ class SummarizationLoader(JsonLoader):
 
         return ds
 
-    def process(self, paths, vocab_size, vocab_path, sent_max_len, doc_max_timesteps, domain=False, tag=False, load_vocab=True):
+    def process(self, paths, vocab_size, vocab_path, sent_max_len, doc_max_timesteps, domain=False, tag=False, load_vocab_file=True):
         """
         :param paths: dict  path for each dataset
         :param vocab_size: int  max_size for vocab
@@ -65,7 +65,7 @@ class SummarizationLoader(JsonLoader):
         :param doc_max_timesteps: int   max sentence number of the document
         :param domain: bool  build vocab for publication, use 'X' for unknown
         :param tag: bool  build vocab for tag, use 'X' for unknown
-        :param load_vocab: bool  build vocab (False) or load vocab (True)
+        :param load_vocab_file: bool  build vocab (False) or load vocab (True)
         :return: DataBundle
             datasets: dict  keys correspond to the paths dict
             vocabs: dict  key: vocab(if "train" in paths), domain(if domain=True), tag(if tag=True)
@@ -146,7 +146,7 @@ class SummarizationLoader(JsonLoader):
                 train_ds = datasets[key]
 
         vocab_dict = {}
-        if load_vocab == False:
+        if load_vocab_file == False:
             logger.info("[INFO] Build new vocab from training dataset!")
             if train_ds == None:
                 raise ValueError("Lack train file to build vocabulary!")

reproduction/Summarization/Baseline/tools/data.py

@@ -36,8 +36,8 @@ import pickle
 
 from nltk.tokenize import sent_tokenize
 
-import utils
-from logger import *
+import tools.utils
+from tools.logger import *
 
 # <s> and </s> are used in the data files to segment the abstracts into sentences. They don't receive vocab ids.
 SENTENCE_START = '<s>'
@@ -313,7 +313,8 @@ class Example(object):
         for sent in article_sents:
             article_words = sent.split()
             self.enc_sent_len.append(len(article_words)) # store the length after truncation but before padding
-            self.enc_sent_input.append([vocab.word2id(w) for w in article_words]) # list of word ids; OOVs are represented by the id for UNK token
+            # self.enc_sent_input.append([vocab.word2id(w) for w in article_words]) # list of word ids; OOVs are represented by the id for UNK token
+            self.enc_sent_input.append([vocab.word2id(w.lower()) for w in article_words]) # list of word ids; OOVs are represented by the id for UNK token
         self._pad_encoder_input(vocab.word2id('[PAD]'))
 
         # Store the original strings

reproduction/Summarization/Baseline/train.py

@@ -29,7 +29,7 @@ import torch.nn
 
 os.environ['FASTNLP_BASE_URL'] = 'http://10.141.222.118:8888/file/download/'
 os.environ['FASTNLP_CACHE_DIR'] = '/remote-home/hyan01/fastnlp_caches'
-sys.path.append('/remote-home/dqwang/FastNLP/fastNLP/')
+sys.path.append('/remote-home/dqwang/FastNLP/fastNLP_brxx/')
 
 
 from fastNLP.core.const import Const

reproduction/Summarization/README.md

@@ -39,6 +39,54 @@ FastNLP中实现的模型包括：
 
 
 
+### Evaluation
+
+#### FastRougeMetric
+
+FastRougeMetric使用python实现的ROUGE非官方库来实现在训练过程中快速计算rouge近似值。
+ 源代码可见 [https://github.com/pltrdy/rouge](https://github.com/pltrdy/rouge)
+
+在fastNLP中，该方法已经被包装成Metric.py中的FastRougeMetric类以供trainer直接使用。
+需要事先使用pip安装该rouge库。
+
+    pip install rouge
+
+
+**注意：由于实现细节的差异，该结果和官方ROUGE结果存在1-2个点的差异，仅可作为训练过程优化趋势的粗略估计。**
+
+    
+
+#### PyRougeMetric
+
+PyRougeMetric 使用论文 [*ROUGE: A Package for Automatic Evaluation of Summaries*](https://www.aclweb.org/anthology/W04-1013) 提供的官方ROUGE 1.5.5评测库。
+
+由于原本的ROUGE使用perl解释器，[pyrouge](https://github.com/bheinzerling/pyrouge)对其进行了python包装，而PyRougeMetric将其进一步包装为trainer可以直接使用的Metric类。
+
+为了使用ROUGE 1.5.5，需要使用sudo权限安装一系列依赖库。
+
+1. ROUGE 本身在Ubuntu下的安装可以参考[博客](https://blog.csdn.net/Hay54/article/details/78744912)
+2. 配置wordnet可参考：
+```shell
+$ cd ~/rouge/RELEASE-1.5.5/data/WordNet-2.0-Exceptions/
+$ ./buildExeptionDB.pl . exc WordNet-2.0.exc.db
+$ cd ../
+$ ln -s WordNet-2.0-Exceptions/WordNet-2.0.exc.db WordNet-2.0.exc.db
+```
+3. 安装pyrouge
+```shell
+$ git clone https://github.com/bheinzerling/pyrouge
+$ cd pyrouge
+$ python setup.py install
+```
+4. 测试ROUGE安装是否正确
+```shell
+$ pyrouge_set_rouge_path /absolute/path/to/ROUGE-1.5.5/directory
+$ python -m pyrouge.test
+```
+
+
+
+
 ### Dataset_loader
 
 - SummarizationLoader: 用于读取处理好的jsonl格式数据集，返回以下field
@@ -56,6 +104,21 @@ FastNLP中实现的模型包括：
 
 
 
+### Train Cmdline
+
+#### Baseline
+
+LSTM + Sequence Labeling
+
+    python train.py --cuda --gpu <gpuid> --sentence_encoder deeplstm --sentence_decoder seqlab --save_root <savedir> --log_root <logdir> --lr_descent --grad_clip --max_grad_norm 10
+
+Transformer + Sequence Labeling
+
+    python train.py --cuda --gpu <gpuid> --sentence_encoder transformer --sentence_decoder seqlab --save_root <savedir> --log_root <logdir> --lr_descent --grad_clip --max_grad_norm 10
+
+
+
+#### BertSum