mirror of
https://gitee.com/fastnlp/fastNLP.git
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Ready for V0.3.1
* 升级parser API和模型 * update docs: add new pages for tutorials * upgrade CWS api download source * add a new method for dataset field access * add introduction for bert * add more unit tests for api/processor * remove unused test data. Add new test data.
This commit is contained in:
FengZiYjun
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0c5630bd16
@ -1,7 +1,8 @@
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fastNLP上手教程
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fastNLP 10分钟上手教程
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===============
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教程原文见 https://github.com/fastnlp/fastNLP/blob/master/tutorials/fastnlp_10min_tutorial.ipynb
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fastNLP提供方便的数据预处理，训练和测试模型的功能
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DataSet & Instance
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@ -2,6 +2,8 @@
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FastNLP 1分钟上手教程
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=====================
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教程原文见 https://github.com/fastnlp/fastNLP/blob/master/tutorials/fastnlp_1min_tutorial.ipynb
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step 1
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------
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5
docs/source/tutorials/fastnlp_advanced_tutorial.rst
Normal file
5
docs/source/tutorials/fastnlp_advanced_tutorial.rst
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@ -0,0 +1,5 @@
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fastNLP 进阶教程
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===============
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教程原文见 https://github.com/fastnlp/fastNLP/blob/master/tutorials/fastnlp_advanced_tutorial/advance_tutorial.ipynb
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5
docs/source/tutorials/fastnlp_developer_guide.rst
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5
docs/source/tutorials/fastnlp_developer_guide.rst
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@ -0,0 +1,5 @@
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fastNLP 开发者指南
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===============
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原文见 https://github.com/fastnlp/fastNLP/blob/master/tutorials/tutorial_for_developer.md
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@ -5,6 +5,7 @@ Installation
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.. contents::
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:local:
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Make sure your environment satisfies https://github.com/fastnlp/fastNLP/blob/master/requirements.txt .
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Run the following commands to install fastNLP package:
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@ -6,4 +6,6 @@ Quickstart
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../tutorials/fastnlp_1_minute_tutorial
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../tutorials/fastnlp_10tmin_tutorial
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../tutorials/fastnlp_advanced_tutorial
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../tutorials/fastnlp_developer_guide
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@ -9,7 +9,7 @@ from fastNLP.core.dataset import DataSet
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from fastNLP.api.utils import load_url
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from fastNLP.api.processor import ModelProcessor
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from fastNLP.io.dataset_loader import ConllCWSReader, ConllxDataLoader, add_seg_tag
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from fastNLP.io.dataset_loader import ConllCWSReader, ConllxDataLoader
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from fastNLP.core.instance import Instance
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from fastNLP.api.pipeline import Pipeline
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from fastNLP.core.metrics import SpanFPreRecMetric
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@ -17,9 +17,9 @@ from fastNLP.api.processor import IndexerProcessor
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# TODO add pretrain urls
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model_urls = {
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"cws": "http://123.206.98.91:8888/download/cws_crf_1_11-457fc899.pkl",
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"cws": "http://123.206.98.91:8888/download/cws_lstm_ctb9_1_20-09908656.pkl",
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"pos": "http://123.206.98.91:8888/download/pos_tag_model_20190119-43f8b435.pkl",
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"parser": "http://123.206.98.91:8888/download/biaffine_parser-3a2f052c.pkl"
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"parser": "http://123.206.98.91:8888/download/parser_20190204-c72ca5c0.pkl"
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}
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@ -90,38 +90,28 @@ class POS(API):
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# 3. 使用pipeline
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self.pipeline(dataset)
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# def decode_tags(ins):
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# pred_tags = ins["tag"]
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# chars = ins["words"]
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# words = []
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# start_idx = 0
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# for idx, tag in enumerate(pred_tags):
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# if tag[0] == "S":
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# words.append(chars[start_idx:idx + 1] + "/" + tag[2:])
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# start_idx = idx + 1
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# elif tag[0] == "E":
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# words.append("".join(chars[start_idx:idx + 1]) + "/" + tag[2:])
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# start_idx = idx + 1
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# return words
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#
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# dataset.apply(decode_tags, new_field_name="tag_output")
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def merge_tag(words_list, tags_list):
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rtn = []
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for words, tags in zip(words_list, tags_list):
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rtn.append([w + "/" + t for w, t in zip(words, tags)])
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return rtn
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output = dataset.field_arrays["tag"].content
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if isinstance(content, str):
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return output[0]
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elif isinstance(content, list):
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return output
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return merge_tag(content, output)
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def test(self, file_path):
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test_data = ConllxDataLoader().load(file_path)
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with open("model_pp_0117.pkl", "rb") as f:
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save_dict = torch.load(f)
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save_dict = self._dict
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tag_vocab = save_dict["tag_vocab"]
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pipeline = save_dict["pipeline"]
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index_tag = IndexerProcessor(vocab=tag_vocab, field_name="tag", new_added_field_name="truth", is_input=False)
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pipeline.pipeline = [index_tag] + pipeline.pipeline
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test_data.rename_field("pos_tags", "tag")
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pipeline(test_data)
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test_data.set_target("truth")
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prediction = test_data.field_arrays["predict"].content
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@ -235,7 +225,7 @@ class CWS(API):
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rec = eval_res['BMESF1PreRecMetric']['rec']
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# print("f1:{:.2f}, pre:{:.2f}, rec:{:.2f}".format(f1, pre, rec))
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return f1, pre, rec
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return {"F1": f1, "precision": pre, "recall": rec}
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class Parser(API):
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@ -260,6 +250,7 @@ class Parser(API):
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dataset.add_field('wp', pos_out)
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dataset.apply(lambda x: ['<BOS>'] + [w.split('/')[0] for w in x['wp']], new_field_name='words')
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dataset.apply(lambda x: ['<BOS>'] + [w.split('/')[1] for w in x['wp']], new_field_name='pos')
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dataset.rename_field("words", "raw_words")
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# 3. 使用pipeline
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self.pipeline(dataset)
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@ -269,31 +260,74 @@ class Parser(API):
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# output like: [['2/top', '0/root', '4/nn', '2/dep']]
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return dataset.field_arrays['output'].content
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def test(self, filepath):
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data = ConllxDataLoader().load(filepath)
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ds = DataSet()
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for ins1, ins2 in zip(add_seg_tag(data), data):
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ds.append(Instance(words=ins1[0], tag=ins1[1],
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gold_words=ins2[0], gold_pos=ins2[1],
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gold_heads=ins2[2], gold_head_tags=ins2[3]))
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def load_test_file(self, path):
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def get_one(sample):
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sample = list(map(list, zip(*sample)))
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if len(sample) == 0:
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return None
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for w in sample[7]:
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if w == '_':
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print('Error Sample {}'.format(sample))
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return None
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# return word_seq, pos_seq, head_seq, head_tag_seq
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return sample[1], sample[3], list(map(int, sample[6])), sample[7]
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datalist = []
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with open(path, 'r', encoding='utf-8') as f:
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sample = []
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for line in f:
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if line.startswith('\n'):
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datalist.append(sample)
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sample = []
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elif line.startswith('#'):
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continue
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else:
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sample.append(line.split('\t'))
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if len(sample) > 0:
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datalist.append(sample)
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data = [get_one(sample) for sample in datalist]
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data_list = list(filter(lambda x: x is not None, data))
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return data_list
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def test(self, filepath):
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data = self.load_test_file(filepath)
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def convert(data):
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BOS = '<BOS>'
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dataset = DataSet()
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for sample in data:
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word_seq = [BOS] + sample[0]
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pos_seq = [BOS] + sample[1]
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heads = [0] + sample[2]
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head_tags = [BOS] + sample[3]
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dataset.append(Instance(raw_words=word_seq,
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pos=pos_seq,
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gold_heads=heads,
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arc_true=heads,
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tags=head_tags))
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return dataset
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ds = convert(data)
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pp = self.pipeline
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for p in pp:
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if p.field_name == 'word_list':
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p.field_name = 'gold_words'
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elif p.field_name == 'pos_list':
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p.field_name = 'gold_pos'
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# ds.rename_field("words", "raw_words")
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# ds.rename_field("tag", "pos")
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pp(ds)
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head_cor, label_cor, total = 0, 0, 0
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for ins in ds:
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head_gold = ins['gold_heads']
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head_pred = ins['heads']
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head_pred = ins['arc_pred']
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length = len(head_gold)
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total += length
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for i in range(length):
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head_cor += 1 if head_pred[i] == head_gold[i] else 0
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uas = head_cor / total
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print('uas:{:.2f}'.format(uas))
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# print('uas:{:.2f}'.format(uas))
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for p in pp:
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if p.field_name == 'gold_words':
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@ -301,7 +335,7 @@ class Parser(API):
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elif p.field_name == 'gold_pos':
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p.field_name = 'pos_list'
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return uas
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return {"USA": round(uas, 5)}
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class Analyzer:
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@ -15,19 +15,40 @@ def chinese_word_segmentation():
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print(cws.predict(text))
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def chinese_word_segmentation_test():
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cws = CWS(device='cpu')
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print(cws.test("../../test/data_for_tests/zh_sample.conllx"))
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def pos_tagging():
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# 输入已分词序列
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text = ['编者 按： 7月 12日 ， 英国 航空 航天 系统 公司 公布 了 该 公司 研制 的 第一款 高科技 隐形 无人机 雷电之神 。']
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text = [text[0].split()]
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print(text)
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pos = POS(device='cpu')
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print(pos.predict(text))
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def pos_tagging_test():
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pos = POS(device='cpu')
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print(pos.test("../../test/data_for_tests/zh_sample.conllx"))
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def syntactic_parsing():
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text = ['编者 按： 7月 12日 ， 英国 航空 航天 系统 公司 公布 了 该 公司 研制 的 第一款 高科技 隐形 无人机 雷电之神 。']
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text = [text[0].split()]
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parser = Parser(device='cpu')
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print(parser.predict(text))
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def syntactic_parsing_test():
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parser = Parser(device='cpu')
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print(parser.test("../../test/data_for_tests/zh_sample.conllx"))
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if __name__ == "__main__":
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chinese_word_segmentation()
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chinese_word_segmentation_test()
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pos_tagging()
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pos_tagging_test()
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syntactic_parsing()
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syntactic_parsing_test()
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@ -102,6 +102,7 @@ class PreAppendProcessor(Processor):
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[data] + instance[field_name]
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"""
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def __init__(self, data, field_name, new_added_field_name=None):
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super(PreAppendProcessor, self).__init__(field_name, new_added_field_name)
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self.data = data
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@ -116,6 +117,7 @@ class SliceProcessor(Processor):
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从某个field中只取部分内容。等价于instance[field_name][start:end:step]
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"""
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def __init__(self, start, end, step, field_name, new_added_field_name=None):
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super(SliceProcessor, self).__init__(field_name, new_added_field_name)
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for o in (start, end, step):
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@ -132,6 +134,7 @@ class Num2TagProcessor(Processor):
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将一句话中的数字转换为某个tag。
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"""
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def __init__(self, tag, field_name, new_added_field_name=None):
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"""
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@ -163,6 +166,7 @@ class IndexerProcessor(Processor):
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给定一个vocabulary , 将指定field转换为index形式。指定field应该是一维的list，比如
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['我', '是', xxx]
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"""
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def __init__(self, vocab, field_name, new_added_field_name, delete_old_field=False, is_input=True):
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assert isinstance(vocab, Vocabulary), "Only Vocabulary class is allowed, not {}.".format(type(vocab))
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@ -215,6 +219,7 @@ class SeqLenProcessor(Processor):
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根据某个field新增一个sequence length的field。取该field的第一维
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"""
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def __init__(self, field_name, new_added_field_name='seq_lens', is_input=True):
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super(SeqLenProcessor, self).__init__(field_name, new_added_field_name)
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self.is_input = is_input
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@ -229,6 +234,7 @@ class SeqLenProcessor(Processor):
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from fastNLP.core.utils import _build_args
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class ModelProcessor(Processor):
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def __init__(self, model, seq_len_field_name='seq_lens', batch_size=32):
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"""
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@ -292,6 +298,7 @@ class Index2WordProcessor(Processor):
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将DataSet中某个为index的field根据vocab转换为str
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"""
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def __init__(self, vocab, field_name, new_added_field_name):
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super(Index2WordProcessor, self).__init__(field_name, new_added_field_name)
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self.vocab = vocab
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@ -303,7 +310,6 @@ class Index2WordProcessor(Processor):
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class SetTargetProcessor(Processor):
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# TODO; remove it.
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def __init__(self, *fields, flag=True):
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super(SetTargetProcessor, self).__init__(None, None)
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self.fields = fields
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@ -313,6 +319,7 @@ class SetTargetProcessor(Processor):
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dataset.set_target(*self.fields, flag=self.flag)
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return dataset
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class SetInputProcessor(Processor):
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def __init__(self, *fields, flag=True):
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super(SetInputProcessor, self).__init__(None, None)
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@ -92,6 +92,10 @@ class DataSet(object):
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data_set.add_field(name=field.name, fields=field.content[idx], padder=field.padder,
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is_input=field.is_input, is_target=field.is_target)
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return data_set
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elif isinstance(idx, str):
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if idx not in self:
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raise KeyError("No such field called {} in DataSet.".format(idx))
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return self.field_arrays[idx]
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else:
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raise KeyError("Unrecognized type {} for idx in __getitem__ method".format(type(idx)))
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@ -1,3 +1,7 @@
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"""
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bert.py is modified from huggingface/pytorch-pretrained-BERT, which is licensed under the Apache License 2.0.
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"""
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import copy
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import json
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import math
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@ -220,7 +224,23 @@ class BertPooler(nn.Module):
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class BertModel(nn.Module):
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"""BERT model ("Bidirectional Embedding Representations from a Transformer").
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"""Bidirectional Embedding Representations from Transformers.
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If you want to use pre-trained weights, please download from the following sources provided by pytorch-pretrained-BERT.
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sources::
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'bert-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased.tar.gz",
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'bert-large-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased.tar.gz",
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'bert-base-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased.tar.gz",
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'bert-large-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased.tar.gz",
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'bert-base-multilingual-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-uncased.tar.gz",
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'bert-base-multilingual-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-cased.tar.gz",
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'bert-base-chinese': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese.tar.gz",
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Construct a BERT model with pre-trained weights::
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model = BertModel.from_pretrained("path/to/weights/directory")
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"""
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@ -1,5 +1,5 @@
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[train]
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n_epochs = 1
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n_epochs = 20
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batch_size = 32
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use_cuda = true
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use_tqdm=true
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@ -1,9 +1,12 @@
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import random
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import unittest
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from fastNLP import Vocabulary
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import numpy as np
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from fastNLP import Vocabulary, Instance
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from fastNLP.api.processor import FullSpaceToHalfSpaceProcessor, PreAppendProcessor, SliceProcessor, Num2TagProcessor, \
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IndexerProcessor, VocabProcessor, SeqLenProcessor
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IndexerProcessor, VocabProcessor, SeqLenProcessor, ModelProcessor, Index2WordProcessor, SetTargetProcessor, \
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SetInputProcessor, VocabIndexerProcessor
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from fastNLP.core.dataset import DataSet
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@ -53,3 +56,46 @@ class TestProcessor(unittest.TestCase):
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ds = proc(ds)
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for data in ds.field_arrays["len"].content:
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self.assertEqual(data, 30)
|
||||
|
||||
def test_ModelProcessor(self):
|
||||
from fastNLP.models.cnn_text_classification import CNNText
|
||||
model = CNNText(100, 100, 5)
|
||||
ins_list = []
|
||||
for _ in range(64):
|
||||
seq_len = np.random.randint(5, 30)
|
||||
ins_list.append(Instance(word_seq=[np.random.randint(0, 100) for _ in range(seq_len)], seq_lens=seq_len))
|
||||
data_set = DataSet(ins_list)
|
||||
data_set.set_input("word_seq", "seq_lens")
|
||||
proc = ModelProcessor(model)
|
||||
data_set = proc(data_set)
|
||||
self.assertTrue("pred" in data_set)
|
||||
|
||||
def test_Index2WordProcessor(self):
|
||||
vocab = Vocabulary()
|
||||
vocab.add_word_lst(["a", "b", "c", "d", "e"])
|
||||
proc = Index2WordProcessor(vocab, "tag_id", "tag")
|
||||
data_set = DataSet([Instance(tag_id=[np.random.randint(0, 7) for _ in range(32)])])
|
||||
data_set = proc(data_set)
|
||||
self.assertTrue("tag" in data_set)
|
||||
|
||||
def test_SetTargetProcessor(self):
|
||||
proc = SetTargetProcessor("a", "b", "c")
|
||||
data_set = DataSet({"a": [1, 2, 3], "b": [1, 2, 3], "c": [1, 2, 3]})
|
||||
data_set = proc(data_set)
|
||||
self.assertTrue(data_set["a"].is_target)
|
||||
self.assertTrue(data_set["b"].is_target)
|
||||
self.assertTrue(data_set["c"].is_target)
|
||||
|
||||
def test_SetInputProcessor(self):
|
||||
proc = SetInputProcessor("a", "b", "c")
|
||||
data_set = DataSet({"a": [1, 2, 3], "b": [1, 2, 3], "c": [1, 2, 3]})
|
||||
data_set = proc(data_set)
|
||||
self.assertTrue(data_set["a"].is_input)
|
||||
self.assertTrue(data_set["b"].is_input)
|
||||
self.assertTrue(data_set["c"].is_input)
|
||||
|
||||
def test_VocabIndexerProcessor(self):
|
||||
proc = VocabIndexerProcessor("word_seq", "word_ids")
|
||||
data_set = DataSet([Instance(word_seq=["a", "b", "c", "d", "e"])])
|
||||
data_set = proc(data_set)
|
||||
self.assertTrue("word_ids" in data_set)
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
@ -1,2 +0,0 @@
|
||||
迈向充满希望的新世纪——一九九八年新年讲话
|
||||
（附图片1张）
|
||||
100
test/data_for_tests/zh_sample.conllx
Normal file
100
test/data_for_tests/zh_sample.conllx
Normal file
@ -0,0 +1,100 @@
|
||||
1 上海 _ NR NR _ 3 nsubj _ _
|
||||
2 积极 _ AD AD _ 3 advmod _ _
|
||||
3 准备 _ VV VV _ 0 root _ _
|
||||
4 迎接 _ VV VV _ 3 ccomp _ _
|
||||
5 欧元 _ NN NN _ 6 nn _ _
|
||||
6 诞生 _ NN NN _ 4 dobj _ _
|
||||
|
||||
1 新华社 _ NR NR _ 7 dep _ _
|
||||
2 上海 _ NR NR _ 7 dep _ _
|
||||
3 十二月 _ NT NT _ 7 dep _ _
|
||||
4 三十日 _ NT NT _ 7 dep _ _
|
||||
5 电 _ NN NN _ 7 dep _ _
|
||||
6 （ _ PU PU _ 7 punct _ _
|
||||
7 记者 _ NN NN _ 0 root _ _
|
||||
8 潘清 _ NR NR _ 7 dep _ _
|
||||
9 ） _ PU PU _ 7 punct _ _
|
||||
|
||||
1 即将 _ AD AD _ 2 advmod _ _
|
||||
2 诞生 _ VV VV _ 4 rcmod _ _
|
||||
3 的 _ DEC DEC _ 2 cpm _ _
|
||||
4 欧元 _ NN NN _ 6 nsubj _ _
|
||||
5 ， _ PU PU _ 6 punct _ _
|
||||
6 引起 _ VV VV _ 0 root _ _
|
||||
7 了 _ AS AS _ 6 asp _ _
|
||||
8 上海 _ NR NR _ 14 nn _ _
|
||||
9 这 _ DT DT _ 14 det _ _
|
||||
10 个 _ M M _ 9 clf _ _
|
||||
11 中国 _ NR NR _ 13 nn _ _
|
||||
12 金融 _ NN NN _ 13 nn _ _
|
||||
13 中心 _ NN NN _ 14 nn _ _
|
||||
14 城市 _ NN NN _ 16 assmod _ _
|
||||
15 的 _ DEG DEG _ 14 assm _ _
|
||||
16 关注 _ NN NN _ 6 dobj _ _
|
||||
17 。 _ PU PU _ 6 punct _ _
|
||||
|
||||
1 上海 _ NR NR _ 2 nn _ _
|
||||
2 银行界 _ NN NN _ 4 nsubj _ _
|
||||
3 纷纷 _ AD AD _ 4 advmod _ _
|
||||
4 推出 _ VV VV _ 0 root _ _
|
||||
5 了 _ AS AS _ 4 asp _ _
|
||||
6 与 _ P P _ 8 prep _ _
|
||||
7 之 _ PN PN _ 6 pobj _ _
|
||||
8 相关 _ VA VA _ 15 rcmod _ _
|
||||
9 的 _ DEC DEC _ 8 cpm _ _
|
||||
10 外汇 _ NN NN _ 15 nn _ _
|
||||
11 业务 _ NN NN _ 15 nn _ _
|
||||
12 品种 _ NN NN _ 15 conj _ _
|
||||
13 和 _ CC CC _ 15 cc _ _
|
||||
14 服务 _ NN NN _ 15 nn _ _
|
||||
15 举措 _ NN NN _ 4 dobj _ _
|
||||
16 ， _ PU PU _ 4 punct _ _
|
||||
17 积极 _ AD AD _ 18 advmod _ _
|
||||
18 准备 _ VV VV _ 4 dep _ _
|
||||
19 启动 _ VV VV _ 18 ccomp _ _
|
||||
20 欧元 _ NN NN _ 21 nn _ _
|
||||
21 业务 _ NN NN _ 19 dobj _ _
|
||||
22 。 _ PU PU _ 4 punct _ _
|
||||
|
||||
1 一些 _ CD CD _ 8 nummod _ _
|
||||
2 热衷于 _ VV VV _ 8 rcmod _ _
|
||||
3 个人 _ NN NN _ 5 nn _ _
|
||||
4 外汇 _ NN NN _ 5 nn _ _
|
||||
5 交易 _ NN NN _ 2 dobj _ _
|
||||
6 的 _ DEC DEC _ 2 cpm _ _
|
||||
7 上海 _ NR NR _ 8 nn _ _
|
||||
8 市民 _ NN NN _ 13 nsubj _ _
|
||||
9 ， _ PU PU _ 13 punct _ _
|
||||
10 也 _ AD AD _ 13 advmod _ _
|
||||
11 对 _ P P _ 13 prep _ _
|
||||
12 欧元 _ NN NN _ 11 pobj _ _
|
||||
13 表示 _ VV VV _ 0 root _ _
|
||||
14 出 _ VV VV _ 13 rcomp _ _
|
||||
15 极 _ AD AD _ 16 advmod _ _
|
||||
16 大 _ VA VA _ 18 rcmod _ _
|
||||
17 的 _ DEC DEC _ 16 cpm _ _
|
||||
18 兴趣 _ NN NN _ 13 dobj _ _
|
||||
19 。 _ PU PU _ 13 punct _ _
|
||||
|
||||
1 继 _ P P _ 38 prep _ _
|
||||
2 上海 _ NR NR _ 6 nn _ _
|
||||
3 大众 _ NR NR _ 6 nn _ _
|
||||
4 汽车 _ NN NN _ 6 nn _ _
|
||||
5 有限 _ JJ JJ _ 6 amod _ _
|
||||
6 公司 _ NN NN _ 13 nsubj _ _
|
||||
7 十八日 _ NT NT _ 13 tmod _ _
|
||||
8 在 _ P P _ 13 prep _ _
|
||||
9 中国 _ NR NR _ 10 nn _ _
|
||||
10 银行 _ NN NN _ 12 nn _ _
|
||||
11 上海 _ NR NR _ 12 nn _ _
|
||||
12 分行 _ NN NN _ 8 pobj _ _
|
||||
13 开立 _ VV VV _ 19 lccomp _ _
|
||||
14 上海 _ NR NR _ 16 dep _ _
|
||||
15 第一 _ OD OD _ 16 ordmod _ _
|
||||
16 个 _ M M _ 18 clf _ _
|
||||
17 欧元 _ NN NN _ 18 nn _ _
|
||||
18 帐户 _ NN NN _ 13 dobj _ _
|
||||
19 后 _ LC LC _ 1 plmod _ _
|
||||
20 ， _ PU PU _ 38 punct _ _
|
||||
21 工商 _ NN NN _ 28 nn _ _
|
||||
22 银行 _ NN NN _ 28 conj _ _
|
||||
Loading…
Reference in New Issue
Block a user