A Unified Generative Framework for Various NER Subtasks.

pip install git+https://github.com/fastnlp/[email protected]
pip install git+https://github.com/fastnlp/fitlog

    - BARTNER/
        - train.py
        ...
    - data/
        - conll2003
            - train.txt
            - text.txt
            - dev.txt
        - en-ontonotes
            - ...
        - Share_2013
        - Share_2014
        - CADEC
        - en_ace04
        - en_ace05
        - genia

LONDON B-LOC
1996-08-30 O

West B-MISC
Indian I-MISC
all-rounder O
Phil B-PER

{"ners": [[[16, 16, "DNA"], [4, 8, "DNA"], [24, 26, "DNA"], [19, 20, "DNA"]], [[31, 31, "DNA"], [2, 2, "DNA"], [4, 4, "DNA"], [30, 31, "DNA"]], [[23, 24, "RNA"], [14, 15, "cell_type"], [1, 2, "RNA"]], [[2, 2, "DNA"]], [], [[0, 0, "DNA"], [9, 9, "cell_type"]]], "sentences": [["There", "is", "a", "single", "methionine", "codon-initiated", "open", "reading", "frame", "of", "1,458", "nt", "in", "frame", "with", "a", "homeobox", "and", "a", "CAX", "repeat", ",", "and", "the", "open", "reading", "frame", "is", "predicted", "to", "encode", "a", "protein", "of", "51,659", "daltons."], ["When", "the", "homeodomain", "from", "HB24", "was", "compared", "to", "known", "mammalian", "and", "Drosophila", "homeodomains", "it", "was", "found", "to", "be", "only", "moderately", "conserved,", "but", "when", "it", "was", "compared", "to", "a", "highly", "diverged", "Drosophila", "homeodomain", ",", "H2.0,", "it", "was", "found", "to", "be", "80%", "identical."], ["The", "HB24", "mRNA", "was", "absent", "or", "present", "at", "low", "levels", "in", "normal", "B", "and", "T", "lymphocytes", ";", "however,", "with", "the", "appropriate", "activation", "signal", "HB24", "mRNA", "was", "induced", "within", "several", "hours", "even", "in", "the", "presence", "of", "cycloheximide", "."], ["Characterization", "of", "HB24", "expression", "in", "lymphoid", "and", "select", "developing", "tissues", "was", "performed", "by", "in", "situ", "hybridization", "."], ["Positive", "hybridization", "was", "found", "in", "thymus", ",", "tonsil", ",", "bone", "marrow", ",", "developing", "vessels", ",", "and", "in", "fetal", "brain", "."], ["HB24", "is", "likely", "to", "have", "an", "important", "role", "in", "lymphocytes", "as", "well", "as", "in", "certain", "developing", "tissues", "."]]}
{"ners": [[[16, 16, "DNA"], [4, 8, "DNA"], [24, 26, "DNA"], [19, 20, "DNA"]], [[31, 31, "DNA"], [2, 2, "DNA"], [4, 4, "DNA"], [30, 31, "DNA"]], [[23, 24, "RNA"], [14, 15, "cell_type"], [1, 2, "RNA"]], [[2, 2, "DNA"]], [], [[0, 0, "DNA"], [9, 9, "cell_type"]]], "sentences": [["There", "is", "a", "single", "methionine", "codon-initiated", "open", "reading", "frame", "of", "1,458", "nt", "in", "frame", "with", "a", "homeobox", "and", "a", "CAX", "repeat", ",", "and", "the", "open", "reading", "frame", "is", "predicted", "to", "encode", "a", "protein", "of", "51,659", "daltons."], ["When", "the", "homeodomain", "from", "HB24", "was", "compared", "to", "known", "mammalian", "and", "Drosophila", "homeodomains", "it", "was", "found", "to", "be", "only", "moderately", "conserved,", "but", "when", "it", "was", "compared", "to", "a", "highly", "diverged", "Drosophila", "homeodomain", ",", "H2.0,", "it", "was", "found", "to", "be", "80%", "identical."], ["The", "HB24", "mRNA", "was", "absent", "or", "present", "at", "low", "levels", "in", "normal", "B", "and", "T", "lymphocytes", ";", "however,", "with", "the", "appropriate", "activation", "signal", "HB24", "mRNA", "was", "induced", "within", "several", "hours", "even", "in", "the", "presence", "of", "cycloheximide", "."], ["Characterization", "of", "HB24", "expression", "in", "lymphoid", "and", "select", "developing", "tissues", "was", "performed", "by", "in", "situ", "hybridization", "."], ["Positive", "hybridization", "was", "found", "in", "thymus", ",", "tonsil", ",", "bone", "marrow", ",", "developing", "vessels", ",", "and", "in", "fetal", "brain", "."], ["HB24", "is", "likely", "to", "have", "an", "important", "role", "in", "lymphocytes", "as", "well", "as", "in", "certain", "developing", "tissues", "."]]}
...

Abdominal cramps , flatulence , gas , bloating .
0,1 ADR|3,3 ADR|7,7 ADR|5,5 ADR

Cramps would start within 15 minutes of taking pill , even during meals .
0,0 ADR

...

python train.py

Save cache to caches/data_facebook/bart-large_conll2003_word.pt.                                                                                                        
max_len_a:0.6, max_len:10
In total 3 datasets:
        test has 3453 instances.
        train has 14041 instances.
        dev has 3250 instances.

The number of tokens in tokenizer  50265
50269 50274
input fields after batch(if batch size is 2):
        tgt_tokens: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2, 8]) 
        src_tokens: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2, 11]) 
        first: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2, 11]) 
        src_seq_len: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2]) 
        tgt_seq_len: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2]) 
target fields after batch(if batch size is 2):
        entities: (1)type:numpy.ndarray (2)dtype:object, (3)shape:(2,) 
        tgt_tokens: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2, 8]) 
        target_span: (1)type:numpy.ndarray (2)dtype:object, (3)shape:(2,) 
        tgt_seq_len: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2]) 

training epochs started 2021-06-02-11-49-26-964889
Epoch 1/30:   0%|                                                         | 15/32430 [00:06<3:12:37,  2.80it/s, loss:6.96158

- BartNER
  - data
     - pipe.py # load and process data
  - model
     - bart.py # the model file
  - train.py  # the training file

- raw_words  # List[str]
    # ['AL-AIN', ',', 'United', 'Arab', 'Emirates', '1996-12-06']
- entities  # List[List[str]]
    # [['AL-AIN'], ['United', 'Arab', 'Emirates']]
- entity_tags  # List[str], the same length as entities
    # ['loc', 'loc']
- entity_spans # List[List[int]], the inner list must have an even number of ints, means the start(inclusive，开区间) and end(exclusive，开区间) of an entity segment
    # [[0, 1], [2, 5]] or for discontinous NER [[0, 1, 5, 7], [2, 3, 5, 7],...]