Comments (1)
Data fetchers now simplified. You can read any CoNLL column formatted file by passing a dict that specifies what column is what field.
For instance:
from flair.data_fetcher import NLPTaskDataFetcher
sentences = NLPTaskDataFetcher.read_column_data('/path/to/conll03/data', column_name_map={0: 'text', 3: 'ner'})
for sentence in sentences:
print(sentence.to_tagged_string())
Will read a CoNLL-03 formatted file and map the first column (index 0) to the lexical value of each word (i.e. the token text) and column index 3 as the NER tag.
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Related Issues (20)
- [Bug]: Model double sizes after training. Ho to make FP16 for prediction? HOT 7
- [Bug]: Cannot use NER models offline HOT 1
- [Question]: "Redewiedergabe" taggers for flair versions > 0.10 HOT 1
- [Bug]: Receiving Named Entity from Token with `get_label()` does not work as expected HOT 2
- [Question]: Why not include cell type detection in Hunflair? HOT 3
- [Question]: Regarding the issue of reading label spans during corpus construction.
- [Feature]: Latin NLP Model HOT 2
- [Question]: CSVClassificationCorpus and tagger HOT 1
- [Bug]: unable to load upos-multi with SequenceTagger - AttributeError HOT 3
- Assertion error while reading training data
- [Bug]: Sentence Splitters do not set previous and next sentence HOT 1
- [Question]: Low and different results when reload the final_model.pt HOT 1
- [Question]: Semantic Role Labelling Usage Instruction HOT 1
- [Bug]: Whitespace offsets not properly utilized in TransformerEmbeddings
- A missing implementation of a method causing training to be stopped HOT 3
- [Bug]: splitter.split() `ValueError: substring not found` for specific character combination HOT 5
- [Question]: Regarding few shot multi label text classification HOT 1
- [Bug]: Sentence.get_token() incorrectly returning None HOT 2
- [Question]: How to add NER-Entities generated from another model into the dataset for fine-tuning? HOT 1
- [Question]: How does .embed(Sentence) work under the hood? HOT 3
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