# Load annotated data in .spacy format to Prodigy for further correction

**URL:** <https://support.prodi.gy/t/load-annotated-data-in-spacy-format-to-prodigy-for-further-correction/6789>\
**Category:** Uncategorized\
**Created:** [September 14, 2023, 10:15am UTC](https://support.prodi.gy/t/load-annotated-data-in-spacy-format-to-prodigy-for-further-correction/6789 "2023-09-14T10:15:51Z")\
**Posts on this page:** 1\
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**Author:** ![magdaaniol](https://sea2.discourse-cdn.com/flex020/user_avatar/support.prodi.gy/magdaaniol/32/2787_2.png) [@magdaaniol](https://support.prodi.gy/u/magdaaniol)\
**Post date:** [September 14, 2023, 2:31pm UTC](https://support.prodi.gy/t/load-annotated-data-in-spacy-format-to-prodigy-for-further-correction/6789/2 "2023-09-14T14:31:59Z")

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Hi @Andrey,

If the examples you need to correct are in `.jsonl` format it's as easy as feed them into the [`ner.manual`](https://prodi.gy/docs/recipes/#ner-manual) recipe specifying the labels you want to have available.  
So if the input file is called `to_be_corrected.jsonl` and assuming `blank:en` as the base model then the call would be:

```python
python -m prodigy ner.manual ner_data_corrected blank:en dataset:ner_data --label MY_LABEL1, MY_LABEL2

```

That will load your examples with existing annotations and allow you to manually correct them and the result will be saved in the `ner_data_corrected` dataset.

If your data is in `.spacy` format, you'd have write a small script to convert it back to the `.jsonl` format and save on disk or save directly to the database.

To save on disk:

```python
import spacy
from spacy.tokens import DocBin
import srsly

path_data = "train.spacy"

nlp = spacy.blank("en")

doc_bin = DocBin().from_disk(path_data)
examples = []
for doc in doc_bin.get_docs(nlp.vocab):
    spans = [{"start": ent.start_char, "end": ent.end_char, "label": ent.label_} for ent in doc.ents]
    examples.append({"text": doc.text, "spans": spans})

srsly.write_jsonl("train.jsonl", examples)

```

To save directly in the DB:

```python
import spacy
from spacy.tokens import DocBin
from prodigy.components.db import connect
from prodigy import set_hashes

path_data = "train.spacy"

nlp = spacy.blank("en")

doc_bin = DocBin().from_disk(path_data)
examples = []
for doc in doc_bin.get_docs(nlp.vocab):
    spans = [{"start": ent.start_char, "end": ent.end_char, "label": ent.label_} for ent in doc.ents]
    examples.append({"text": doc.text, "spans": spans})

db = connect() # connect to the database
db.add_dataset("ner_sample") # add dataset ner_sample
examples = (set_hashes(eg) for eg in examples) # add hashes; creates generator
db.add_examples(list(examples), ["ner_sample"]) # add examples to ner_sample;

```

The snippets come from [this post](https://support.prodi.gy/t/convert-docbins-or-spacy-files-to-jsonl-format/6210/2) on the topic 🙂

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