# Unable to use Prodigy annotations with SpaCy CLI train **URL:** \ **Category:** Uncategorized\ **Tags:** usage, spacy, solved\ **Created:** [October 7, 2019, 8:41pm UTC](https://support.prodi.gy/t/unable-to-use-prodigy-annotations-with-spacy-cli-train/2084 "2019-10-07T20:41:52Z")\ **Posts on this page:** 3\ **Page:** 1
**Author:** ![ojustwin.naik](https://avatars.discourse-cdn.com/v4/letter/o/e495f1/32.png) [@ojustwin.naik](https://support.prodi.gy/u/ojustwin.naik)\ **Post date:** [October 7, 2019, 8:41pm UTC](https://support.prodi.gy/t/unable-to-use-prodigy-annotations-with-spacy-cli-train/2084/1 "2019-10-07T20:41:52Z")
I am able to use prodigy annotations to train NER models using prodigy ner.batch-train and python code. But when I try to use the same annotation dataset (exported using ner.gold-to-spacy) with the cli "train" command I get the following error. What am I doing wrong? python -m spacy train en ner\_system\_model ner\_system\_train\_manual\_annotations.json Preformatted textner\_system\_test\_manual\_annotations.json -b model -p ner Training pipeline: ['ner'] Starting with base model 'model' Counting training words (limit=0) ``` Itn Dep Loss NER Loss UAS NER P NER R NER F Tag % Token % CPU WPS GPU WPS --- ---------- ---------- ------- ------- ------- ------- ------- ------- ------- ------- ✔ Saved model to output directory ner_system_model\model-final Traceback (most recent call last): File "C:\Users\ojustwin.naik\AppData\Local\Continuum\anaconda3\envs\tf-gpu\lib\site-packages\spacy\cli\train.py", line 281, in train scorer = nlp_loaded.evaluate(dev_docs, debug) File "C:\Users\ojustwin.naik\AppData\Local\Continuum\anaconda3\envs\tf-gpu\lib\site-packages\spacy\language.py", line 631, in evaluate docs, golds = zip(*docs_golds) ValueError: not enough values to unpack (expected 2, got 0) During handling of the above exception, another exception occurred: Traceback (most recent call last): File "C:\Users\ojustwin.naik\AppData\Local\Continuum\anaconda3\envs\tf-gpu\lib\runpy.py", line 193, in _run_module_as_main " __main__", mod_spec) File "C:\Users\ojustwin.naik\AppData\Local\Continuum\anaconda3\envs\tf-gpu\lib\runpy.py", line 85, in _run_code exec(code, run_globals) File "C:\Users\ojustwin.naik\AppData\Local\Continuum\anaconda3\envs\tf-gpu\lib\site-packages\spacy\ __main__.py", line 35, in plac.call(commands[command], sys.argv[1:]) File "C:\Users\ojustwin.naik\AppData\Local\Continuum\anaconda3\envs\tf-gpu\lib\site-packages\plac_core.py", line 328, in call cmd, result = parser.consume(arglist) File "C:\Users\ojustwin.naik\AppData\Local\Continuum\anaconda3\envs\tf-gpu\lib\site-packages\plac_core.py", line 207, in consume return cmd, self.func(*(args + varargs + extraopts), **kwargs) File "C:\Users\ojustwin.naik\AppData\Local\Continuum\anaconda3\envs\tf-gpu\lib\site-packages\spacy\cli\train.py", line 368, in train best_model_path = _collate_best_model(meta, output_path, nlp.pipe_names) File "C:\Users\ojustwin.naik\AppData\Local\Continuum\anaconda3\envs\tf-gpu\lib\site-packages\spacy\cli\train.py", line 425, in _collate_best_model bests[component] = _find_best(output_path, component) File "C:\Users\ojustwin.naik\AppData\Local\Continuum\anaconda3\envs\tf-gpu\lib\site-packages\spacy\cli\train.py", line 444, in _find_best accs = srsly.read_json(epoch_model / "accuracy.json") File "C:\Users\ojustwin.naik\AppData\Local\Continuum\anaconda3\envs\tf-gpu\lib\site-packages\srsly\_json_api.py", line 49, in read_json file_path = force_path(location) File "C:\Users\ojustwin.naik\AppData\Local\Continuum\anaconda3\envs\tf-gpu\lib\site-packages\srsly\util.py", line 11, in force_path raise ValueError("Can't read file: {}".format(location)) ValueError: Can't read file: ner_system_model\model0\accuracy.json ``` ---
**Author:** ![honnibal](https://sea2.discourse-cdn.com/flex020/user_avatar/support.prodi.gy/honnibal/32/35_2.png) [@honnibal](https://support.prodi.gy/u/honnibal)\ **Post date:** [October 7, 2019, 9:20pm UTC](https://support.prodi.gy/t/unable-to-use-prodigy-annotations-with-spacy-cli-train/2084/2 "2019-10-07T21:20:31Z")
We've been trying to make training with spaCy's CLI easier, so we've moved the conversion over into spaCy so that non-prodigy users can make use of it. So, the easiest way is to use the `spacy convert` command, which supports Prodigy's jsonl format. This creates the full json format used by spaCy, so you can use the train CLI. The `ner.gold-to-spacy` produces a simpler format that's easier to work with if you're writing your own training loop. We should probably remove or update this now that the `spacy convert` function handles Prodigy's jsonl format. I just tried the following on a dataset I happened to have in my DB: ```python python -m prodigy db-out tmp-per-manual-2 > ~/tmp/ner_per.jsonl python -m spacy convert ~/tmp/ner_per.jsonl . --converter jsonl --lang en ``` The `--lang` argument was necessary because spaCy needs to tokenize the file to produce its json format, and no tokens were already set in the dataset. This produced a file `ner_per.json` in my current directory, the start of which looks like this: ```python [ { "id":0, "paragraphs":[ { "raw":"Thanks fellas", "sentences":[ { "tokens":[ { "id":0, "orth":"Thanks", "ner":"O" }, { "id":1, "orth":"fellas", "ner":"O" } ], "brackets":[ ] } ], "cats":[ ] } ] }, ``` ---
**Author:** ![ojustwin.naik](https://avatars.discourse-cdn.com/v4/letter/o/e495f1/32.png) [@ojustwin.naik](https://support.prodi.gy/u/ojustwin.naik)\ **Post date:** [October 8, 2019, 2:53pm UTC](https://support.prodi.gy/t/unable-to-use-prodigy-annotations-with-spacy-cli-train/2084/3 "2019-10-08T14:53:52Z")
Thanks Matthew. The converter worked, but with a hiccup. When I first ran "train" on the converted annotations I got the following error: ``` KeyError: "[E022] Could not find a transition with the name 'B-' in the NER model." ``` I found that in the annotations there was an instance of multi word entity which was tagged with only "ner":"B-", "ner":"I-", "ner":"L-", essentially without the Entity name after the "B-". So I manually fixed this and then was able to run the CLI "train" command successfully.