bplank / bilstm-aux Goto Github PK

When running bilty.py and loading trained model, the following error occurs:

python3 src/bilty.py --dynet-mem 5000 --model "models/test1.model" --dev data/da-ud-dev.conllu --pred_layer 1 --dynet-gpus 1 --output predictions/pred1 --save models/test1
[dynet] random seed: 2913040757
[dynet] allocating memory: 5000MB
[dynet] memory allocation done.
loading model from file models/test1.model
Traceback (most recent call last):
  File "src/bilty.py", line 767, in <module>
    main()
  File "src/bilty.py", line 113, in main
    tagger = load(args)
  File "src/bilty.py", line 188, in load
    myparams = pickle.load(open(model_path+".params.pickle", "rb"))
TypeError: unsupported operand type(s) for +: 'Namespace' and 'str'

It looks like line 113 in bilty.py tagger = load(args) should be tagger = load(args.model) - is this correct?

to fix: need to reload tagger from model_path (if patience is used)

Hi,

I am applying a self-trained model to Twitter messages which might contain unicode emojis. The lstm tagger seems to have problems with those emojis.

for instance:

lol	X
"	.
😂😂💯	X

I can help myself with substituting those cases with a text constant before I call the tagger but I wonder if this problem is known?

I am using python3 on a linux machine.

Best,

code contains some old stuff from bilty, fails when using --save. Reported by @hectormartinez

File "src/simplebilty.py", line 78, in main save(tagger, args) File "src/simplebilty.py", line 135, in save "tasks_ids": nntagger.tasks_ids, AttributeError: 'SimpleBiltyTagger' object has no attribute 'tasks_ids'

Hi Barbara,

I tried playing with your bi-LSTM tagger. [..] However, after loading the source files successfully following
your example, it throws an error:
Traceback (most recent call last):
File "src/bilty.py", line 567, in
main()
File "src/bilty.py", line 86, in main
tagger.fit(args.train, args.iters, args.trainer, dev=args.dev)
File "src/bilty.py", line 222, in fit
self.predictors, self.char_rnn, self.wembeds, self.cembeds =
self.build_computation_graph(num_words, num_chars)
File "src/bilty.py", line 261, in build_computation_graph
wembeds = self.model.add_lookup_parameters("lookup_wembeds", (num_words,
self.in_dim))
TypeError: add_lookup_parameters() takes exactly one argument (2 given)

Dynet's library has changed. Although the documentation moved, the source code of the tagger hasn't. Working on it.

The link in the readme for downloading the polyglot embeddings does not work any more.

Hello, and thank you for making this tagger available!

I tried running the tagger with the --raw and --output options, using an input file with one sentence per line and with space-separated tokens. But it seems that after prediction, once the output_preds function is called, the pred_tags for each sequence remains empty, and the output file stores only newline characters.

My current workaround is simply to reformat the input file to have one line per token and add my own dummy tags before parsing. But as far as I can see, the tagger successfully parses raw inputs anyway, and correctly stores the words and tags per sequence. This leads me to believe that the difference lies in the predict function behavior.

i use python3 embeds/bert.prep.py ,then it generate a folder named bert. Then i run the command "python3 embeds/bert.py".Unfortunately, it output a sentence"please provide embeddings , conl file and port". But the folder bert contain five files.( bert_config ,bert_model.ckpt.index, bert_model.ckpt.data, bert_model.ckpt.meta, vocab)Which one i should input?

‘the option of running a CRF has been added’---- how to run the crf version by the common?