Thank you!
@LiyuanLucasLiu

code:
`!pip install lightner

from lightner import decoder_wrapper

model = decoder_wrapper("http://dmserv4.cs.illinois.edu/pner0.th", configs)

model = decoder_wrapper()
model.decode(["Ronaldo", "won", "'t", "score", "more", "than", "30", "goals", "for", "Juve", "."])`

[2023-09-09 22:40:28,224] Loading model from http://dmserv4.cs.illinois.edu/pner0.th (might download from source if not cached).
% Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0
curl: (7) Failed to connect to dmserv4.cs.illinois.edu port 80: Connection refused

FileNotFoundError Traceback (most recent call last)
Cell In [6], line 5
3 from lightner import decoder_wrapper
4 # model = decoder_wrapper("http://dmserv4.cs.illinois.edu/pner0.th", configs)
----> 5 model = decoder_wrapper()
6 model.decode(["Ronaldo", "won", "'t", "score", "more", "than", "30", "goals", "for", "Juve", "."])

File /usr/local/lib/python3.9/dist-packages/lightner/commands/decoder.py:174, in decoder_wrapper(model_file_path, configs)
171 pw = wrapper(configs.get("log_path", None))
173 logger.info("Loading model from {} (might download from source if not cached).".format(model_file_path))
--> 174 model_file = wrapper.restore_checkpoint(model_file_path)
176 model_type = model_file['config'].get("model_type", 'char-lstm-crf')
177 logger.info('Preparing the pre-trained {} model.'.format(model_type))

File /usr/local/lib/python3.9/dist-packages/torch_scope/wrapper.py:161, in basic_wrapper.restore_checkpoint(file_path)
146 @staticmethod
147 def restore_checkpoint(file_path):
148 """
149 Restore checkpoint.
150
(...)
159 A dict contains 'model' and 'optimizer' (if saved).
160 """
--> 161 return torch.load(cached_url(file_path), map_location=lambda storage, loc: storage)

File /usr/local/lib/python3.9/dist-packages/torch/serialization.py:699, in load(f, map_location, pickle_module, **pickle_load_args)
696 if 'encoding' not in pickle_load_args.keys():
697 pickle_load_args['encoding'] = 'utf-8'
--> 699 with _open_file_like(f, 'rb') as opened_file:
700 if _is_zipfile(opened_file):
701 # The zipfile reader is going to advance the current file position.
702 # If we want to actually tail call to torch.jit.load, we need to
703 # reset back to the original position.
704 orig_position = opened_file.tell()

File /usr/local/lib/python3.9/dist-packages/torch/serialization.py:230, in _open_file_like(name_or_buffer, mode)
228 def _open_file_like(name_or_buffer, mode):
229 if _is_path(name_or_buffer):
--> 230 return _open_file(name_or_buffer, mode)
231 else:
232 if 'w' in mode:

File /usr/local/lib/python3.9/dist-packages/torch/serialization.py:211, in _open_file.init(self, name, mode)
210 def init(self, name, mode):
--> 211 super(_open_file, self).init(open(name, mode))

FileNotFoundError: [Errno 2] No such file or directory: '/root/.ts_cache/b91f996d8ec980e293ffd169408e9f99b9728f4024744405bef1f2f04ecaedd5/download.cached'

@LiyuanLucasLiu
Thank you very much!

https://github.com/divelab/dtn
Is the link I want?
@LiyuanLucasLiu
Thank you very much!

Hi, thanks for this great work. It is very helpful to release the pre-trained LM in such a clear code (unlike Elmo)

I am wondering if you have any plan to release the pre-trained R-Elmo model in your paper (#6 and #7 in table 1 of the paper)?

Thanks!

Why use detach()?
Thank you! @LiyuanLucasLiu

Thanks for sharing.

Thank you! @LiyuanLucasLiu

Currently, CoreNLP NER supports from 3 classes CoNLL to the MUC-7 classes, depending on the model loaded in the CoreNLP pipeline:

# tags: LOCATION, ORGANIZATION, PERSON
edu/stanford/nlp/models/ner/english.all.3class.distsim.crf.ser.gz
# tags: DATE, LOCATION, MONEY, ORGANIZATION, PERCENT, PERSON, TIME
edu/stanford/nlp/models/ner/english.muc.7class.distsim.crf.ser.gz
# LOCATION, MISC, ORGANIZATION, PERSON
edu/stanford/nlp/models/ner/english.conll.4class.distsim.crf.ser.gz

They can also be combine all together to obtain the 7 classes output CFR predictor.
It would be possible to support the MUC-7 classes?

Thank you.