Coversion issue,about xuezhemax/neuronlp2

Comments (17)

XuezheMax commented on August 17, 2024 1

Hi Manni, I guess it is an inconsistent issue between PyTorch on Python2 and Python3. I will try to fixed if ASAP. Thanks.

…

On Fri, Nov 10, 2017 at 2:06 PM, Manni Singh ***@***.***> wrote: Hi, I am getting following error, Any suggestions? Thanks ------------------------------ TypeError Traceback (most recent call last) in () 11 word, char, _, _, labels, masks, lengths = conll03_data.get_batch_variable(data_train, batch_size) 12 optim.zero_grad() ---> 13 loss = network.loss(word, char, labels, mask=masks) 14 loss.backward() 15 optim.step() ~/NeuroNLP2/neuronlp2/models/sequence_labeling.py in loss(self, input_word, input_char, target, mask, length, hx) 291 def loss(self, input_word, input_char, target, mask=None, length=None, hx=None): 292 # output from rnn [batch, length, tag_space] --> 293 output, _, mask, length = self._get_rnn_output(input_word, input_char, mask=mask, length=length, hx=hx) 294 295 if length is not None: ~/NeuroNLP2/neuronlp2/models/sequence_labeling.py in _get_rnn_output(self, input_word, input_char, mask, length, hx) 252 253 # [batch, length, char_length, char_dim] --> 254 char = self.char_embedd(input_char) 255 char_size = char.size() 256 # first transform to [batch *length, char_length, char_dim] /anaconda/lib/python3.6/site-packages/torch/nn/modules/module.py in *call*(self, *input, **kwargs) 222 for hook in self._forward_pre_hooks.values(): 223 hook(self, input) --> 224 result = self.forward(*input, **kwargs) 225 for hook in self._forward_hooks.values(): 226 hook_result = hook(self, input, result) ~/NeuroNLP2/neuronlp2/nn/modules/sparse.py in forward(self, input) 68 if input.dim() > 2: 69 num_inputs = np.prod(input_size[:-1]) ---> 70 input = input.view(num_inputs, input_size[-1]) 71 72 output_size = input_size + (self.embedding_dim, ) /anaconda/lib/python3.6/site-packages/torch/autograd/variable.py in view(self, *sizes) 508 509 def view(self, *sizes): --> 510 return View.apply(self, sizes) 511 512 def view_as(self, tensor): /anaconda/lib/python3.6/site-packages/torch/autograd/_functions/tensor.py in forward(ctx, i, sizes) 94 ctx.new_sizes = sizes 95 ctx.old_size = i.size() ---> 96 result = i.view(*sizes) 97 ctx.mark_shared_storage((i, result)) 98 return result TypeError: view received an invalid combination of arguments - got (numpy.int64, int), but expected one of: - (int ... size) didn't match because some of the arguments have invalid types: (numpy.int64, int) - (torch.Size size) — You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub <#1>, or mute the thread <https://github.com/notifications/unsubscribe-auth/ADUtlhLrPt7kJ7EgXeDODmDvSnCJzBzmks5s1J7FgaJpZM4QZ-pG> .

-- ------------------ Best regards, Ma，Xuezhe Language Technologies Institute, School of Computer Science, Carnegie Mellon University Tel: +1 206-512-5977

from neuronlp2.

XuezheMax commented on August 17, 2024 1

Hi Manni,

Please try again to see if it works now.
Thanks.

from neuronlp2.

ManniSingh commented on August 17, 2024

Thanks Max, i will wait for it.
Though i have a Tensorflow version but for some reason doesn't give F1: 91+ on same params.
Lasagne version is also so slow.

from neuronlp2.

ManniSingh commented on August 17, 2024

Thanks it Works,
However, not giving me anywhere near f1: 90+ on conll2003. I tried both of your codes.

This one gives:
dev acc: 97.91%, precision: 90.59%, recall: 86.55%, F1: 88.52%
best dev acc: 98.01%, precision: 89.95%, recall: 87.31%, F1: 88.61% (epoch: 14)
best test acc: 96.40%, precision: 82.97%, recall: 80.67%, F1: 81.80% (epoch: 14)

Without any early stop:
Epoch 100 (LSTM(std), learning rate=0.0025, decay rate=0.0500 (1)):
loss: 0.0423, time: 133.20s
dev acc: 97.98%, precision: 89.70%, recall: 87.45%, F1: 88.56%
best dev acc: 98.03%, precision: 90.04%, recall: 87.89%, F1: 88.95% (epoch: 69)
best test acc: 96.27%, precision: 82.60%, recall: 80.74%, F1: 81.66% (epoch: 69)

Lasagne code gives:
best dev acc: 97.99%, precision: 89.74%, recall: 87.73%, F1: 88.72%
best test acc: 96.21%, precision: 81.13%, recall: 79.78%, F1: 80.45%

Under settings : (mentioned in your paper),
following are logs from the code:

loading embedding: glove from /data/manni/ner/glove/glove.6B.100d.txt.gz
2017-11-18 13:02:00,893 - NERCRF - INFO - Creating Alphabets
2017-11-18 13:02:00,911 - Create Alphabets - INFO - Word Alphabet Size (Singleton): 11985 (0)
2017-11-18 13:02:00,913 - Create Alphabets - INFO - Character Alphabet Size: 86
2017-11-18 13:02:00,915 - Create Alphabets - INFO - POS Alphabet Size: 47
2017-11-18 13:02:00,916 - Create Alphabets - INFO - Chunk Alphabet Size: 19
2017-11-18 13:02:00,918 - Create Alphabets - INFO - NER Alphabet Size: 9
2017-11-18 13:02:00,919 - NERCRF - INFO - Word Alphabet Size: 11985
2017-11-18 13:02:00,921 - NERCRF - INFO - Character Alphabet Size: 86
2017-11-18 13:02:00,923 - NERCRF - INFO - POS Alphabet Size: 47
2017-11-18 13:02:00,925 - NERCRF - INFO - Chunk Alphabet Size: 19
2017-11-18 13:02:00,926 - NERCRF - INFO - NER Alphabet Size: 9
2017-11-18 13:02:00,928 - NERCRF - INFO - Reading Data
Reading data from /data/manni/ner/conll2003/eng.train
reading data: 10000
Total number of data: 14041
Reading data from /data/manni/ner/conll2003/eng.testa
Total number of data: 3250
Reading data from /data/manni/ner/conll2003/eng.testb
Total number of data: 3453
oov: 11984
2017-11-18 13:02:12,675 - NERCRF - INFO - constructing network...
2017-11-18 13:02:13,287 - NERCRF - INFO - Network: LSTM, num_layer=1, hidden=200, filter=30, tag_space=128, crf=bigram
2017-11-18 13:02:13,289 - NERCRF - INFO - training: l2: 0.000000, (#training data: 14041, batch: 10, dropout: 0.50)

settings:
tag_space = 128 # WHAT IS THIS?
dropout = 'std'
logger = get_logger("NERCRF")
mode = 'LSTM'
train_path = "/data/manni/ner/conll2003/eng.train"
dev_path = "/data/manni/ner/conll2003/eng.testa"
test_path = "/data/manni/ner/conll2003/eng.testb"
num_epochs = 100
batch_size = 10
hidden_size = 200
num_filters = 30
learning_rate = 0.015
momentum = 0.9
decay_rate = 0.05
gamma = 0.0
schedule = 1
p = 0.5
bigram = True
embedding = 'glove'

from neuronlp2.

XuezheMax commented on August 17, 2024

Hi Manni,
Here is my logs for the first few epochs.
loading embedding: glove from data/glove/glove.6B/glove.6B.100d.gz
2017-11-18 17:47:27,021 - NERCRF - INFO - Creating Alphabets
2017-11-18 17:47:27,041 - Create Alphabets - INFO - Word Alphabet Size (Singleton): 23598 (8122)
2017-11-18 17:47:27,041 - Create Alphabets - INFO - Character Alphabet Size: 86
2017-11-18 17:47:27,041 - Create Alphabets - INFO - POS Alphabet Size: 47
2017-11-18 17:47:27,041 - Create Alphabets - INFO - Chunk Alphabet Size: 19
2017-11-18 17:47:27,041 - Create Alphabets - INFO - NER Alphabet Size: 18
2017-11-18 17:47:27,041 - NERCRF - INFO - Word Alphabet Size: 23598
2017-11-18 17:47:27,041 - NERCRF - INFO - Character Alphabet Size: 86
2017-11-18 17:47:27,041 - NERCRF - INFO - POS Alphabet Size: 47
2017-11-18 17:47:27,041 - NERCRF - INFO - Chunk Alphabet Size: 19
2017-11-18 17:47:27,041 - NERCRF - INFO - NER Alphabet Size: 18
2017-11-18 17:47:27,042 - NERCRF - INFO - Reading Data
Reading data from data/conll2003/english/eng.train.bioes.conll
reading data: 10000
Total number of data: 14987
Reading data from data/conll2003/english/eng.dev.bioes.conll
Total number of data: 3466
Reading data from data/conll2003/english/eng.test.bioes.conll
Total number of data: 3684
oov: 339
2017-11-18 17:47:31,883 - NERCRF - INFO - constructing network...
2017-11-18 17:47:32,315 - NERCRF - INFO - Network: LSTM, num_layer=1, hidden=256, filter=30, tag_space=128, crf=bigram
2017-11-18 17:47:32,315 - NERCRF - INFO - training: l2: 0.000000, (#training data: 14987, batch: 16, dropout: 0.50, unk replace: 0.00)
Epoch 1 (LSTM(std), learning rate=0.0100, decay rate=0.0500 (schedule=1)):
train: 937 loss: 2.3267, time: 50.82s
dev acc: 97.04%, precision: 88.88%, recall: 85.93%, F1: 87.38%
best dev acc: 97.04%, precision: 88.88%, recall: 85.93%, F1: 87.38% (epoch: 1)
best test acc: 96.16%, precision: 84.89%, recall: 82.95%, F1: 83.91% (epoch: 1)
Epoch 2 (LSTM(std), learning rate=0.0095, decay rate=0.0500 (schedule=1)):
train: 937 loss: 0.8332, time: 50.86s
dev acc: 97.66%, precision: 90.54%, recall: 89.26%, F1: 89.90%
best dev acc: 97.66%, precision: 90.54%, recall: 89.26%, F1: 89.90% (epoch: 2)
best test acc: 96.83%, precision: 86.99%, recall: 86.51%, F1: 86.75% (epoch: 2)
Epoch 3 (LSTM(std), learning rate=0.0091, decay rate=0.0500 (schedule=1)):
train: 937 loss: 0.6846, time: 33.49s
dev acc: 98.10%, precision: 92.61%, recall: 90.86%, F1: 91.73%
best dev acc: 98.10%, precision: 92.61%, recall: 90.86%, F1: 91.73% (epoch: 3)
best test acc: 97.35%, precision: 88.87%, recall: 87.98%, F1: 88.42% (epoch: 3)
Epoch 4 (LSTM(std), learning rate=0.0087, decay rate=0.0500 (schedule=1)):
train: 937 loss: 0.5764, time: 39.42s
dev acc: 98.29%, precision: 92.49%, recall: 92.07%, F1: 92.28%
best dev acc: 98.29%, precision: 92.49%, recall: 92.07%, F1: 92.28% (epoch: 4)
best test acc: 97.46%, precision: 88.24%, recall: 88.60%, F1: 88.42% (epoch: 4)

It seems that there are some issues in your data. Please make sure that you follows the data format describe here #2
and use the BIOES tagging schema.
When you are trying to re-run the code, please first remove the vocabulary dir in data/alphabets/ner_crf/ so that the new vocabulary can be rebuilt.
Thanks.

from neuronlp2.

XuezheMax commented on August 17, 2024

Moreover, PyTorch has some implicit parameter initialization which makes the training of the first few epochs unstable. When you see a pretty large loss at the beginning of the training (like 50+), just kill the program and re-run it :)

from neuronlp2.

ManniSingh commented on August 17, 2024

Hi Max,

Cleaned everything
Error starts < 15
I converted data to BIOES
Did not remove "-docstart-" sentences, i noticed from your log that says, "Total number of data: 14987".

But still (1% improvement i got):

Epoch 100 (LSTM(std), learning rate=0.0025, decay rate=0.0500 (1)):
1499/1499 [===========================>] - ETA: 8s - train loss: 0.0395, time: 126.78s
dev acc: 97.68%, precision: 89.58%, recall: 87.28%, F1: 88.42%
best dev acc: 97.87%, precision: 90.29%, recall: 88.44%, F1: 89.36% (epoch: 21)
best test acc: 96.17%, precision: 83.23%, recall: 81.80%, F1: 82.51% (epoch: 21)

from neuronlp2.

XuezheMax commented on August 17, 2024

Hi Manni, It seams you did not update to the latest version. Please pull first then run it again. If it still cannot obtain comparable performance. would you please paste your complete log here?

…

On Sun, Nov 19, 2017 at 8:13 AM, Manni Singh ***@***.***> wrote: Hi Max, - Cleaned everything - Error starts < 15 - I converted data to BIOES - Did not remove "-docstart-" sentences, i noticed from your log that says, "Total number of data: 14987". But still (1% improvement i got): Epoch 100 (LSTM(std), learning rate=0.0025, decay rate=0.0500 (1)): 1499/1499 [===========================>] - ETA: 8s - train loss: 0.0395, time: 126.78s dev acc: 97.68%, precision: 89.58%, recall: 87.28%, F1: 88.42% best dev acc: 97.87%, precision: 90.29%, recall: 88.44%, F1: 89.36% (epoch: 21) best test acc: 96.17%, precision: 83.23%, recall: 81.80%, F1: 82.51% (epoch: 21) — You are receiving this because you commented. Reply to this email directly, view it on GitHub <#1 (comment)>, or mute the thread <https://github.com/notifications/unsubscribe-auth/ADUtlictchyB2rxyeWFlslSDcaqT5ki9ks5s4Cl6gaJpZM4QZ-pG> .

-- ------------------ Best regards, Ma，Xuezhe Language Technologies Institute, School of Computer Science, Carnegie Mellon University Tel: +1 206-512-5977

from neuronlp2.

ManniSingh commented on August 17, 2024

HI Max,
I did clone, that is why it is working.

Following is the log:

loading embedding: glove from /data/manni/ner/glove/glove.6B.100d.txt.gz
2017-11-19 09:08:22,562 - NERCRF - INFO - Creating Alphabets
2017-11-19 09:08:22,565 - Create Alphabets - INFO - Creating Alphabets: /data/manni/alphabets/ner_crf/
2017-11-19 09:08:23,173 - Create Alphabets - INFO - Total Vocabulary Size: 23625
2017-11-19 09:08:23,175 - Create Alphabets - INFO - TOtal Singleton Size: 11641
2017-11-19 09:08:23,180 - Create Alphabets - INFO - Total Vocabulary Size (w.o rare words): 11984
2017-11-19 09:08:23,351 - Create Alphabets - INFO - Word Alphabet Size (Singleton): 11985 (0)
2017-11-19 09:08:23,352 - Create Alphabets - INFO - Character Alphabet Size: 86
2017-11-19 09:08:23,353 - Create Alphabets - INFO - POS Alphabet Size: 47
2017-11-19 09:08:23,354 - Create Alphabets - INFO - Chunk Alphabet Size: 19
2017-11-19 09:08:23,355 - Create Alphabets - INFO - NER Alphabet Size: 18
2017-11-19 09:08:23,357 - NERCRF - INFO - Word Alphabet Size: 11985
2017-11-19 09:08:23,358 - NERCRF - INFO - Character Alphabet Size: 86
2017-11-19 09:08:23,359 - NERCRF - INFO - POS Alphabet Size: 47
2017-11-19 09:08:23,360 - NERCRF - INFO - Chunk Alphabet Size: 19
2017-11-19 09:08:23,361 - NERCRF - INFO - NER Alphabet Size: 18
2017-11-19 09:08:23,362 - NERCRF - INFO - Reading Data
Reading data from /data/manni/ner/conll2003/eng.bioes.train
reading data: 10000
Total number of data: 14987
Reading data from /data/manni/ner/conll2003/eng.bioes.testa
Total number of data: 3466
Reading data from /data/manni/ner/conll2003/eng.bioes.testb
Total number of data: 3684
oov: 11984
2017-11-19 09:08:36,198 - NERCRF - INFO - constructing network...
2017-11-19 09:08:36,776 - NERCRF - INFO - Network: LSTM, num_layer=1, hidden=200, filter=30, tag_space=128, crf=bigram
2017-11-19 09:08:36,777 - NERCRF - INFO - training: l2: 0.000000, (#training data: 14987, batch: 10, dropout: 0.50)

After Epoch 100:

Epoch 100 (LSTM(std), learning rate=0.0025, decay rate=0.0500 (1)):
dev acc: 97.68%, precision: 89.58%, recall: 87.28%, F1: 88.42%
best dev acc: 97.87%, precision: 90.29%, recall: 88.44%, F1: 89.36% (epoch: 21)
best test acc: 96.17%, precision: 83.23%, recall: 81.80%, F1: 82.51% (epoch: 21)

from neuronlp2.

XuezheMax commented on August 17, 2024

Hi Manni, After seeing your log, I guess the issue might be your word embedding. There are 11948 words that are not included in your embedding dict, while the number in my case is only 339. Please make sure to use the correct GloVe 6B embedding. Moreover, according to your log, I believe that you are not working on the latest version. That might be another possible issue. Thanks.

…

On Mon, Nov 20, 2017 at 12:01 PM, Manni Singh ***@***.***> wrote: HI Max, I did clone, that is why it is working. *Following is the log:* loading embedding: glove from /data/manni/ner/glove/glove.6B.100d.txt.gz 2017-11-19 09:08:22,562 - NERCRF - INFO - Creating Alphabets 2017-11-19 09:08:22,565 - Create Alphabets - INFO - Creating Alphabets: /data/manni/alphabets/ner_crf/ 2017-11-19 09:08:23,173 - Create Alphabets - INFO - Total Vocabulary Size: 23625 2017-11-19 09:08:23,175 - Create Alphabets - INFO - TOtal Singleton Size: 11641 2017-11-19 09:08:23,180 - Create Alphabets - INFO - Total Vocabulary Size (w.o rare words): 11984 2017-11-19 09:08:23,351 - Create Alphabets - INFO - Word Alphabet Size (Singleton): 11985 (0) 2017-11-19 09:08:23,352 - Create Alphabets - INFO - Character Alphabet Size: 86 2017-11-19 09:08:23,353 - Create Alphabets - INFO - POS Alphabet Size: 47 2017-11-19 09:08:23,354 - Create Alphabets - INFO - Chunk Alphabet Size: 19 2017-11-19 09:08:23,355 - Create Alphabets - INFO - NER Alphabet Size: 18 2017-11-19 09:08:23,357 - NERCRF - INFO - Word Alphabet Size: 11985 2017-11-19 09:08:23,358 - NERCRF - INFO - Character Alphabet Size: 86 2017-11-19 09:08:23,359 - NERCRF - INFO - POS Alphabet Size: 47 2017-11-19 09:08:23,360 - NERCRF - INFO - Chunk Alphabet Size: 19 2017-11-19 09:08:23,361 - NERCRF - INFO - NER Alphabet Size: 18 2017-11-19 09:08:23,362 - NERCRF - INFO - Reading Data Reading data from /data/manni/ner/conll2003/eng.bioes.train reading data: 10000 Total number of data: 14987 Reading data from /data/manni/ner/conll2003/eng.bioes.testa Total number of data: 3466 Reading data from /data/manni/ner/conll2003/eng.bioes.testb Total number of data: 3684 oov: 11984 2017-11-19 09:08:36,198 - NERCRF - INFO - constructing network... 2017-11-19 09:08:36,776 - NERCRF - INFO - Network: LSTM, num_layer=1, hidden=200, filter=30, tag_space=128, crf=bigram 2017-11-19 09:08:36,777 - NERCRF - INFO - training: l2: 0.000000, (#training data: 14987, batch: 10, dropout: 0.50) *After Epoch 100:* Epoch 100 (LSTM(std), learning rate=0.0025, decay rate=0.0500 (1)): dev acc: 97.68%, precision: 89.58%, recall: 87.28%, F1: 88.42% best dev acc: 97.87%, precision: 90.29%, recall: 88.44%, F1: 89.36% (epoch: 21) best test acc: 96.17%, precision: 83.23%, recall: 81.80%, F1: 82.51% (epoch: 21) — You are receiving this because you commented. Reply to this email directly, view it on GitHub <#1 (comment)>, or mute the thread <https://github.com/notifications/unsubscribe-auth/ADUtliuKLSMTMXE65SRLj2tiHzlZrVgjks5s4bB7gaJpZM4QZ-pG> .

-- ------------------ Best regards, Ma，Xuezhe Language Technologies Institute, School of Computer Science, Carnegie Mellon University Tel: +1 206-512-5977

from neuronlp2.

ManniSingh commented on August 17, 2024

I lowered words in "conll03_data.py"
Also did same in "reader.py"
Now the oov is 476 and getting F1:91+
But why OOV is so less (I have "normalize_digits=False")? Are you doing Masking somewhere?

The orignal composition of glove to conll2003 i calculated is:

Glove Vocab length:400000
Vocab length:21009
Vocab length:9002
Vocab length:8548
Total Vocab: 26869
Total OOV: 3922

Now the result is:

Epoch 41 (LSTM(std), learning rate=0.0050, decay rate=0.0500 (1)):
1499/1499 [===========================>] - ETA: 8s - train: 61459 loss: 0.1576, time: 126.63s
dev acc: 98.81%, precision: 94.80%, recall: 94.58%, F1: 94.69%
best dev acc: 98.81%, precision: 94.80%, recall: 94.58%, F1: 94.69% (epoch: 41)
best test acc: 97.93%, precision: 91.26%, recall: 90.92%, F1: 91.09% (epoch: 41)

from neuronlp2.

XuezheMax commented on August 17, 2024

The trick I used for handle OOV is I first lookup the word in GloVe. If it is not there, we lookup the lower case of the word in GloVe, again. It can significantly reduce the number of OOV.

…

On Tue, Nov 21, 2017 at 9:09 AM, Manni Singh ***@***.***> wrote: - I lowered words in "conll03_data.py" - Also did same in "reader.py" - Now the oov is 476 and getting F1:91+ But why OOV is so less (I have "normalize_digits=False")? Are you doing Masking somewhere? *The orignal composition of glove to conll2003 i calculated is:* Glove Vocab length:400000 Vocab length:21009 Vocab length:9002 Vocab length:8548 Total Vocab: 26869 Total OOV: 3922 Epoch 41 (LSTM(std), learning rate=0.0050, decay rate=0.0500 (1)): 1499/1499 [===========================>] - ETA: 8s - train: 61459 loss: 0.1576, time: 126.63s dev acc: 98.81%, precision: 94.80%, recall: 94.58%, F1: 94.69% best dev acc: 98.81%, precision: 94.80%, recall: 94.58%, F1: 94.69% (epoch: 41) best test acc: 97.93%, precision: 91.26%, recall: 90.92%, F1: 91.09% (epoch: 41) — You are receiving this because you commented. Reply to this email directly, view it on GitHub <#1 (comment)>, or mute the thread <https://github.com/notifications/unsubscribe-auth/ADUtlvjyfyqjDRmjuEufCCkS9YkgYOVYks5s4tmigaJpZM4QZ-pG> .

-- ------------------ Best regards, Ma，Xuezhe Language Technologies Institute, School of Computer Science, Carnegie Mellon University Tel: +1 206-512-5977

from neuronlp2.

ManniSingh commented on August 17, 2024

I noticed that in the code. But, to my knowledge, the 6B GloVe is uncased. Also, there are many non-word elements (Digits, Punkts etc.) in Conll2003 English dataset are you masking those?

from neuronlp2.

XuezheMax commented on August 17, 2024

No, I did not mask those. I guess another possible reason is that when I create the vocabulary, i throw away the singletons that do not appear in GloVe.

…

On Tue, Nov 21, 2017 at 4:48 PM, Manni Singh ***@***.***> wrote: I noticed that in the code. But, to my knowledge, the 6B GloVe is uncased. Also, there are many non-word elements (Digits, Punkts etc.) in Conll2003 English dataset are you masking those? — You are receiving this because you commented. Reply to this email directly, view it on GitHub <#1 (comment)>, or mute the thread <https://github.com/notifications/unsubscribe-auth/ADUtlih2nYtHKEhMmjQ33d_aegzDXNJKks5s40UugaJpZM4QZ-pG> .

-- ------------------ Best regards, Ma，Xuezhe Language Technologies Institute, School of Computer Science, Carnegie Mellon University Tel: +1 206-512-5977

from neuronlp2.

ManniSingh commented on August 17, 2024

So does that mean, you considering them(singletons) "unk" ?
BTW, That seems a good idea for generalisation!

from neuronlp2.

XuezheMax commented on August 17, 2024

Yes, if a singleton is excluded in the vocabulary, it will be regarded as 'UNK'.

…

On Tue, Nov 21, 2017 at 5:00 PM, Manni Singh ***@***.***> wrote: So does tha mean, you considering them(singletons) "unk" ? BTW, That seems a good idea for generalisation! — You are receiving this because you commented. Reply to this email directly, view it on GitHub <#1 (comment)>, or mute the thread <https://github.com/notifications/unsubscribe-auth/ADUtloaBT6sJSW_7ihZWCcYTY6vyKfCGks5s40gGgaJpZM4QZ-pG> .

-- ------------------ Best regards, Ma，Xuezhe Language Technologies Institute, School of Computer Science, Carnegie Mellon University Tel: +1 206-512-5977

from neuronlp2.

ManniSingh commented on August 17, 2024

Great! Thanks i will try that.

from neuronlp2.

Coversion issue about neuronlp2 HOT 17 CLOSED

Comments (17)

Related Issues (20)

Recommend Projects

React

Vue.js

Typescript

TensorFlow

Django

Laravel

D3

Recommend Topics

javascript

web

server

Machine learning

Visualization

Game

Recommend Org

Facebook

Microsoft

Google

Alibaba

D3

Tencent