I am gettingRuntimeError: Expected object of type Variable[torch.LongTensor] but found type Variable[torch.FloatTensor] for argument #1 'target'. May be we need to change CrossEntropyLoss with something else.

---------------------------------------------------------------------------
AssertionError                            Traceback (most recent call last)
<ipython-input-11-6c7dc2606552> in <module>()
      3 
      4 for i in flatten_list:
----> 5   deepmojify(i, top_n = 5)

1 frames
/content/torchMoji/torchmoji/sentence_tokenizer.py in tokenize_sentences(self, sentences, reset_stats, max_sentences)
    117         # may filter the sentences etc.
    118         if not self.uses_custom_wordgen and not self.ignore_sentences_with_only_custom:
--> 119             assert len(sentences) == next_insert
    120         else:
    121             # adjust based on actual tokens received

AssertionError: 

Hi,

The above error keeps coming when I run text_emojize.py file.

I have given a list of around 4700+ sentences for the model to convert it into 5 emojis.

I made changes to this block of code >> st = SentenceTokenizer(vocabulary, 100)

What am I doing wrong? Is it because I gave too many sentences?

Hi. When we run score_texts_emojis.py, we get an output like the below picture saved in a .CSV file.

I want to convert the ID's of Emojis(Emoji_1, Emoji_2, Emoji_3, Emoji_4, Emoji_5) given into either actual emojis or their UNICODES.

Can someone help me with that?

TIA.

I'm trying to get 2304 fixed length vector embeddings for a set of tweets for my dataset.

I'm using Google Colaboratory(Python 3) and I get this error with this command

!python3 examples/text_emojize.py --text f"This is the shit!"

as well as using this

encoding = model(tokenized)

The attention weights are not initialized properly; right now, they take on random values up to 10^38, which makes training a model from scratch virtually impossible.

In contrast, the original Keras implementation uses a uniform random initialization scheme with values from -0.05 to 0.05. Obviously this doesn't affect pretrained models, but it would still be nice to have this fixed.

A fun experiment of mine. It is basically the same as score_texts_emojis.py, but there are 2 differences:

• I mapped emoji's id from emoji_overview.png and output the visualization with emoji package, use use_aliases=True.
• I receive a single input and use user's input rather than placeholder example text

Demo:

python examples/text_emojize.py --text "I love mom's cooking\!"
# => I love mom's cooking! 😋 😍 💓 💛 ❤

I tried running examples/encode_texts.py, it yielded the error at the end of the issue.

Story: I installed torch from the original torch downloads, and it appears it expects CUDA even though I assume it will just run with CPU (especially given the notificiation about GPU in the README).

Strangely, looking here: http://pytorch.org/

Whenever I select "pip", "3.6" and "none" (gpu), it still contains cu75 in the url which might indicate it needs CUDA?

Trace

pascal@archbook:~/gits/torchMoji$ python examples/encode_texts.py Tokenizing using dictionary from /home/pascal/gits/torchMoji/model/vocabulary.json Loading model from /home/pascal/gits/torchMoji/model/pytorch_model.bin. Loading weights for embed.weight Loading weights for lstm_0.weight_ih_l0 Loading weights for lstm_0.weight_hh_l0 Loading weights for lstm_0.bias_ih_l0 Loading weights for lstm_0.bias_hh_l0 Loading weights for lstm_0.weight_ih_l0_reverse Loading weights for lstm_0.weight_hh_l0_reverse Loading weights for lstm_0.bias_ih_l0_reverse Loading weights for lstm_0.bias_hh_l0_reverse Loading weights for lstm_1.weight_ih_l0 Loading weights for lstm_1.weight_hh_l0 Loading weights for lstm_1.bias_ih_l0 Loading weights for lstm_1.bias_hh_l0 Loading weights for lstm_1.weight_ih_l0_reverse Loading weights for lstm_1.weight_hh_l0_reverse Loading weights for lstm_1.bias_ih_l0_reverse Loading weights for lstm_1.bias_hh_l0_reverse Loading weights for attention_layer.attention_vector Ignoring weights for output_layer.0.weight Ignoring weights for output_layer.0.bias TorchMoji ( (embed): Embedding(50000, 256) (embed_dropout): Dropout2d (p=0) (lstm_0): LSTMHardSigmoid(256, 512, batch_first=True, bidirectional=True) (lstm_1): LSTMHardSigmoid(1024, 512, batch_first=True, bidirectional=True) (attention_layer): Attention(2304, return attention=False) ) Encoding texts.. Traceback (most recent call last): File "examples/encode_texts.py", line 34, in encoding = model(tokenized) File "/home/pascal/.pyenv/versions/3.6.2/lib/python3.6/site-packages/torch/nn/modules/module.py", line 224, in call result = self.forward(*input, **kwargs) File "/home/pascal/gits/torchMoji/torchmoji/model_def.py", line 233, in forward x, att_weights = self.attention_layer(input_seqs, input_lengths) File "/home/pascal/.pyenv/versions/3.6.2/lib/python3.6/site-packages/torch/nn/modules/module.py", line 224, in call result = self.forward(*input, **kwargs) File "/home/pascal/gits/torchMoji/torchmoji/attlayer.py", line 56, in forward mask = Variable((idxes < input_lengths.unsqueeze(1)).float()) File "/home/pascal/.pyenv/versions/3.6.2/lib/python3.6/site-packages/torch/tensor.py", line 351, in lt return self.lt(other) TypeError: lt received an invalid combination of arguments - got (torch.LongTensor), but expected one of:

• (int value) didn't match because some of the arguments have invalid types: (torch.LongTensor)
• (torch.cuda.LongTensor other) didn't match because some of the arguments have invalid types: (torch.LongTensor)

Bumps numpy from 1.13.1 to 1.21.0.

Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.

I am trying to replicate this paper as per my project proposal. Could you please assist with this?

While running

python examples/text_emojize.py --text "I love mom's cooking!"

I got C:\...\torchMoji/model/pytorch_model.bin' No such file or directory: issue.

On newest PyTorch PackedSequence takes in *args so the current constructor calls don't work anymore (although it shouldn't really be constructed from user land). This patch fixes that.

Also see the relevant https://github.com/pytorch/pytorch/pull/9864 that adds support for this on master. However, that patch isn't making into the new release we (the PyTorch team) are preparing so this change will be needed for the upcoming released PyTorch.

I get a few errors when I run score_text_emojis.py I'm using pytorch-cpu=0.4.0=py35_cpu_1 and master of torchMoji (b3dd91958ba2a752e0fdee2fd1839ae2bbea549a)

It seems the errors are caused by changes to PyTorch.

e.g.

$ python score_texts_emojis.py

Tokenizing using dictionary from /home/dev/Downloads/torchMoji/model/vocabulary.json
Loading model from /home/dev/Downloads/torchMoji/model/pytorch_model.bin.
/home/dev/Downloads/torchMoji/torchmoji/model_def.py:159: UserWarning: nn.init.uniform is now deprecated in favor of nn.init.uniform_.
  nn.init.uniform(self.embed.weight.data, a=-0.5, b=0.5)
/home/dev/Downloads/torchMoji/torchmoji/model_def.py:161: UserWarning: nn.init.xavier_uniform is now deprecated in favor of nn.init.xavier_uniform_.
  nn.init.xavier_uniform(t)
/home/dev/Downloads/torchMoji/torchmoji/model_def.py:163: UserWarning: nn.init.orthogonal is now deprecated in favor of nn.init.orthogonal_.
  nn.init.orthogonal(t)
/home/dev/Downloads/torchMoji/torchmoji/model_def.py:165: UserWarning: nn.init.constant is now deprecated in favor of nn.init.constant_.
  nn.init.constant(t, 0)
/home/dev/Downloads/torchMoji/torchmoji/model_def.py:167: UserWarning: nn.init.xavier_uniform is now deprecated in favor of nn.init.xavier_uniform_.
  nn.init.xavier_uniform(self.output_layer[0].weight.data)
TorchMoji(
  (embed): Embedding(50000, 256)
  (embed_dropout): Dropout2d(p=0)
  (lstm_0): LSTMHardSigmoid(256, 512, batch_first=True, bidirectional=True)
  (lstm_1): LSTMHardSigmoid(1024, 512, batch_first=True, bidirectional=True)
  (attention_layer): Attention(2304, return attention=False)
  (final_dropout): Dropout(p=0)
  (output_layer): Sequential(
    (0): Linear(in_features=2304, out_features=64, bias=True)
    (1): Softmax()
  )
)
Running predictions.
Traceback (most recent call last):
  File "score_texts_emojis.py", line 48, in <module>
    prob = model(tokenized)
  File "/home/dev/miniconda3/envs/hub/lib/python3.5/site-packages/torch/nn/modules/module.py", line 491, in __call__
    result = self.forward(*input, **kwargs)
  File "/home/dev/Downloads/torchMoji/torchmoji/model_def.py", line 218, in forward
    packed_input = PackedSequence(data=x, batch_sizes=packed_input.batch_sizes)
TypeError: __new__() got an unexpected keyword argument 'data'

I have got the example running after the following modifications:

• Need to remove argument names from calls to PackedSequence.

https://github.com/huggingface/torchMoji/blob/b3dd91958ba2a752e0fdee2fd1839ae2bbea549a/torchmoji/model_def.py#L218

Change to packed_input = PackedSequence(x, packed_input.batch_sizes)

https://github.com/huggingface/torchMoji/blob/b3dd91958ba2a752e0fdee2fd1839ae2bbea549a/torchmoji/model_def.py#L226-L229

Change to:

        packed_input = PackedSequence(torch.cat((lstm_1_output.data,
                                                 lstm_0_output.data,
                                                 packed_input.data), dim=1),
                                      packed_input.batch_sizes)

• Cannot reverse batch_sizes.

https://github.com/huggingface/torchMoji/blob/b3dd91958ba2a752e0fdee2fd1839ae2bbea549a/torchmoji/lstm.py#L266

Change to for batch_size in reversed(list(batch_sizes)):

Bumps numpy from 1.13.1 to 1.22.0.

Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.

Hello,

I'm trying to make a deployable version of torchmoji.. I'm still very new to Pyspark and I'm doing this project on Databricks.

My code:

import pyspark.sql.functions as F
from pyspark.sql.types import *

def deepmojify(sentence,top_n=1):
  tokenized, _, _ = st.tokenize_sentences([sentence])
  prob = model(tokenized)[0]
  emoji_ids = top_elements(prob, top_n)
  emojis = map(lambda x: EMOJIS[x], emoji_ids)

  # returning the emojis as a list named as list_emojis
  return emoji.emojize(f"{' '.join(emojis)}", use_aliases=True)

udf_deepmojify = udf(deepmojify, StringType())
test_udf_deepmojify = df.withColumn("emojis", udf_deepmojify("review_by_customer"))

display(test_udf_deepmojify)

The error I keep getting is Py4JJavaError and ModuleNotFoundError: No module named 'torchmoji'.

What do I do?

Full error:

---------------------------------------------------------------------------
Py4JJavaError                             Traceback (most recent call last)
<command-4055905865243119> in <module>
----> 1 final_df_test.show()

/databricks/spark/python/pyspark/sql/dataframe.py in show(self, n, truncate, vertical)
    382         """
    383         if isinstance(truncate, bool) and truncate:
--> 384             print(self._jdf.showString(n, 20, vertical))
    385         else:
    386             print(self._jdf.showString(n, int(truncate), vertical))

/databricks/spark/python/lib/py4j-0.10.7-src.zip/py4j/java_gateway.py in __call__(self, *args)
   1255         answer = self.gateway_client.send_command(command)
   1256         return_value = get_return_value(
-> 1257             answer, self.gateway_client, self.target_id, self.name)
   1258 
   1259         for temp_arg in temp_args:

/databricks/spark/python/pyspark/sql/utils.py in deco(*a, **kw)
     61     def deco(*a, **kw):
     62         try:
---> 63             return f(*a, **kw)
     64         except py4j.protocol.Py4JJavaError as e:
     65             s = e.java_exception.toString()

/databricks/spark/python/lib/py4j-0.10.7-src.zip/py4j/protocol.py in get_return_value(answer, gateway_client, target_id, name)
    326                 raise Py4JJavaError(
    327                     "An error occurred while calling {0}{1}{2}.\n".
--> 328                     format(target_id, ".", name), value)
    329             else:
    330                 raise Py4JError(

Py4JJavaError: An error occurred while calling o12923.showString.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 77.0 failed 4 times, most recent failure: Lost task 0.3 in stage 77.0 (TID 201, 10.139.64.5, executor 0): org.apache.spark.api.python.PythonException: Traceback (most recent call last):
  File "/databricks/spark/python/pyspark/worker.py", line 464, in main
    func, profiler, deserializer, serializer = read_udfs(pickleSer, infile, eval_type)
  File "/databricks/spark/python/pyspark/worker.py", line 316, in read_udfs
    arg_offsets, udf = read_single_udf(pickleSer, infile, eval_type, runner_conf)
  File "/databricks/spark/python/pyspark/worker.py", line 170, in read_single_udf
    f, return_type = read_command(pickleSer, infile)
  File "/databricks/spark/python/pyspark/worker.py", line 73, in read_command
    command = serializer.loads(command.value)
  File "/databricks/spark/python/pyspark/serializers.py", line 695, in loads
    return pickle.loads(obj, encoding=encoding)
ModuleNotFoundError: No module named 'torchmoji'

	at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:540)
	at org.apache.spark.sql.execution.python.PythonUDFRunner$$anon$1.read(PythonUDFRunner.scala:81)
	at org.apache.spark.sql.execution.python.PythonUDFRunner$$anon$1.read(PythonUDFRunner.scala:64)
	at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:494)
	at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
	at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:440)
	at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
	at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
	at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage2.processNext(Unknown Source)
	at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
	at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$13$$anon$1.hasNext(WholeStageCodegenExec.scala:640)
	at org.apache.spark.sql.execution.collect.UnsafeRowBatchUtils$.encodeUnsafeRows(UnsafeRowBatchUtils.scala:62)
	at org.apache.spark.sql.execution.collect.Collector$$anonfun$2.apply(Collector.scala:159)
	at org.apache.spark.sql.execution.collect.Collector$$anonfun$2.apply(Collector.scala:158)
	at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
	at org.apache.spark.scheduler.Task.doRunTask(Task.scala:140)
	at org.apache.spark.scheduler.Task.run(Task.scala:113)
	at org.apache.spark.executor.Executor$TaskRunner$$anonfun$13.apply(Executor.scala:537)
	at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1541)
	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:543)
	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
	at java.lang.Thread.run(Thread.java:748)

Driver stacktrace:
	at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:2362)
	at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:2350)
	at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:2349)
	at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
	at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
	at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:2349)
	at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:1102)
	at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:1102)
	at scala.Option.foreach(Option.scala:257)
	at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:1102)
	at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2582)
	at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2529)
	at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2517)
	at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
	at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:897)
	at org.apache.spark.SparkContext.runJob(SparkContext.scala:2280)
	at org.apache.spark.sql.execution.collect.Collector.runSparkJobs(Collector.scala:270)
	at org.apache.spark.sql.execution.collect.Collector.collect(Collector.scala:280)
	at org.apache.spark.sql.execution.collect.Collector$.collect(Collector.scala:80)
	at org.apache.spark.sql.execution.collect.Collector$.collect(Collector.scala:86)
	at org.apache.spark.sql.execution.ResultCacheManager.getOrComputeResult(ResultCacheManager.scala:508)
	at org.apache.spark.sql.execution.CollectLimitExec.executeCollectResult(limit.scala:57)
	at org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$collectResult(Dataset.scala:2905)
	at org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$collectFromPlan(Dataset.scala:3517)
	at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2634)
	at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2634)
	at org.apache.spark.sql.Dataset$$anonfun$54.apply(Dataset.scala:3501)
	at org.apache.spark.sql.Dataset$$anonfun$54.apply(Dataset.scala:3496)
	at org.apache.spark.sql.execution.SQLExecution$$anonfun$withCustomExecutionEnv$1$$anonfun$apply$1.apply(SQLExecution.scala:112)
	at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:232)
	at org.apache.spark.sql.execution.SQLExecution$$anonfun$withCustomExecutionEnv$1.apply(SQLExecution.scala:98)
	at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:835)
	at org.apache.spark.sql.execution.SQLExecution$.withCustomExecutionEnv(SQLExecution.scala:74)
	at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:184)
	at org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$withAction(Dataset.scala:3496)
	at org.apache.spark.sql.Dataset.head(Dataset.scala:2634)
	at org.apache.spark.sql.Dataset.take(Dataset.scala:2848)
	at org.apache.spark.sql.Dataset.getRows(Dataset.scala:279)
	at org.apache.spark.sql.Dataset.showString(Dataset.scala:316)
	at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
	at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
	at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
	at java.lang.reflect.Method.invoke(Method.java:498)
	at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
	at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:380)
	at py4j.Gateway.invoke(Gateway.java:295)
	at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
	at py4j.commands.CallCommand.execute(CallCommand.java:79)
	at py4j.GatewayConnection.run(GatewayConnection.java:251)
	at java.lang.Thread.run(Thread.java:748)
Caused by: org.apache.spark.api.python.PythonException: Traceback (most recent call last):
  File "/databricks/spark/python/pyspark/worker.py", line 464, in main
    func, profiler, deserializer, serializer = read_udfs(pickleSer, infile, eval_type)
  File "/databricks/spark/python/pyspark/worker.py", line 316, in read_udfs
    arg_offsets, udf = read_single_udf(pickleSer, infile, eval_type, runner_conf)
  File "/databricks/spark/python/pyspark/worker.py", line 170, in read_single_udf
    f, return_type = read_command(pickleSer, infile)
  File "/databricks/spark/python/pyspark/worker.py", line 73, in read_command
    command = serializer.loads(command.value)
  File "/databricks/spark/python/pyspark/serializers.py", line 695, in loads
    return pickle.loads(obj, encoding=encoding)
ModuleNotFoundError: No module named 'torchmoji'

	at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:540)
	at org.apache.spark.sql.execution.python.PythonUDFRunner$$anon$1.read(PythonUDFRunner.scala:81)
	at org.apache.spark.sql.execution.python.PythonUDFRunner$$anon$1.read(PythonUDFRunner.scala:64)
	at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:494)
	at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
	at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:440)
	at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
	at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
	at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage2.processNext(Unknown Source)
	at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
	at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$13$$anon$1.hasNext(WholeStageCodegenExec.scala:640)
	at org.apache.spark.sql.execution.collect.UnsafeRowBatchUtils$.encodeUnsafeRows(UnsafeRowBatchUtils.scala:62)
	at org.apache.spark.sql.execution.collect.Collector$$anonfun$2.apply(Collector.scala:159)
	at org.apache.spark.sql.execution.collect.Collector$$anonfun$2.apply(Collector.scala:158)
	at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
	at org.apache.spark.scheduler.Task.doRunTask(Task.scala:140)
	at org.apache.spark.scheduler.Task.run(Task.scala:113)
	at org.apache.spark.executor.Executor$TaskRunner$$anonfun$13.apply(Executor.scala:537)
	at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1541)
	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:543)
	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
	... 1 more

(This should be somehow a pull request, but I have no idea how to do that, I am not a github-expert user) In order to work with the provided torchMoji follow these instructions:

• Use Ubuntu (version 16.04 in my case) and install Miniconda or Anaconda
• Create the file torchmoji_env.yml containing:

name: torchmoji_env
channels:
  - pytorch
  - defaults
dependencies:
  - _libgcc_mutex=0.1=main
  - alabaster=0.7.12=py36_0
  - astroid=2.4.2=py36_0
  - attrs=19.3.0=py_0
  - babel=2.8.0=py_0
  - backcall=0.2.0=py_0
  - blas=1.0=mkl
  - bleach=3.1.5=py_0
  - brotlipy=0.7.0=py36h7b6447c_1000
  - ca-certificates=2020.6.24=0
  - certifi=2020.6.20=py36_0
  - cffi=1.14.0=py36h2e261b9_0
  - chardet=3.0.4=py36_1003
  - cloudpickle=1.4.1=py_0
  - cryptography=2.9.2=py36h1ba5d50_0
  - cudatoolkit=10.0.130=0
  - dbus=1.13.16=hb2f20db_0
  - decorator=4.4.2=py_0
  - defusedxml=0.6.0=py_0
  - docutils=0.16=py36_1
  - entrypoints=0.3=py36_0
  - expat=2.2.9=he6710b0_2
  - fontconfig=2.13.0=h9420a91_0
  - freetype=2.10.2=h5ab3b9f_0
  - glib=2.63.1=h5a9c865_0
  - gst-plugins-base=1.14.0=hbbd80ab_1
  - gstreamer=1.14.0=hb453b48_1
  - icu=58.2=he6710b0_3
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  - importlib_metadata=1.7.0=0
  - intel-openmp=2019.4=243
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  - ipython_genutils=0.2.0=py36_0
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  - libgcc-ng=9.1.0=hdf63c60_0
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  - ncurses=6.2=he6710b0_1
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  - packaging=20.4=py_0
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  - pytorch=1.0.1=py3.6_cuda10.0.130_cudnn7.4.2_2
  - pytz=2020.1=py_0
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  - zlib=1.2.11=h7b6447c_3
  - pip:
    - emoji==0.4.5
    - jupyter-client==6.1.5
    - nose==1.3.7
    - numpy==1.13.1
    - pandas==0.20.3
    - scikit-learn==0.19.0
    - scipy==0.19.1
    - text-unidecode==1.0
prefix: /home/user/Miniconda3/ana/envs/torchmoji_env

Then, install and activate it as:

conda env create -f torchmoji_env.yml
conda activate torchmoji_env

Now in order to run it, you have to fix some bugs in the code (maybe un-compatibility issues), look at: https://github.com/huggingface/torchMoji/issues/20 https://github.com/huggingface/torchMoji/issues/21#issuecomment-512642707 and when you get a bug in something like .numpy() [0] replace it with .numpy()

I try to re-train the model but I don't think that the GPU is ever used ...

The part of the code I use is:

model = torchmoji_transfer(nb_classes, weight_path=pretrained_path)
model, acc = finetune(model, split_texts, split_labels, nb_classes, batch_size, nb_epochs = 1000, method =  'chain-thaw')

and GPU doesn't show to increase its usage

Hi,

I have cloned the repository and downloaded weights with given script. Then I have tried to run examples/text_emojize.py with --text "Today is great'" --maxlen 30, but it gave ValueError with following traceback:

➜  torchMoji git:(master) python3 examples/text_emojize.py --text "Today is great!" --maxlen 30
Traceback (most recent call last):
  File "examples/text_emojize.py", line 55, in <module>
    prob = model(tokenized)[0]
  File "/Users/zafer/.pyenv/versions/3.6.7/lib/python3.6/site-packages/torch/nn/modules/module.py", line 547, in __call__
    result = self.forward(*input, **kwargs)
  File "/Users/repos/torchMoji/torchmoji/model_def.py", line 222, in forward
    lstm_0_output, _ = self.lstm_0(packed_input, hidden)
  File "/Users/zafer/.pyenv/versions/3.6.7/lib/python3.6/site-packages/torch/nn/modules/module.py", line 547, in __call__
    result = self.forward(*input, **kwargs)
  File "/Users/repos/torchMoji/torchmoji/lstm.py", line 78, in forward
    input, batch_sizes = input
ValueError: too many values to unpack (expected 2)

My torch version is 1.2.0 and Python version is 3.6.7

As mentioned in DeepMoji GitHub repo https://github.com/bfelbo/DeepMoji, the large Twitter dataset of tweets with emojis is not released.

I wonder if there is still a chance to get the original training dataset, even a permission is required. If I understand correctly, torchMoji is also trained on the same dataset, right? Could you share how you get the training dataset? In the original paper, I saw the authors wrote

The authors would like to thank Janys Analytics for generously allowing us to use their dataset ofhuman-rated tweets

Should I contact Janys Analytics in order to get the training dataset?