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😇A pyTorch implementation of the DeepMoji model: state-of-the-art deep learning model for analyzing sentiment, emotion, sarcasm etc

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Bumps [numpy](https://github.com/numpy/numpy) from 1.13.1 to 1.22.0. Release notes Sourced from numpy's releases. v1.22.0 NumPy 1.22.0 Release Notes NumPy 1.22.0 is a big release featuring the work of 153 contributors spread...

dependencies

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...

Thanks for open-sourcing this work! I'm afraid I couldn't get the dependencies to resolve without conflicts when following the README instructions, so this PR upgrades the dependency. Since the README...

I'm trying to train the model using my own data. In the examples directory, it says to run: `create_twitter_vocab.py` This script tries to open this file: `../../twitterdata/tweets.2016-09-01 ` But there...

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: ```python import pyspark.sql.functions as F...

(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...

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)...

I am just curious that how can I get the impact of the words in the sentence that determines the respective emojis for the sentence, as shown in [http://deepmoji.mit.edu/](http://deepmoji.mit.edu/) Thanks...

As mentioned in DeepMoji GitHub repo [https://github.com/bfelbo/DeepMoji](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...

https://github.com/huggingface/torchMoji/blob/198f7d4e0711a7d3cd01968812af0121c54477f8/torchmoji/model_def.py#L243 during inference in batches, if `reorder_output` becomes `True` (if `input_seqs` are not of `PackedSequence`), then outputs are correctly reordered to match input order. **however**, if `return_attention` is requested (set...