<jupyter_start><jupyter_text>BENDeepBENDeep is a pytorch based deep learning solution for Bengali NLP Task like bengali translation, bengali sentiment analysis and so on.  Installation<jupyter_code>!pip install bendeep<jupyter_output>Collecting bendeep
  Downloading https://files.pythonhosted.org/packages/4d/52/279d35a893bba7bad6454d74dc860850ed2cb77c7b70b18539e53d548cee/bendeep-1.2-py3-none-any.whl
Requirement already satisfied: tqdm in /usr/local/lib/python3.6/dist-packages (from bendeep) (4.41.1)
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Requirement already satisfied: scipy>=0.17.0 in /usr/local/lib/python3.6/dist-packages (from scikit-learn->bendeep) (1.4.1)
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Requirement already satisfied: pyparsing!=2.0.4,!=2.[...]<jupyter_text>Sentiment Analysis  Analyze<jupyter_code>from bendeep import sentiment
model_path = "trained.pt"
vocab_path = "vocab.txt"
text = "রোহিঙ্গা মুসলমানদের দুর্ভোগের অন্ত নেই।জলে কুমির ডাংগায় বাঘ।আজকে দুটি ঘটনা আমাকে ভীষণ ব্যতিত করেছে।নিরবে কিছুক্ষন অশ্রু বিসর্জন দিয়ে মনটাকে হাল্কা করার ব্যর্থ প্রয়াস চালিয়েছি।"

sentiment.analyze(model_path, vocab_path, text)<jupyter_output>3.25e-05: Negative sentiment<jupyter_text>Training<jupyter_code>from bendeep import sentiment

data_path = 'socian_bn_sen2.csv'

sentiment.train(data_path, epochs=100)<jupyter_output><empty_output><jupyter_text>Translation  Bengali to English Translation<jupyter_code>from bendeep import translation
from bendeep.translation import EncoderRNN
from bendeep.translation import AttnDecoderRNN

data_path = "eng-ben.txt"
encoder = "encoder.pt"
decoder = "decoder.pt"
input_sentence = "আমার শীত করছে।"
translation.bn2en(data_path, encoder, decoder, input_sentence)<jupyter_output>> আমার শীত করছে ।
= i feel cold .<jupyter_text>Training Translation Model<jupyter_code>from bendeep import translation
from bendeep.translation import EncoderRNN
from bendeep.translation import AttnDecoderRNN

data_path = "eng-ben.txt"
translation.training(data_path, iteration=5000)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.12619426751592358, 'fraction_numerical': 0.042993630573248405, 'mean_word_length': 6.661585365853658, 'pattern_counts': {'":': 0, '<': 18, '<?xml version=': 0, '>': 23, 'https://': 1, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 1, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '5', 'dir': 'jupyter-structured-clean-dedup', 'id': '404957', 'license': 'MIT', 'n_tokens_mistral': 1116, 'n_tokens_neox': 1125, 'n_words': 194, 'path': 'notebook/bendeep_colab_notebook.ipynb', 'repo_name': 'sagorbrur/bendeep', 'size': '2512'}