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<pre class="v2-to-clipboard-content whitespace-pre-line text-sm p-4 text-gray-700">
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("HooshvareLab/bert-fa-base-uncased-sentiment-digikala")
model = AutoModelForSequenceClassification.from_pretrained("HooshvareLab/bert-fa-base-uncased-sentiment-digikala")
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Or just clone the model repo
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git lfs install
git clone https://huggingface.co/HooshvareLab/bert-fa-base-uncased-sentiment-digikala
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# if you want to clone without large files – just their pointers
# prepend your git clone with the following env var:
GIT_LFS_SKIP_SMUDGE=1
</pre>
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<div class="prose">
<h1 id="parsbert-v20">ParsBERT (v2.0)</h1>
<p>A Transformer-based Model for Persian Language Understanding</p>
<p>We reconstructed the vocabulary and fine-tuned the ParsBERT v1.1 on the new Persian corpora in order to provide some functionalities for using ParsBERT in other scopes!
Please follow the <a href="https://github.com/hooshvare/parsbert">ParsBERT</a> repo for the latest information about previous and current models.</p>
<h2 id="persian-sentiment-digikala-snappfood-deepsentipers">Persian Sentiment [Digikala, SnappFood, DeepSentiPers]</h2>
<p>It aims to classify text, such as comments, based on their emotional bias. We tested three well-known datasets for this task: <code>Digikala</code> user comments, <code>SnappFood</code> user comments, and <code>DeepSentiPers</code> in two binary-form and multi-form types.</p>
<h3 id="digikala">Digikala</h3>
<p>Digikala user comments provided by <a href="https://www.digikala.com/opendata/">Open Data Mining Program (ODMP)</a>. This dataset contains 62,321 user comments with three labels: </p>
<th align="center">Label</th>
<th align="center">#</th>
<td align="center">no_idea</td>
<td align="center">10394</td>
<td align="center">not_recommended</td>
<td align="center">15885</td>
<td align="center">recommended</td>
<td align="center">36042</td>
<p><strong>Download</strong>
You can download the dataset from <a href="https://www.digikala.com/opendata/">here</a></p>
<h2 id="results">Results</h2>
<p>The following table summarizes the F1 score obtained by ParsBERT as compared to other models and architectures.</p>
<th align="center">Dataset</th>
<th align="center">ParsBERT v2</th>
<th align="center">ParsBERT v1</th>
<th align="center">mBERT</th>
<th align="center">DeepSentiPers</th>
<td align="center">Digikala User Comments</td>
<td align="center">81.72</td>
<td align="center">81.74*</td>
<td align="center">80.74</td>
<td align="center">-</td>
<h2 id="how-to-use-hugs">How to use :hugs:</h2>
<table>
<thead>
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</thead>
<tbody><tr>
<td>Sentiment Analysis</td>
<td><a href="https://colab.research.google.com/github/hooshvare/parsbert/blob/master/notebooks/Taaghche_Sentiment_Analysis.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
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<h3 id="bibtex-entry-and-citation-info">BibTeX entry and citation info</h3>
<p>Please cite in publications as the following:</p>
<pre><code class="language-bibtex">@article{ParsBERT,
title={ParsBERT: Transformer-based Model for Persian Language Understanding},
author={Mehrdad Farahani, Mohammad Gharachorloo, Marzieh Farahani, Mohammad Manthouri},
journal={ArXiv},
year={2020},
volume={abs/2005.12515}
}</code></pre>
<h2 id="questions">Questions?</h2>
<p>Post a Github issue on the <a href="https://github.com/hooshvare/parsbert/issues">ParsBERT Issues</a> repo.</p>
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