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Model Details

TookaBERT models are a family of encoder models trained on Persian in two sizes base and large. These Models pre-trained on over 500GB of Persian data including a variety of topics such as News, Blogs, Forums, Books, etc. They pre-trained with the MLM (WWM) objective using two context lengths. TookaBERT-Large is the first large encoder model pre-trained on Persian and currently is the state-of-the-art model in Persian tasks.

How to use

You can use this model directly for Masked Language Modeling using the provided code below.

from transformers import AutoTokenizer, AutoModelForMaskedLM

tokenizer = AutoTokenizer.from_pretrained("PartAI/TookaBERT-Large")
model = AutoModelForMaskedLM.from_pretrained("PartAI/TookaBERT-Large")

# prepare input
text = "شهر برلین در کشور <mask> واقع شده است."
encoded_input = tokenizer(text, return_tensors='pt')

# forward pass
output = model(**encoded_input)

It is also possible to use inference pipelines such as below.

from transformers import pipeline

inference_pipeline = pipeline('fill-mask', model="PartAI/TookaBERT-Large")
inference_pipeline("شهر برلین در کشور <mask> واقع شده است.")

You can use this model to fine-tune it over your dataset and prepare it for your task.

  • DeepSentiPers (Sentiment Analysis) Colab Code
  • ParsiNLU - Multiple-choice (Multiple-choice) Colab Code

Evaluation

TookaBERT models are evaluated on a wide range of NLP downstream tasks, such as Sentiment Analysis (SA), Text Classification, Multiple-choice, Question Answering, and Named Entity Recognition (NER). Here are some key performance results:

Model name DeepSentiPers (f1/acc) MultiCoNER-v2 (f1/acc) PQuAD (best_exact/best_f1/HasAns_exact/HasAns_f1) FarsTail (f1/acc) ParsiNLU-Multiple-choice (f1/acc) ParsiNLU-Reading-comprehension (exact/f1) ParsiNLU-QQP (f1/acc)
TookaBERT-large 85.66/85.78 69.69/94.07 75.56/88.06/70.24/87.83 89.71/89.72 36.13/35.97 33.6/60.5 82.72/82.63
TookaBERT-base 83.93/83.93 66.23/93.3 73.18/85.71/68.29/85.94 83.26/83.41 33.6/33.81 20.8/42.52 81.33/81.29
Shiraz 81.17/81.08 59.1/92.83 65.96/81.25/59.63/81.31 77.76/77.75 34.73/34.53 17.6/39.61 79.68/79.51
ParsBERT 80.22/80.23 64.91/93.23 71.41/84.21/66.29/84.57 80.89/80.94 35.34/35.25 20/39.58 80.15/80.07
XLM-V-base 83.43/83.36 58.83/92.23 73.26/85.69/68.21/85.56 81.1/81.2 35.28/35.25 8/26.66 80.1/79.96
XLM-RoBERTa-base 83.99/84.07 60.38/92.49 73.72/86.24/68.16/85.8 82.0/81.98 32.4/32.37 20.0/40.43 79.14/78.95
FaBERT 82.68/82.65 63.89/93.01 72.57/85.39/67.16/85.31 83.69/83.67 32.47/32.37 27.2/48.42 82.34/82.29
mBERT 78.57/78.66 60.31/92.54 71.79/84.68/65.89/83.99 82.69/82.82 33.41/33.09 27.2/42.18 79.19/79.29
AriaBERT 80.51/80.51 60.98/92.45 68.09/81.23/62.12/80.94 74.47/74.43 30.75/30.94 14.4/35.48 79.09/78.84

*Note because of the randomness in the fine-tuning process, results with less than 1% differences are considered together.

Contact us

If you have any questions regarding this model, you can reach us via the community of the model in Hugging Face.

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