| --- |
| license: apache-2.0 |
| language: |
| - fa |
| pipeline_tag: fill-mask |
| mask_token: "<mask>" |
| widget: |
| - text: "توانا بود هر که <mask> بود ز دانش دل پیر برنا بود" |
| - text: "شهر برلین در کشور <mask> واقع شده است." |
| - text: "بهنام <mask> از خوانندگان مشهور کشور ما است." |
| - text: "رضا <mask> از بازیگران مشهور کشور ما است." |
| - text: "سید ابراهیم رییسی در سال <mask> رییس جمهور ایران شد." |
| - text: "دیگر امکان ادامه وجود ندارد. باید قرارداد را <mask> کنیم." |
| --- |
| # 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.** |
|
|
| For more information you can read our paper on [arXiv](https://arxiv.org/abs/2407.16382). |
|
|
| ## How to use |
|
|
| You can use this model directly for Masked Language Modeling using the provided code below. |
|
|
| ```Python |
| 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. |
|
|
| ```Python |
| 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) <a href="https://colab.research.google.com/drive/1G2LgVRwq4X9J0Tf6vg7Pwm5hmCZ5ziJ4#scrollTo=1B1YrypZxajF"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Colab Code" width="87" height="15"/></a> |
| - ParsiNLU - Multiple-choice (Multiple-choice) <a href="https://colab.research.google.com/drive/1Xs48SYi6tRiofIh86h221fiMf6ZPiPS4#scrollTo=kdavgKAeGj4s"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Colab Code" width="87" height="15"/></a> |
|
|
| ## 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 | <u>83.93/83.93</u> | <u>66.23/93.3</u> | <u>73.18</u>/<u>85.71</u>/<u>68.29</u>/<u>85.94</u> | <u>83.26/83.41</u> | 33.6/<u>33.81</u> | 20.8/42.52 | <u>81.33/81.29</u> | |
| | Shiraz | 81.17/81.08 | 59.1/92.83 | 65.96/81.25/59.63/81.31 | 77.76/77.75 | <u>34.73/34.53</u> | 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 | <u>83.43/83.36</u> | 58.83/92.23 | <u>73.26</u>/<u>85.69</u>/<u>68.21</u>/<u>85.56</u> | 81.1/81.2 | **35.28/35.25** | 8/26.66 | 80.1/79.96 | |
| | XLM-RoBERTa-base | <u>83.99/84.07</u> | 60.38/92.49 | <u>73.72</u>/<u>86.24</u>/<u>68.16</u>/<u>85.8</u> | 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 | <u>72.57</u>/<u>85.39</u>/67.16/<u>85.31</u> | <u>83.69/83.67</u> | 32.47/32.37 | <u>27.2/48.42</u> | **82.34/82.29** | |
| | mBERT | 78.57/78.66 | 60.31/92.54 | 71.79/84.68/65.89/83.99 | <u>82.69/82.82</u> | 33.41/33.09 | <u>27.2</u>/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](https://huggingface.co/PartAI/TookaBERT-Large/discussions) of the model in Hugging Face. |
| |
