Add README.md

README.md

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+---
+language: 
+- ar
+license: apache-2.0
+widget:
+ - text: "الهدف من الحياة هو [MASK] ."
+---
+
+# bert-base-camelbert-msa
+
+## Model description
+
+**CAMeLBERT** is a BERT model pre-trained on Arabic texts with different sizes and variants.
+The details are described in the paper *"The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models."*
+We release eight models with different sizes and variants as follows:
+
+||Model|Variant|Size|#Word|
+|-|-|:-:|-:|-:|
+|✔|`bert-base-camelbert-mix`|CA,DA,MSA|167GB|17.3B|
+||`bert-base-camelbert-ca`|CA|6GB|847M|
+||`bert-base-camelbert-da`|DA|54GB|5.8B|
+||`bert-base-camelbert-msa`|MSA|107GB|12.6B|
+||`bert-base-camelbert-msa-half`|MSA|53GB|6.3B|
+||`bert-base-camelbert-msa-quarter`|MSA|27GB|3.1B|
+||`bert-base-camelbert-msa-eighth`|MSA|14GB|1.6B|
+||`bert-base-camelbert-msa-sixteenth`|MSA|6GB|746M|
+
+This model card describes `bert-base-camelbert-mix`, a model pre-trained on a mixture of these variants: CA, DA, and MSA.
+
+## Intended uses
+You can use the released model for either masked language modeling or next sentence prediction.
+However, it is mostly intended to be fine-tuned on an NLP task, such as NER, POS tagging, sentiment analysis, dialect identification, and poetry classification.
+We release our fine-tuninig code [here](https://github.com/CAMeL-Lab/CAMeLBERT).
+
+#### How to use
+You can use this model directly with a pipeline for masked language modeling:
+```python
+>>> from transformers import pipeline
+>>> unmasker = pipeline('fill-mask', model='bert-base-camelbert-mix')
+>>> unmasker("الهدف من الحياة هو [MASK] .")
+[{'sequence': '[CLS] الهدف من الحياة هو النجاح. [SEP]',
+  'score': 0.10861027985811234,
+  'token': 6232,
+  'token_str': 'النجاح'},
+ {'sequence': '[CLS] الهدف من الحياة هو.. [SEP]',
+  'score': 0.07626965641975403,
+  'token': 18,
+  'token_str': '.'},
+ {'sequence': '[CLS] الهدف من الحياة هو الحياة. [SEP]',
+  'score': 0.05131986364722252,
+  'token': 3696,
+  'token_str': 'الحياة'},
+ {'sequence': '[CLS] الهدف من الحياة هو الموت. [SEP]',
+  'score': 0.03734956309199333,
+  'token': 4295,
+  'token_str': 'الموت'},
+ {'sequence': '[CLS] الهدف من الحياة هو العمل. [SEP]',
+  'score': 0.027189988642930984,
+  'token': 2854,
+  'token_str': 'العمل'}]
+```
+
+Here is how to use this model to get the features of a given text in PyTorch:
+```python
+from transformers import AutoTokenizer, AutoModel
+tokenizer = AutoTokenizer.from_pretrained('bert-base-camelbert-mix')
+model = AutoModel.from_pretrained('bert-base-camelbert-mix')
+text = "مرحبا يا عالم."
+encoded_input = tokenizer(text, return_tensors='pt')
+output = model(**encoded_input)
+```
+
+and in TensorFlow:
+```python
+from transformers import AutoTokenizer, TFAutoModel
+tokenizer = AutoTokenizer.from_pretrained('bert-base-camelbert-mix')
+model = TFAutoModel.from_pretrained('bert-base-camelbert-mix')
+text = "مرحبا يا عالم."
+encoded_input = tokenizer(text, return_tensors='tf')
+output = model(encoded_input)
+```
+
+## Training data
+- MSA
+  - [The Arabic Gigaword Fifth Edition](https://catalog.ldc.upenn.edu/LDC2011T11)
+  - [Abu El-Khair Corpus](http://www.abuelkhair.net/index.php/en/arabic/abu-el-khair-corpus)
+  - [OSIAN corpus](https://vlo.clarin.eu/search;jsessionid=31066390B2C9E8C6304845BA79869AC1?1&q=osian)
+  - [Arabic Wikipedia](https://archive.org/details/arwiki-20190201)
+  - The unshuffled version of the Arabic [OSCAR corpus](https://oscar-corpus.com/)
+- DA
+  - A collection of dialectal Arabic data described in our paper.
+- CA
+  - [OpenITI (Version 2020.1.2)](https://zenodo.org/record/3891466#.YEX4-F0zbzc)
+
+## Training procedure
+We use [the original implementation](https://github.com/google-research/bert) released by Google for pre-training.
+We follow the original English BERT model's hyperparameters for pre-training, unless otherwise specified.
+
+### Preprocessing
+- After extracting the raw text from each corpus, we apply the following pre-processing.
+- We first remove invalid characters and normalize white spaces using the utilities provided by [the original BERT implementation](https://github.com/google-research/bert/blob/eedf5716ce1268e56f0a50264a88cafad334ac61/tokenization.py#L286-L297).
+- We also remove lines without any Arabic characters.
+- We then remove diacritics and kashida using [CAMeL Tools](https://github.com/CAMeL-Lab/camel_tools).
+- Finally, we split each line into sentences with a heuristics-based sentence segmenter.
+- We train a WordPiece tokenizer on the entire dataset (167 GB text) with a vocabulary size of 30,000 using [HuggingFace's tokenizers](https://github.com/huggingface/tokenizers).
+- We do not lowercase letters nor strip accents.
+
+### Pre-training
+- The model was trained on a single cloud TPU (`v3-8`) for one million steps in total.
+- The first 90,000 steps were trained with a batch size of 1,024 and the rest was trained with a batch size of 256.
+- The sequence length was limited to 128 tokens for 90% of the steps and 512 for the remaining 10%.
+- We use whole word masking and a duplicate factor of 10.
+- We set max predictions per sequence to 20 for the dataset with max sequence length of 128 tokens and 80 for the dataset with max sequence length of 512 tokens.
+- We use a random seed of 12345, masked language model probability of 0.15, and short sequence probability of 0.1.
+- The optimizer used is Adam with a learning rate of 1e-4, \\(\beta_{1} = 0.9\\) and \\(\beta_{2} = 0.999\\), a weight decay of 0.01, learning rate warmup for 10,000 steps and linear decay of the learning rate after.
+
+## Evaluation results
+- We evaluate our pre-trained language models on five NLP tasks: NER, POS tagging, sentiment analysis, dialect identification, and poetry classification.
+- We fine-tune and evaluate the models using 12 dataset.
+- We used Hugging Face's transformers to fine-tune our CAMeLBERT models.
+- We used transformers `v3.1.0` along with PyTorch `v1.5.1`.
+- The fine-tuning was done by adding a fully connected linear layer to the last hidden state.
+- We use \\F_{1}\\ score as a metric for all tasks.
+- Code used for fine-tuning is available [here](https://github.com/CAMeL-Lab/CAMeLBERT).
+
+### Results
+
+| Task                 | Dataset         | Variant | Mix   | CA    | DA    | MSA   | MSA-1/2 | MSA-1/4 | MSA-1/8 | MSA-1/16 |
+| -------------------- | --------------- | ------- | ----- | ----- | ----- | ----- | ------- | ------- | ------- | -------- |
+| NER                  | ANERcorp        | MSA     | 80.2% | 66.2% | 74.2% | 82.4% | 82.3%   | 82.0%   | 82.3%   | 80.5%    |
+| POS                  | PATB (MSA)      | MSA     | 97.3% | 96.6% | 96.5% | 97.4% | 97.4%   | 97.4%   | 97.4%   | 97.4%    |
+|                      | ARZTB (EGY)     | DA      | 90.1% | 88.6% | 89.4% | 90.8% | 90.3%   | 90.5%   | 90.5%   | 90.4%    |
+|                      | Gumar (GLF)     | DA      | 97.3% | 96.5% | 97.0% | 97.1% | 97.0%   | 97.0%   | 97.1%   | 97.0%    |
+| SA                   | ASTD            | MSA     | 76.3% | 69.4% | 74.6% | 76.9% | 76.0%   | 76.8%   | 76.7%   | 75.3%    |
+|                      | ArSAS           | MSA     | 92.7% | 89.4% | 91.8% | 93.0% | 92.6%   | 92.5%   | 92.5%   | 92.3%    |
+|                      | SemEval         | MSA     | 69.0% | 58.5% | 68.4% | 72.1% | 70.7%   | 72.8%   | 71.6%   | 71.2%    |
+| DID                  | MADAR-26        | DA      | 62.9% | 61.9% | 61.8% | 62.6% | 62.0%   | 62.8%   | 62.0%   | 62.2%    |
+|                      | MADAR-6         | DA      | 92.5% | 91.5% | 92.2% | 91.9% | 91.8%   | 92.2%   | 92.1%   | 92.0%    |
+|                      | MADAR-Twitter-5 | MSA     | 75.7% | 71.4% | 74.2% | 77.6% | 78.5%   | 77.3%   | 77.7%   | 76.2%    |
+|                      | NADI            | DA      | 24.7% | 17.3% | 20.1% | 24.9% | 24.6%   | 24.6%   | 24.9%   | 23.8%    |
+| Poetry               | APCD            | CA      | 79.8% | 80.9% | 79.6% | 79.7% | 79.9%   | 80.0%   | 79.7%   | 79.8%    |
+
+### Results (Average)
+|                      | Variant | Mix   | CA    | DA    | MSA   | MSA-1/2 | MSA-1/4 | MSA-1/8 | MSA-1/16 |
+| -------------------- | ------- | ----- | ----- | ----- | ----- | ------- | ------- | ------- | -------- |
+| Variant-wise-average<sup>[[1]](#footnote-1)</sup> | MSA     | 81.9% | 75.3% | 79.9% | 83.2% | 82.9%   | 83.1%   | 83.0%   | 82.1%    |
+|                      | DA      | 73.5% | 71.1% | 72.1% | 73.5% | 73.1%   | 73.4%   | 73.3%   | 73.1%    |
+|                      | CA      | 79.8% | 80.9% | 79.6% | 79.7% | 79.9%   | 80.0%   | 79.7%   | 79.8%    |
+| Macro-Average        | ALL     | 78.2% | 74.0% | 76.6% | 78.9% | 78.6%   | 78.8%   | 78.7%   | 78.2%    |
+
+<a name="footnote-1">[1]</a>: Variant-wise-average refers to average over a group of tasks in the same language variant.
+
+## Acknowledgements
+This research was supported with Cloud TPUs from Google’s TensorFlow Research Cloud (TFRC).
+
+## Citation
+
+```bibtex
+@inproceedings{inoue-etal-2021-interplay,
+    title = "The Interplay of Variant, Size, and Task Type in {A}rabic Pre-trained Language Models",
+    author = "Inoue, Go  and
+      Alhafni, Bashar  and
+      Baimukan, Nurpeiis  and
+      Bouamor, Houda  and
+      Habash, Nizar",
+    booktitle = "Proceedings of the Sixth Arabic Natural Language Processing Workshop",
+    month = apr,
+    year = "2021",
+    address = "Kyiv, Ukraine (Online)",
+    publisher = "Association for Computational Linguistics",
+    abstract = "In this paper, we explore the effects of language variants, data sizes, and fine-tuning task types in Arabic pre-trained language models. To do so, we build three pre-trained language models across three variants of Arabic: Modern Standard Arabic (MSA), dialectal Arabic, and classical Arabic, in addition to a fourth language model which is pre-trained on a mix of the three. We also examine the importance of pre-training data size by building additional models that are pre-trained on a scaled-down set of the MSA variant. We compare our different models to each other, as well as to eight publicly available models by fine-tuning them on five NLP tasks spanning 12 datasets. Our results suggest that the variant proximity of pre-training data to fine-tuning data is more important than the pre-training data size. We exploit this insight in defining an optimized system selection model for the studied tasks.",
+}
+```