transfer models from org to user account

README.md

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----
-license: mit
----
+---
+language: ar
+datasets:
+- oscar
+- wikipedia
+tags:
+- ar
+- masked-lm
+---
+
+
+# Arabic-ALBERT Base
+
+Arabic edition of ALBERT Base pretrained language model
+
+_If you use any of these models in your work, please cite this work as:_
+
+```
+@software{ali_safaya_2020_4718724,
+  author       = {Ali Safaya},
+  title        = {Arabic-ALBERT},
+  month        = aug,
+  year         = 2020,
+  publisher    = {Zenodo},
+  version      = {1.0.0},
+  doi          = {10.5281/zenodo.4718724},
+  url          = {https://doi.org/10.5281/zenodo.4718724}
+}
+```
+
+## Pretraining data
+
+The models were pretrained on ~4.4 Billion words:
+
+- Arabic version of [OSCAR](https://oscar-corpus.com/) (unshuffled version of the corpus) - filtered from [Common Crawl](http://commoncrawl.org/)
+- Recent dump of Arabic [Wikipedia](https://dumps.wikimedia.org/backup-index.html)
+
+__Notes on training data:__
+
+- Our final version of corpus contains some non-Arabic words inlines, which we did not remove from sentences since that would affect some tasks like NER.
+- Although non-Arabic characters were lowered as a preprocessing step, since Arabic characters do not have upper or lower case, there is no cased and uncased version of the model.
+- The corpus and vocabulary set are not restricted to Modern Standard Arabic, they contain some dialectical Arabic too.
+
+## Pretraining details
+
+- These models were trained using Google ALBERT's github [repository](https://github.com/google-research/albert) on a single TPU v3-8 provided for free from [TFRC](https://www.tensorflow.org/tfrc).
+- Our pretraining procedure follows training settings of bert with some changes: trained for 7M training steps with batchsize of 64, instead of 125K with batchsize of 4096.
+
+## Models
+
+|  | albert-base | albert-large | albert-xlarge |
+|:---:|:---:|:---:|:---:|
+| Hidden Layers | 12 | 24 | 24 |
+| Attention heads | 12 | 16 | 32 |
+| Hidden size | 768 | 1024 | 2048 |
+
+## Results
+
+For further details on the models performance or any other queries, please refer to [Arabic-ALBERT](https://github.com/KUIS-AI-Lab/Arabic-ALBERT/)
+
+## How to use
+
+You can use these models by installing `torch` or `tensorflow` and Huggingface library `transformers`. And you can use it directly by initializing it like this:  
+
+```python
+
+from transformers import AutoTokenizer, AutoModel
+
+# loading the tokenizer
+base_tokenizer    = AutoTokenizer.from_pretrained("kuisailab/albert-base-arabic")
+
+# loading the model
+base_model   = AutoModelForMaskedLM.from_pretrained("kuisailab/albert-base-arabic")
+
+```
+
+## Acknowledgement
+
+Thanks to Google for providing free TPU for the training process and for Huggingface for hosting these models on their servers 😊

config.json

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+{
+  "architectures": [
+    "AlbertForMaskedLM"
+  ],
+  "attention_probs_dropout_prob": 0,
+  "bos_token_id": 2,
+  "classifier_dropout_prob": 0.1,
+  "down_scale_factor": 1,
+  "embedding_size": 128,
+  "eos_token_id": 3,
+  "gap_size": 0,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "inner_group_num": 1,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "albert",
+  "net_structure_type": 0,
+  "num_attention_heads": 12,
+  "num_hidden_groups": 1,
+  "num_hidden_layers": 12,
+  "num_memory_blocks": 0,
+  "pad_token_id": 0,
+  "type_vocab_size": 2,
+  "vocab_size": 30000
+}

pytorch_model.bin

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special_tokens_map.json

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spiece.model

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tf_model.h5

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tokenizer_config.json

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