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language: ro |
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# ALBert |
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The ALR-Bert , **cased** model for Romanian, trained on a 15GB corpus! |
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ALR-BERT is a multi-layer bidirectional Transformer encoder that shares ALBERT's factorized embedding parameterization and cross-layer sharing. ALR-BERT-base inherits ALBERT-base and features 12 parameter-sharing layers, a 128-dimension embedding size, 768 hidden units, 12 heads, and GELU non-linearities. Masked language modeling (MLM) and sentence order prediction (SOP) losses are the two objectives that ALBERT is pre-trained on. For ALR-BERT, we preserve both these objectives. |
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The model was trained using 40 batches per GPU (for 128 sequence length) and then 20 batches per GPU (for 512 sequence length). Layer-wise Adaptive Moments optimizer for Batch (LAMB) training was utilized, with a warm-up over the first 1\% of steps up to a learning rate of 1e4, then a decay. Eight NVIDIA Tesla V100 SXM3 with 32GB memory were used, and the pre-training process took around 2 weeks per model. |
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Training methodology follows closely work previous done in Romanian Bert (https://huggingface.co/dumitrescustefan/bert-base-romanian-cased-v1) |
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### How to use |
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```python |
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from transformers import AutoTokenizer, AutoModel |
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import torch |
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# load tokenizer and model |
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tokenizer = AutoTokenizer.from_pretrained("dragosnicolae555/ALR_BERT") |
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model = AutoModel.from_pretrained("dragosnicolae555/ALR_BERT") |
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#Here add your magic |
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``` |
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Remember to always sanitize your text! Replace ``s`` and ``t`` cedilla-letters to comma-letters with : |
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``` |
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text = text.replace("ţ", "ț").replace("ş", "ș").replace("Ţ", "Ț").replace("Ş", "Ș") |
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``` |
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because the model was **NOT** trained on cedilla ``s`` and ``t``s. If you don't, you will have decreased performance due to <UNK>s and increased number of tokens per word. |
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### Evaluation |
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Here, we evaluate ALR-BERT on Simple Universal Dependencies task. One model for each task, evaluating labeling performance on the UPOS (Universal Part-of-Speech) and the XPOS (Extended Part-of-Speech) (eXtended Part-of-Speech). We compare our proposed ALR-BERT with Romanian BERT and multiligual BERT, using the cased version. To counteract the random seed effect, we repeat each experiment five times and simply provide the mean score. |
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| Model | UPOS | XPOS | MLAS | AllTags | |
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|--------------------------------|:-----:|:------:|:-----:|:-----:| |
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| M-BERT (cased) | 93.87 | 89.89 | 90.01 | 87.04| |
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| Romanian BERT (cased) | 95.56 | 95.35 | 92.78 | 93.22 | |
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| ALR-BERT (cased) | **87.38** | **84.05** | **79.82** | **78.82**| |
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### Corpus |
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The model is trained on the following corpora (stats in the table below are after cleaning): |
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| Corpus | Lines(M) | Words(M) | Chars(B) | Size(GB) | |
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|----------- |:--------: |:--------: |:--------: |:--------: | |
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| OPUS | 55.05 | 635.04 | 4.045 | 3.8 | |
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| OSCAR | 33.56 | 1725.82 | 11.411 | 11 | |
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| Wikipedia | 1.54 | 60.47 | 0.411 | 0.4 | |
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| **Total** | **90.15** | **2421.33** | **15.867** | **15.2** | |
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