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  ---
 
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  tags:
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- - generated_from_trainer
 
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  datasets:
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- - oscar-corpus/OSCAR-2109
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- model-index:
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- - name: runs
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- results: []
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  ---
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- <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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- should probably proofread and complete it, then remove this comment. -->
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- # runs
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- This model was trained from scratch on the oscar-corpus/OSCAR-2109 deduplicated_lo dataset.
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- It achieves the following results on the evaluation set:
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- - Loss: 1.4556
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- ## Model description
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- More information needed
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-
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- ## Intended uses & limitations
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-
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- More information needed
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-
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- ## Training and evaluation data
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-
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- More information needed
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  ## Training procedure
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
 
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  - learning_rate: 0.0002
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  - train_batch_size: 128
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  - eval_batch_size: 128
@@ -50,40 +41,80 @@ The following hyperparameters were used during training:
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss |
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- |:-------------:|:-----:|:----:|:---------------:|
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- | No log | 1.0 | 216 | 5.8586 |
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- | No log | 2.0 | 432 | 5.5095 |
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- | 6.688 | 3.0 | 648 | 5.3976 |
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- | 6.688 | 4.0 | 864 | 5.3562 |
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- | 5.3629 | 5.0 | 1080 | 5.2912 |
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- | 5.3629 | 6.0 | 1296 | 5.2385 |
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- | 5.22 | 7.0 | 1512 | 5.1955 |
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- | 5.22 | 8.0 | 1728 | 5.1785 |
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- | 5.22 | 9.0 | 1944 | 5.1327 |
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- | 5.1248 | 10.0 | 2160 | 5.1243 |
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- | 5.1248 | 11.0 | 2376 | 5.0889 |
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- | 5.0591 | 12.0 | 2592 | 5.0732 |
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- | 5.0591 | 13.0 | 2808 | 5.0417 |
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- | 5.0094 | 14.0 | 3024 | 5.0388 |
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- | 5.0094 | 15.0 | 3240 | 4.9299 |
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- | 5.0094 | 16.0 | 3456 | 4.2991 |
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- | 4.7527 | 17.0 | 3672 | 3.6541 |
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- | 4.7527 | 18.0 | 3888 | 2.7826 |
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- | 3.4431 | 19.0 | 4104 | 2.2796 |
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- | 3.4431 | 20.0 | 4320 | 2.0213 |
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- | 2.2803 | 21.0 | 4536 | 1.8809 |
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- | 2.2803 | 22.0 | 4752 | 1.7615 |
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- | 2.2803 | 23.0 | 4968 | 1.6925 |
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- | 1.8601 | 24.0 | 5184 | 1.6205 |
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- | 1.8601 | 25.0 | 5400 | 1.5751 |
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- | 1.6697 | 26.0 | 5616 | 1.5391 |
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- | 1.6697 | 27.0 | 5832 | 1.5200 |
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- | 1.5655 | 28.0 | 6048 | 1.4866 |
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- | 1.5655 | 29.0 | 6264 | 1.4656 |
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- | 1.5655 | 30.0 | 6480 | 1.4627 |
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-
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-
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- ### Framework versions
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  - Transformers 4.13.0.dev0
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  - Pytorch 1.9.0+cu102
 
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  ---
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+ language: lo
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  tags:
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+ - lao-roberta-base
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+ license: mit
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  datasets:
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+ - oscar-corpus/OSCAR-2109
 
 
 
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  ---
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+ ## Lao RoBERTa Base
 
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+ Lao RoBERTa Base is a masked language model based on the [RoBERTa](https://arxiv.org/abs/1907.11692) model. It was trained on the [OSCAR-2109](https://huggingface.co/datasets/oscar-corpus/OSCAR-2109) dataset, specifically the `deduplicated_lo` subset. The model was trained from scratch and achieved an evaluation loss of 1.4556 and an evaluation perplexity of 4.287.
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+ This model was trained using HuggingFace's PyTorch framework and the training script found [here](https://github.com/huggingface/transformers/blob/master/examples/pytorch/language-modeling/run_mlm.py). All training was done on a TPUv3-8, provided by the [TPU Research Cloud](https://sites.research.google/trc/about/) program. You can view the detailed training results in the [Training metrics](https://huggingface.co/w11wo/lao-roberta-base/tensorboard) tab, logged via Tensorboard.
 
 
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+ ## Model
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+ | Model | #params | Arch. | Training/Validation data (text) |
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+ | ------------------ | ------- | ------- | ------------------------------------ |
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+ | `lao-roberta-base` | 124M | RoBERTa | OSCAR-2109 `deduplicated_lo` Dataset |
 
 
 
 
 
 
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  ## Training procedure
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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+
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  - learning_rate: 0.0002
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  - train_batch_size: 128
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  - eval_batch_size: 128
 
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss |
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+ | :-----------: | :---: | :--: | :-------------: |
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+ | No log | 1.0 | 216 | 5.8586 |
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+ | No log | 2.0 | 432 | 5.5095 |
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+ | 6.688 | 3.0 | 648 | 5.3976 |
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+ | 6.688 | 4.0 | 864 | 5.3562 |
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+ | 5.3629 | 5.0 | 1080 | 5.2912 |
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+ | 5.3629 | 6.0 | 1296 | 5.2385 |
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+ | 5.22 | 7.0 | 1512 | 5.1955 |
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+ | 5.22 | 8.0 | 1728 | 5.1785 |
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+ | 5.22 | 9.0 | 1944 | 5.1327 |
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+ | 5.1248 | 10.0 | 2160 | 5.1243 |
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+ | 5.1248 | 11.0 | 2376 | 5.0889 |
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+ | 5.0591 | 12.0 | 2592 | 5.0732 |
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+ | 5.0591 | 13.0 | 2808 | 5.0417 |
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+ | 5.0094 | 14.0 | 3024 | 5.0388 |
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+ | 5.0094 | 15.0 | 3240 | 4.9299 |
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+ | 5.0094 | 16.0 | 3456 | 4.2991 |
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+ | 4.7527 | 17.0 | 3672 | 3.6541 |
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+ | 4.7527 | 18.0 | 3888 | 2.7826 |
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+ | 3.4431 | 19.0 | 4104 | 2.2796 |
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+ | 3.4431 | 20.0 | 4320 | 2.0213 |
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+ | 2.2803 | 21.0 | 4536 | 1.8809 |
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+ | 2.2803 | 22.0 | 4752 | 1.7615 |
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+ | 2.2803 | 23.0 | 4968 | 1.6925 |
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+ | 1.8601 | 24.0 | 5184 | 1.6205 |
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+ | 1.8601 | 25.0 | 5400 | 1.5751 |
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+ | 1.6697 | 26.0 | 5616 | 1.5391 |
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+ | 1.6697 | 27.0 | 5832 | 1.5200 |
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+ | 1.5655 | 28.0 | 6048 | 1.4866 |
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+ | 1.5655 | 29.0 | 6264 | 1.4656 |
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+ | 1.5655 | 30.0 | 6480 | 1.4627 |
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+
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+ ## How to Use
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+
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+ ### As Masked Language Model
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+
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+ ```python
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+ from transformers import pipeline
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+
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+ pretrained_name = "w11wo/lao-roberta-base"
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+ prompt = "REPLACE WITH MASKED PROMPT"
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+
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+ fill_mask = pipeline(
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+ "fill-mask",
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+ model=pretrained_name,
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+ tokenizer=pretrained_name
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+ )
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+
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+ fill_mask(prompt)
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+ ```
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+
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+ ### Feature Extraction in PyTorch
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+
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+ ```python
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+ from transformers import RobertaModel, RobertaTokenizerFast
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+
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+ pretrained_name = "w11wo/lao-roberta-base"
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+ model = RobertaModel.from_pretrained(pretrained_name)
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+ tokenizer = RobertaTokenizerFast.from_pretrained(pretrained_name)
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+
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+ prompt = "ສະ​ບາຍ​ດີ​ຊາວ​ໂລກ."
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+ encoded_input = tokenizer(prompt, return_tensors='pt')
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+ output = model(**encoded_input)
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+ ```
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+
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+ ## Disclaimer
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+
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+ Do consider the biases which came from pre-training datasets that may be carried over into the results of this model.
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+
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+ ## Author
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+
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+ Lao RoBERTa Base was trained and evaluated by [Wilson Wongso](https://w11wo.github.io/). All computation and development are done on Google's TPU-RC.
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+
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+ ## Framework versions
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  - Transformers 4.13.0.dev0
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  - Pytorch 1.9.0+cu102