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README.md ADDED
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+ ---
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+ license: apache-2.0
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+ base_model: LazarusNLP/IndoNanoT5-base
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - id_liputan6
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+ metrics:
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+ - rouge
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+ model-index:
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+ - name: liputan6-pt-pl50
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+ results: []
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+ ---
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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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+
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+ # liputan6-pt-pl50
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+
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+ This model is a fine-tuned version of [LazarusNLP/IndoNanoT5-base](https://huggingface.co/LazarusNLP/IndoNanoT5-base) on the id_liputan6 dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 3.7381
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+ - Rouge1: 19.5385
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+ - Rouge2: 5.1106
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+ - Rougel: 16.7601
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+ - Rougelsum: 17.9271
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+ - Gen Len: 29.142
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+
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+ ## Model description
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+
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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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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 0.001
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+ - train_batch_size: 16
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+ - eval_batch_size: 32
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+ - seed: 42
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - num_epochs: 5.0
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
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+ |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
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+ | 4.7245 | 1.0 | 63 | 3.9912 | 16.8276 | 3.6927 | 14.367 | 15.3151 | 30.652 |
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+ | 3.9104 | 2.0 | 126 | 3.8609 | 17.712 | 4.2061 | 14.9465 | 15.9818 | 35.104 |
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+ | 3.6651 | 3.0 | 189 | 3.8036 | 18.8508 | 4.6943 | 15.8363 | 17.0134 | 30.749 |
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+ | 3.4442 | 4.0 | 252 | 3.7533 | 19.7665 | 5.1425 | 16.7615 | 18.1456 | 28.31 |
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+ | 3.2664 | 5.0 | 315 | 3.7381 | 19.5385 | 5.1106 | 16.7601 | 17.9271 | 29.142 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.40.2
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+ - Pytorch 2.3.1+cu121
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+ - Datasets 2.20.0
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+ - Tokenizers 0.19.1
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+ "hidden_size": 768,
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+ "model_class": "T5AdapterModel",
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+ "name": "adapter-summarization",
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