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README.md
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---
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library_name: peft
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license: apache-2.0
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base_model: agemagician/mlong-t5-tglobal-base
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tags:
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- generated_from_trainer
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model-index:
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- name: amharic_summarization_lora_model_normalized
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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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# amharic_summarization_lora_model_normalized
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This model is a fine-tuned version of [agemagician/mlong-t5-tglobal-base](https://huggingface.co/agemagician/mlong-t5-tglobal-base) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.9366
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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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: 0.0001
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- train_batch_size: 8
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- eval_batch_size: 8
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- seed: 42
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- gradient_accumulation_steps: 16
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- total_train_batch_size: 128
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- optimizer: Use OptimizerNames.ADAFACTOR and the args are:
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No additional optimizer arguments
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- lr_scheduler_type: cosine
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- num_epochs: 3
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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:------:|:----:|:---------------:|
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| 1.2261 | 0.4913 | 400 | 1.0317 |
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| 1.1147 | 0.9826 | 800 | 0.9737 |
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| 1.082 | 1.4729 | 1200 | 0.9517 |
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| 1.0729 | 1.9642 | 1600 | 0.9467 |
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| 1.062 | 2.4544 | 2000 | 0.9352 |
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| 1.0552 | 2.9457 | 2400 | 0.9366 |
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### Framework versions
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- PEFT 0.14.1.dev0
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- Transformers 4.57.1
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- Pytorch 2.6.0+cu124
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- Datasets 3.2.0
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- Tokenizers 0.22.1
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adapter_model.safetensors
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