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summarization-seq_bn-1

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  1. README.md +68 -0
  2. adapter-summarization/adapter_config.json +42 -0
  3. adapter-summarization/head_config.json +14 -0
  4. adapter-summarization/pytorch_adapter.bin +3 -0
  5. adapter-summarization/pytorch_model_head.bin +3 -0
  6. runs/Jul06_11-56-34_a94680512e46/events.out.tfevents.1720267002.a94680512e46.1463952.0 +2 -2
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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+ metrics:
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+ - rouge
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+ model-index:
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+ - name: summarization-seq_bn-1
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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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+ # summarization-seq_bn-1
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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 an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.4996
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+ - Rouge1: 0.756
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+ - Rouge2: 0.0
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+ - Rougel: 0.7531
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+ - Rougelsum: 0.7521
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+ - Gen Len: 1.0
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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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+ | 0.7745 | 1.0 | 892 | 0.5559 | 0.7822 | 0.0 | 0.7815 | 0.7779 | 1.0 |
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+ | 0.604 | 2.0 | 1784 | 0.5500 | 0.7671 | 0.0 | 0.7671 | 0.7636 | 1.0 |
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+ | 0.5554 | 3.0 | 2676 | 0.5186 | 0.7816 | 0.0 | 0.7796 | 0.7794 | 1.0 |
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+ | 0.5189 | 4.0 | 3568 | 0.5018 | 0.7767 | 0.0 | 0.7758 | 0.7718 | 1.0 |
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+ | 0.4887 | 5.0 | 4460 | 0.4996 | 0.756 | 0.0 | 0.7531 | 0.7521 | 1.0 |
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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
adapter-summarization/adapter_config.json ADDED
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+ {
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+ "config": {
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+ "adapter_residual_before_ln": false,
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+ "cross_adapter": false,
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+ "dropout": 0.0,
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+ "factorized_phm_W": true,
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+ "factorized_phm_rule": false,
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+ "hypercomplex_nonlinearity": "glorot-uniform",
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+ "init_weights": "bert",
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+ "inv_adapter": null,
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+ "inv_adapter_reduction_factor": null,
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+ "is_parallel": false,
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+ "learn_phm": true,
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+ "leave_out": [],
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+ "ln_after": false,
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+ "ln_before": false,
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+ "mh_adapter": false,
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+ "non_linearity": "relu",
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+ "original_ln_after": true,
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+ "original_ln_before": true,
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+ "output_adapter": true,
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+ "phm_bias": true,
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+ "phm_c_init": "normal",
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+ "phm_dim": 4,
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+ "phm_init_range": 0.0001,
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+ "phm_layer": false,
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+ "phm_rank": 1,
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+ "reduction_factor": 16,
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+ "residual_before_ln": true,
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+ "scaling": 1.0,
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+ "shared_W_phm": false,
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+ "shared_phm_rule": true,
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+ "use_gating": false
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+ },
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+ "config_id": "9076f36a74755ac4",
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+ "hidden_size": 768,
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+ "model_class": "T5ForConditionalGeneration",
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+ "model_name": "LazarusNLP/IndoNanoT5-base",
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+ "model_type": "t5",
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+ "name": "adapter-summarization",
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+ "version": "0.2.2"
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+ }
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+ "model_class": "T5ForConditionalGeneration",
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+ "model_name": "LazarusNLP/IndoNanoT5-base",
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+ "model_type": "t5",
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+ "name": null,
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+ "num_labels": 2,
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+ "version": "0.2.2"
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+ }
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