Model save

README.md

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+---
+license: apache-2.0
+base_model: LazarusNLP/IndoNanoT5-base
+tags:
+- generated_from_trainer
+metrics:
+- rouge
+model-index:
+- name: indosum-seq_bn-rf16-0
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# indosum-seq_bn-rf16-0
+
+This model is a fine-tuned version of [LazarusNLP/IndoNanoT5-base](https://huggingface.co/LazarusNLP/IndoNanoT5-base) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.4868
+- Rouge1: 73.1452
+- Rouge2: 66.2201
+- Rougel: 70.1397
+- Rougelsum: 72.2952
+- Gen Len: 102.3827
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 0.001
+- train_batch_size: 16
+- eval_batch_size: 32
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 5.0
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len  |
+|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:|
+| 0.7747        | 1.0   | 892  | 0.5280          | 70.368  | 63.1479 | 67.3463 | 69.4649   | 100.8813 |
+| 0.6074        | 2.0   | 1784 | 0.5154          | 71.3426 | 64.1849 | 68.288  | 70.4796   | 104.6013 |
+| 0.5576        | 3.0   | 2676 | 0.4920          | 71.6471 | 64.5469 | 68.6561 | 70.7418   | 101.8973 |
+| 0.5199        | 4.0   | 3568 | 0.4931          | 72.695  | 65.8499 | 69.8428 | 71.8427   | 103.6333 |
+| 0.4895        | 5.0   | 4460 | 0.4868          | 73.1452 | 66.2201 | 70.1397 | 72.2952   | 102.3827 |
+
+
+### Framework versions
+
+- Transformers 4.40.2
+- Pytorch 2.3.1+cu121
+- Datasets 2.20.0
+- Tokenizers 0.19.1

adapter-summarization/adapter_config.json

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+    "cross_adapter": false,
+    "dropout": 0.0,
+    "factorized_phm_W": true,
+    "factorized_phm_rule": false,
+    "hypercomplex_nonlinearity": "glorot-uniform",
+    "init_weights": "bert",
+    "inv_adapter": null,
+    "inv_adapter_reduction_factor": null,
+    "is_parallel": false,
+    "learn_phm": true,
+    "leave_out": [],
+    "ln_after": false,
+    "ln_before": false,
+    "mh_adapter": false,
+    "non_linearity": "relu",
+    "original_ln_after": true,
+    "original_ln_before": true,
+    "output_adapter": true,
+    "phm_bias": true,
+    "phm_c_init": "normal",
+    "phm_dim": 4,
+    "phm_init_range": 0.0001,
+    "phm_layer": false,
+    "phm_rank": 1,
+    "reduction_factor": 16,
+    "residual_before_ln": true,
+    "scaling": 1.0,
+    "shared_W_phm": false,
+    "shared_phm_rule": true,
+    "use_gating": false
+  },
+  "config_id": "9076f36a74755ac4",
+  "hidden_size": 768,
+  "model_class": "T5AdapterModel",
+  "model_name": "LazarusNLP/IndoNanoT5-base",
+  "model_type": "t5",
+  "name": "adapter-summarization",
+  "version": "0.2.2"
+}

adapter-summarization/pytorch_adapter.bin

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default/head_config.json

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+  "config": {
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+    "bias": false,
+    "head_type": "seq2seq_lm",
+    "label2id": null,
+    "layer_norm": false,
+    "layers": 1,
+    "shift_labels": false,
+    "vocab_size": 32128
+  },
+  "hidden_size": 768,
+  "model_class": "T5AdapterModel",
+  "model_name": "LazarusNLP/IndoNanoT5-base",
+  "model_type": "t5",
+  "name": "default",
+  "version": "0.2.2"
+}

default/pytorch_model_head.bin

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