Model save
Browse files- README.md +68 -0
- adapter-summarization/adapter_config.json +77 -0
- adapter-summarization/head_config.json +14 -0
- adapter-summarization/pytorch_adapter.bin +3 -0
- adapter-summarization/pytorch_model_head.bin +3 -0
- runs/Jul06_20-02-43_a94680512e46/events.out.tfevents.1720296172.a94680512e46.1632364.0 +2 -2
README.md
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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-unipelt-0
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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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# summarization-unipelt-0
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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: 0.6921
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- Rouge1: 0.5367
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- Rouge2: 0.0
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- Rougel: 0.5393
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- Rougelsum: 0.5356
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- Gen Len: 1.0
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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: 0.001
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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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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
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| 2.4132 | 1.0 | 892 | 1.2387 | 0.5009 | 0.0 | 0.5 | 0.5041 | 1.0 |
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| 1.481 | 2.0 | 1784 | 0.9344 | 0.5062 | 0.0 | 0.5067 | 0.5051 | 1.0 |
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| 1.194 | 3.0 | 2676 | 0.7902 | 0.6449 | 0.0 | 0.6488 | 0.6454 | 1.0 |
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| 1.0257 | 4.0 | 3568 | 0.7316 | 0.4501 | 0.0 | 0.4461 | 0.4508 | 1.0 |
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| 0.9184 | 5.0 | 4460 | 0.6921 | 0.5367 | 0.0 | 0.5393 | 0.5356 | 1.0 |
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### Framework versions
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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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adapter-summarization/adapter_config.json
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{
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"config": {
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"architecture": "union",
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"configs": [
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{
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"architecture": "prefix_tuning",
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"bottleneck_size": 512,
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"cross_prefix": true,
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"dropout": 0.0,
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"encoder_prefix": true,
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"flat": false,
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"leave_out": [],
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"non_linearity": "tanh",
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"prefix_length": 10,
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"shared_gating": true,
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"use_gating": true
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},
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{
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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": true
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},
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{
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"alpha": 2,
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"architecture": "lora",
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"attn_matrices": [
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"q",
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"v"
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],
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"composition_mode": "add",
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"dropout": 0.0,
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"init_weights": "lora",
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"intermediate_lora": false,
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"leave_out": [],
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"output_lora": false,
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"r": 8,
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"selfattn_lora": true,
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"use_gating": true
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}
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]
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},
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"config_id": "67ac4937c601ad56",
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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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adapter-summarization/head_config.json
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{
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"config": null,
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"hidden_size": 768,
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"label2id": {
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"LABEL_0": 0,
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"LABEL_1": 1
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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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adapter-summarization/pytorch_adapter.bin
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adapter-summarization/pytorch_model_head.bin
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