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my-lora-sum

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  1. .gitattributes +1 -0
  2. README.md +189 -0
  3. adapter_config.json +32 -0
  4. adapter_model.safetensors +3 -0
  5. runs/Mar10_23-08-48_1412920010036/events.out.tfevents.1741662535.1412920010036.5471.0 +3 -0
  6. special_tokens_map.json +23 -0
  7. spiece.model +3 -0
  8. tokenizer.json +3 -0
  9. tokenizer_config.json +840 -0
  10. training_args.bin +3 -0
.gitattributes CHANGED
@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ tokenizer.json filter=lfs diff=lfs merge=lfs -text
README.md ADDED
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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: google/mt5-small
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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: my-lora-sum
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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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+ # my-lora-sum
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+
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+ This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 5.3452
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+ - Rouge1: 2.9750
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+ - Rouge2: 0.0640
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+ - Rougel: 2.8027
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+ - Rougelsum: 2.8035
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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: 0.0001
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+ - train_batch_size: 4
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+ - eval_batch_size: 4
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+ - seed: 42
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+ - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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+ - lr_scheduler_type: linear
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+ - num_epochs: 2
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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 |
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+ |:-------------:|:------:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
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+ | 23.6472 | 0.0160 | 5 | 11.7512 | 0.4881 | 0.0353 | 0.4380 | 0.4362 |
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+ | 21.3789 | 0.0319 | 10 | 11.7109 | 0.4912 | 0.0353 | 0.4526 | 0.4486 |
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+ | 19.719 | 0.0479 | 15 | 11.6279 | 0.5005 | 0.0432 | 0.4531 | 0.4477 |
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+ | 22.2901 | 0.0639 | 20 | 11.6000 | 0.4946 | 0.0319 | 0.4410 | 0.4381 |
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+ | 19.6455 | 0.0799 | 25 | 11.5404 | 0.5263 | 0.0530 | 0.4856 | 0.4833 |
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+ | 19.892 | 0.0958 | 30 | 11.5011 | 0.5202 | 0.0396 | 0.4798 | 0.4716 |
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+ | 19.8908 | 0.1118 | 35 | 11.4428 | 0.5431 | 0.0429 | 0.5013 | 0.4933 |
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+ | 21.5179 | 0.1278 | 40 | 11.4066 | 0.5156 | 0.0405 | 0.4717 | 0.4682 |
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+ | 21.7581 | 0.1438 | 45 | 11.3629 | 0.5416 | 0.0325 | 0.4834 | 0.4787 |
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+ | 20.9988 | 0.1597 | 50 | 11.2869 | 0.5288 | 0.0270 | 0.4752 | 0.4743 |
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+ | 21.3311 | 0.1757 | 55 | 11.2290 | 0.5260 | 0.0223 | 0.4642 | 0.4629 |
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+ | 23.6669 | 0.1917 | 60 | 11.1785 | 0.4932 | 0.0188 | 0.4345 | 0.4307 |
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+ | 18.2309 | 0.2077 | 65 | 11.1129 | 0.4645 | 0.0193 | 0.4156 | 0.4170 |
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+ | 20.9785 | 0.2236 | 70 | 11.0331 | 0.5031 | 0.0238 | 0.4532 | 0.4530 |
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+ | 16.4643 | 0.2396 | 75 | 10.9403 | 0.4414 | 0.0177 | 0.3945 | 0.3899 |
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+ | 16.8242 | 0.2556 | 80 | 10.8374 | 0.4631 | 0.0177 | 0.4165 | 0.4110 |
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+ | 23.1133 | 0.2716 | 85 | 10.7508 | 0.5054 | 0.0253 | 0.4434 | 0.4380 |
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+ | 21.1076 | 0.2875 | 90 | 10.6718 | 0.4766 | 0.0178 | 0.4129 | 0.4101 |
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+ | 17.6879 | 0.3035 | 95 | 10.5684 | 0.5166 | 0.0212 | 0.4634 | 0.4573 |
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+ | 19.3182 | 0.3195 | 100 | 10.4315 | 0.5774 | 0.0217 | 0.5121 | 0.5070 |
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+ | 18.6413 | 0.3355 | 105 | 10.2988 | 0.5325 | 0.0257 | 0.4803 | 0.4803 |
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+ | 19.9276 | 0.3514 | 110 | 10.1745 | 0.5529 | 0.0348 | 0.4877 | 0.4841 |
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+ | 17.9226 | 0.3674 | 115 | 10.0297 | 0.5852 | 0.0432 | 0.5075 | 0.5039 |
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+ | 15.9707 | 0.3834 | 120 | 9.8977 | 0.5779 | 0.0403 | 0.5015 | 0.4992 |
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+ | 16.5054 | 0.3994 | 125 | 9.7266 | 0.5685 | 0.0416 | 0.5011 | 0.4963 |
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+ | 14.6994 | 0.4153 | 130 | 9.5595 | 0.6368 | 0.0506 | 0.5492 | 0.5447 |
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+ | 15.9555 | 0.4313 | 135 | 9.3894 | 0.6401 | 0.0438 | 0.5520 | 0.5465 |
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+ | 14.876 | 0.4473 | 140 | 9.2403 | 0.6910 | 0.0467 | 0.5961 | 0.5929 |
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+ | 17.3615 | 0.4633 | 145 | 9.0976 | 0.7497 | 0.0499 | 0.6499 | 0.6448 |
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+ | 15.8582 | 0.4792 | 150 | 8.9385 | 0.7409 | 0.0383 | 0.6427 | 0.6398 |
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+ | 14.4111 | 0.4952 | 155 | 8.7924 | 0.7075 | 0.0457 | 0.6404 | 0.6366 |
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+ | 13.9827 | 0.5112 | 160 | 8.6373 | 0.6750 | 0.0446 | 0.6211 | 0.6173 |
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+ | 6.2542 | 1.9968 | 625 | 5.3452 | 2.9750 | 0.0640 | 2.8027 | 2.8035 |
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+
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+
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+ ### Framework versions
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+
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+ - PEFT 0.14.0
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+ - Transformers 4.49.0
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+ - Pytorch 2.6.0+cu124
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+ - Datasets 3.3.2
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+ - Tokenizers 0.21.0
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