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library_name: transformers
license: apache-2.0
base_model: distilbert/distilbert-base-cased
tags:
- generated_from_trainer
metrics:
- accuracy
- rouge
model-index:
- name: df545d64e2eee43eeeab91c8bb51fb25
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. -->
# df545d64e2eee43eeeab91c8bb51fb25
This model is a fine-tuned version of [distilbert/distilbert-base-cased](https://huggingface.co/distilbert/distilbert-base-cased) on the nyu-mll/glue [mnli] dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7537
- Data Size: 1.0
- Epoch Runtime: 325.9256
- Accuracy: 0.7797
- F1 Macro: 0.7793
- Rouge1: 0.7798
- Rouge2: 0.0
- Rougel: 0.7798
- Rougelsum: 0.7798
## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 32
- total_eval_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: constant
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Data Size | Epoch Runtime | Accuracy | F1 Macro | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:------:|:---------------:|:---------:|:-------------:|:--------:|:--------:|:------:|:------:|:------:|:---------:|
| No log | 0 | 0 | 1.1005 | 0 | 2.9869 | 0.3545 | 0.1745 | 0.3544 | 0.0 | 0.3545 | 0.3543 |
| 1.0605 | 1 | 12271 | 0.9454 | 0.0078 | 5.8974 | 0.5638 | 0.5620 | 0.5640 | 0.0 | 0.5638 | 0.5639 |
| 0.8949 | 2 | 24542 | 0.8368 | 0.0156 | 8.2417 | 0.6395 | 0.6349 | 0.6397 | 0.0 | 0.6397 | 0.6396 |
| 0.7852 | 3 | 36813 | 0.7743 | 0.0312 | 13.3493 | 0.6633 | 0.6577 | 0.6632 | 0.0 | 0.6634 | 0.6633 |
| 0.7373 | 4 | 49084 | 0.6952 | 0.0625 | 23.0806 | 0.7144 | 0.7138 | 0.7145 | 0.0 | 0.7145 | 0.7143 |
| 0.6321 | 5 | 61355 | 0.6261 | 0.125 | 43.0151 | 0.7362 | 0.7354 | 0.7363 | 0.0 | 0.7361 | 0.7362 |
| 0.6133 | 6 | 73626 | 0.6297 | 0.25 | 79.9808 | 0.7430 | 0.7436 | 0.7429 | 0.0 | 0.7432 | 0.7430 |
| 0.5218 | 7 | 85897 | 0.5868 | 0.5 | 160.8165 | 0.7641 | 0.7628 | 0.7640 | 0.0 | 0.7642 | 0.7643 |
| 0.5068 | 8.0 | 98168 | 0.5666 | 1.0 | 319.1719 | 0.7797 | 0.7798 | 0.7795 | 0.0 | 0.7797 | 0.7798 |
| 0.4137 | 9.0 | 110439 | 0.5714 | 1.0 | 321.2843 | 0.7796 | 0.7780 | 0.7795 | 0.0 | 0.7796 | 0.7795 |
| 0.3429 | 10.0 | 122710 | 0.6298 | 1.0 | 332.5481 | 0.7815 | 0.7795 | 0.7814 | 0.0 | 0.7816 | 0.7816 |
| 0.2739 | 11.0 | 134981 | 0.7452 | 1.0 | 341.5238 | 0.7786 | 0.7785 | 0.7786 | 0.0 | 0.7784 | 0.7785 |
| 0.2477 | 12.0 | 147252 | 0.7537 | 1.0 | 325.9256 | 0.7797 | 0.7793 | 0.7798 | 0.0 | 0.7798 | 0.7798 |
### Framework versions
- Transformers 4.57.0
- Pytorch 2.8.0+cu128
- Datasets 4.3.0
- Tokenizers 0.22.1
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