---
library_name: transformers
license: apache-2.0
base_model: distilbert/distilbert-base-cased
tags:
- generated_from_trainer
metrics:
- accuracy
- rouge
model-index:
- name: 8a6cfd3a5b44ec8ad4a1399b8111c2d7
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# 8a6cfd3a5b44ec8ad4a1399b8111c2d7

This model is a fine-tuned version of [distilbert/distilbert-base-cased](https://huggingface.co/distilbert/distilbert-base-cased) on the nyu-mll/glue [cola] dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6513
- Data Size: 1.0
- Epoch Runtime: 8.1961
- Accuracy: 0.7783
- F1 Macro: 0.7220
- Rouge1: 0.7783
- Rouge2: 0.0
- Rougel: 0.7783
- Rougelsum: 0.7783

## 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    | 0.7325          | 0         | 0.8980        | 0.3115   | 0.2375   | 0.3105 | 0.0    | 0.3115 | 0.3115    |
| No log        | 1     | 267  | 0.6273          | 0.0078    | 1.4091        | 0.6885   | 0.4078   | 0.6895 | 0.0    | 0.6885 | 0.6885    |
| No log        | 2     | 534  | 0.6784          | 0.0156    | 1.2061        | 0.6885   | 0.4078   | 0.6895 | 0.0    | 0.6885 | 0.6885    |
| No log        | 3     | 801  | 0.6180          | 0.0312    | 1.3246        | 0.6885   | 0.4078   | 0.6895 | 0.0    | 0.6885 | 0.6885    |
| No log        | 4     | 1068 | 0.6193          | 0.0625    | 1.6425        | 0.6885   | 0.4078   | 0.6895 | 0.0    | 0.6885 | 0.6885    |
| 0.0364        | 5     | 1335 | 0.6810          | 0.125     | 2.2565        | 0.6895   | 0.4111   | 0.6904 | 0.0    | 0.6895 | 0.6895    |
| 0.5377        | 6     | 1602 | 0.5564          | 0.25      | 3.0908        | 0.7178   | 0.5519   | 0.7178 | 0.0    | 0.7178 | 0.7178    |
| 0.4537        | 7     | 1869 | 0.5744          | 0.5       | 4.7843        | 0.7383   | 0.5958   | 0.7393 | 0.0    | 0.7383 | 0.7383    |
| 0.3558        | 8.0   | 2136 | 0.5633          | 1.0       | 8.4360        | 0.7598   | 0.6593   | 0.7607 | 0.0    | 0.7598 | 0.7598    |
| 0.2181        | 9.0   | 2403 | 0.7488          | 1.0       | 8.3009        | 0.7637   | 0.6865   | 0.7637 | 0.0    | 0.7637 | 0.7637    |
| 0.1705        | 10.0  | 2670 | 0.6513          | 1.0       | 8.1961        | 0.7783   | 0.7220   | 0.7783 | 0.0    | 0.7783 | 0.7783    |


### Framework versions

- Transformers 4.57.0
- Pytorch 2.8.0+cu128
- Datasets 4.3.0
- Tokenizers 0.22.1