---
license: apache-2.0
base_model: google-bert/bert-base-cased
tags:
- generated_from_trainer
model-index:
- name: bert_baseline_prompt_adherence_task4_fold0
  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. -->

# bert_baseline_prompt_adherence_task4_fold0

This model is a fine-tuned version of [google-bert/bert-base-cased](https://huggingface.co/google-bert/bert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4271
- Qwk: 0.6387
- Mse: 0.4239

## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Qwk    | Mse    |
|:-------------:|:------:|:----:|:---------------:|:------:|:------:|
| No log        | 0.0299 | 2    | 1.2960          | 0.0    | 1.2930 |
| No log        | 0.0597 | 4    | 0.9177          | 0.0    | 0.9154 |
| No log        | 0.0896 | 6    | 0.8271          | 0.3838 | 0.8254 |
| No log        | 0.1194 | 8    | 0.7326          | 0.3465 | 0.7313 |
| No log        | 0.1493 | 10   | 0.6599          | 0.3581 | 0.6586 |
| No log        | 0.1791 | 12   | 0.6243          | 0.3717 | 0.6227 |
| No log        | 0.2090 | 14   | 0.6024          | 0.3919 | 0.6005 |
| No log        | 0.2388 | 16   | 0.5293          | 0.3990 | 0.5275 |
| No log        | 0.2687 | 18   | 0.5958          | 0.6599 | 0.5946 |
| No log        | 0.2985 | 20   | 0.5865          | 0.6470 | 0.5851 |
| No log        | 0.3284 | 22   | 0.4997          | 0.6200 | 0.4975 |
| No log        | 0.3582 | 24   | 0.4852          | 0.4550 | 0.4825 |
| No log        | 0.3881 | 26   | 0.5626          | 0.3360 | 0.5596 |
| No log        | 0.4179 | 28   | 0.6943          | 0.2663 | 0.6911 |
| No log        | 0.4478 | 30   | 0.6648          | 0.2753 | 0.6616 |
| No log        | 0.4776 | 32   | 0.5340          | 0.3669 | 0.5308 |
| No log        | 0.5075 | 34   | 0.4475          | 0.5778 | 0.4444 |
| No log        | 0.5373 | 36   | 0.4749          | 0.6546 | 0.4720 |
| No log        | 0.5672 | 38   | 0.5331          | 0.6635 | 0.5306 |
| No log        | 0.5970 | 40   | 0.5591          | 0.6712 | 0.5569 |
| No log        | 0.6269 | 42   | 0.5329          | 0.6517 | 0.5307 |
| No log        | 0.6567 | 44   | 0.4773          | 0.6521 | 0.4749 |
| No log        | 0.6866 | 46   | 0.4526          | 0.5105 | 0.4499 |
| No log        | 0.7164 | 48   | 0.4667          | 0.4248 | 0.4638 |
| No log        | 0.7463 | 50   | 0.4597          | 0.4232 | 0.4567 |
| No log        | 0.7761 | 52   | 0.4413          | 0.4921 | 0.4382 |
| No log        | 0.8060 | 54   | 0.4265          | 0.5327 | 0.4234 |
| No log        | 0.8358 | 56   | 0.4218          | 0.5857 | 0.4188 |
| No log        | 0.8657 | 58   | 0.4221          | 0.6155 | 0.4191 |
| No log        | 0.8955 | 60   | 0.4244          | 0.6239 | 0.4213 |
| No log        | 0.9254 | 62   | 0.4273          | 0.6354 | 0.4242 |
| No log        | 0.9552 | 64   | 0.4272          | 0.6387 | 0.4241 |
| No log        | 0.9851 | 66   | 0.4271          | 0.6387 | 0.4239 |


### Framework versions

- Transformers 4.42.3
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1