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---
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
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