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library_name: transformers
language:
- en
base_model: Hartunka/bert_base_rand_5_v1
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- spearmanr
model-index:
- name: bert_base_rand_5_v1_stsb
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE STSB
type: glue
args: stsb
metrics:
- name: Spearmanr
type: spearmanr
value: 0.19834161286191287
---
<!-- 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_base_rand_5_v1_stsb
This model is a fine-tuned version of [Hartunka/bert_base_rand_5_v1](https://huggingface.co/Hartunka/bert_base_rand_5_v1) on the GLUE STSB dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2682
- Pearson: 0.2151
- Spearmanr: 0.1983
- Combined Score: 0.2067
## 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: 256
- eval_batch_size: 256
- seed: 10
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr | Combined Score |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|:--------------:|
| 2.7364 | 1.0 | 23 | 2.7827 | 0.0963 | 0.0802 | 0.0883 |
| 1.8997 | 2.0 | 46 | 2.2682 | 0.2151 | 0.1983 | 0.2067 |
| 1.5738 | 3.0 | 69 | 2.3547 | 0.2811 | 0.2677 | 0.2744 |
| 1.242 | 4.0 | 92 | 2.3781 | 0.3120 | 0.3112 | 0.3116 |
| 0.8939 | 5.0 | 115 | 2.4723 | 0.3232 | 0.3164 | 0.3198 |
| 0.725 | 6.0 | 138 | 2.5860 | 0.3181 | 0.3054 | 0.3117 |
| 0.5201 | 7.0 | 161 | 2.3986 | 0.3379 | 0.3299 | 0.3339 |
### Framework versions
- Transformers 4.50.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.21.1
|