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library_name: transformers
language:
- en
base_model: Hartunka/bert_base_rand_5_v2
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- spearmanr
model-index:
- name: bert_base_rand_5_v2_stsb
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE STSB
type: glue
args: stsb
metrics:
- name: Spearmanr
type: spearmanr
value: 0.16189439146929932
---
<!-- 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_v2_stsb
This model is a fine-tuned version of [Hartunka/bert_base_rand_5_v2](https://huggingface.co/Hartunka/bert_base_rand_5_v2) on the GLUE STSB dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3163
- Pearson: 0.1954
- Spearmanr: 0.1619
- Combined Score: 0.1787
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|:--------------:|
| 2.6563 | 1.0 | 23 | 2.4689 | 0.0989 | 0.0822 | 0.0905 |
| 1.8843 | 2.0 | 46 | 2.3163 | 0.1954 | 0.1619 | 0.1787 |
| 1.6315 | 3.0 | 69 | 2.3652 | 0.2604 | 0.2509 | 0.2557 |
| 1.2245 | 4.0 | 92 | 2.5702 | 0.2911 | 0.2862 | 0.2886 |
| 0.8986 | 5.0 | 115 | 2.6825 | 0.2767 | 0.2655 | 0.2711 |
| 0.6617 | 6.0 | 138 | 2.5122 | 0.3121 | 0.3040 | 0.3081 |
| 0.5165 | 7.0 | 161 | 2.3745 | 0.3379 | 0.3323 | 0.3351 |
### Framework versions
- Transformers 4.50.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.21.1
|