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library_name: transformers
language:
- en
base_model: Hartunka/bert_base_km_10_v2
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- spearmanr
model-index:
- name: bert_base_km_10_v2_stsb
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE STSB
type: glue
args: stsb
metrics:
- name: Spearmanr
type: spearmanr
value: 0.19226738898357132
---
<!-- 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_km_10_v2_stsb
This model is a fine-tuned version of [Hartunka/bert_base_km_10_v2](https://huggingface.co/Hartunka/bert_base_km_10_v2) on the GLUE STSB dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2654
- Pearson: 0.2097
- Spearmanr: 0.1923
- Combined Score: 0.2010
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|:--------------:|
| 2.711 | 1.0 | 23 | 2.3545 | 0.1112 | 0.1020 | 0.1066 |
| 1.9988 | 2.0 | 46 | 2.2654 | 0.2097 | 0.1923 | 0.2010 |
| 1.8048 | 3.0 | 69 | 2.2942 | 0.2306 | 0.2124 | 0.2215 |
| 1.5374 | 4.0 | 92 | 2.5475 | 0.2700 | 0.2536 | 0.2618 |
| 1.2573 | 5.0 | 115 | 2.6120 | 0.2696 | 0.2640 | 0.2668 |
| 0.9617 | 6.0 | 138 | 2.5692 | 0.2949 | 0.2881 | 0.2915 |
| 0.7474 | 7.0 | 161 | 2.6657 | 0.3060 | 0.3096 | 0.3078 |
### Framework versions
- Transformers 4.50.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.21.1
|