transformers tensorboard safetensors bert text-classification generated_from_trainer en dataset:glue base_model:google/bert_uncased_L-2_H-256_A-4 base_model:finetune:google/bert_uncased_L-2_H-256_A-4 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
base_model: google/bert_uncased_L-2_H-256_A-4
datasets:
- glue
language:
- en
library_name: transformers
license: apache-2.0
metrics:
- spearmanr
tags:
- generated_from_trainer
model-index:
- name: bert_uncased_L-2_H-256_A-4_stsb
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: GLUE STSB
      type: glue
      args: stsb
    metrics:
    - type: spearmanr
      value: 0.8478853724287159
      name: Spearmanr
---

<!-- 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_uncased_L-2_H-256_A-4_stsb

This model is a fine-tuned version of [google/bert_uncased_L-2_H-256_A-4](https://huggingface.co/google/bert_uncased_L-2_H-256_A-4) on the GLUE STSB dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6440
- Pearson: 0.8507
- Spearmanr: 0.8479
- Combined Score: 0.8493

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|:--------------:|
| 6.8258        | 1.0   | 23   | 3.3322          | 0.1922  | 0.1781    | 0.1851         |
| 3.0892        | 2.0   | 46   | 2.2069          | 0.6152  | 0.5994    | 0.6073         |
| 2.0898        | 3.0   | 69   | 1.7703          | 0.7189  | 0.6773    | 0.6981         |
| 1.4102        | 4.0   | 92   | 1.0357          | 0.7469  | 0.7387    | 0.7428         |
| 0.984         | 5.0   | 115  | 0.9263          | 0.7887  | 0.8082    | 0.7985         |
| 0.8202        | 6.0   | 138  | 0.7993          | 0.8138  | 0.8193    | 0.8166         |
| 0.7105        | 7.0   | 161  | 0.8494          | 0.8163  | 0.8249    | 0.8206         |
| 0.6584        | 8.0   | 184  | 0.7529          | 0.8301  | 0.8352    | 0.8327         |
| 0.6204        | 9.0   | 207  | 0.7247          | 0.8368  | 0.8399    | 0.8383         |
| 0.5581        | 10.0  | 230  | 0.6988          | 0.8414  | 0.8430    | 0.8422         |
| 0.532         | 11.0  | 253  | 0.8075          | 0.8374  | 0.8422    | 0.8398         |
| 0.5051        | 12.0  | 276  | 0.7243          | 0.8421  | 0.8431    | 0.8426         |
| 0.4731        | 13.0  | 299  | 0.6760          | 0.8458  | 0.8438    | 0.8448         |
| 0.445         | 14.0  | 322  | 0.7161          | 0.8459  | 0.8452    | 0.8455         |
| 0.4192        | 15.0  | 345  | 0.6626          | 0.8489  | 0.8468    | 0.8478         |
| 0.405         | 16.0  | 368  | 0.7449          | 0.8458  | 0.8463    | 0.8460         |
| 0.3962        | 17.0  | 391  | 0.6440          | 0.8507  | 0.8479    | 0.8493         |
| 0.3683        | 18.0  | 414  | 0.6780          | 0.8490  | 0.8468    | 0.8479         |
| 0.3479        | 19.0  | 437  | 0.6729          | 0.8492  | 0.8466    | 0.8479         |
| 0.3434        | 20.0  | 460  | 0.6733          | 0.8486  | 0.8453    | 0.8470         |
| 0.3471        | 21.0  | 483  | 0.6503          | 0.8488  | 0.8450    | 0.8469         |
| 0.3221        | 22.0  | 506  | 0.6581          | 0.8497  | 0.8467    | 0.8482         |


### Framework versions

- Transformers 4.46.3
- Pytorch 2.2.1+cu118
- Datasets 2.17.0
- Tokenizers 0.20.3
