---
library_name: transformers
language:
- en
base_model: gokulsrinivasagan/bert_base_lda_20_v1_book
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- spearmanr
model-index:
- name: bert_base_lda_20_v1_book_stsb
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: GLUE STSB
      type: glue
      args: stsb
    metrics:
    - name: Spearmanr
      type: spearmanr
      value: 0.8381999392722225
---

<!-- 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_lda_20_v1_book_stsb

This model is a fine-tuned version of [gokulsrinivasagan/bert_base_lda_20_v1_book](https://huggingface.co/gokulsrinivasagan/bert_base_lda_20_v1_book) on the GLUE STSB dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6650
- Pearson: 0.8407
- Spearmanr: 0.8382
- Combined Score: 0.8394

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|:--------------:|
| 2.8738        | 1.0   | 23   | 2.4670          | 0.1765  | 0.1748    | 0.1756         |
| 1.4719        | 2.0   | 46   | 1.0280          | 0.7397  | 0.7404    | 0.7401         |
| 0.9801        | 3.0   | 69   | 0.8276          | 0.7956  | 0.7954    | 0.7955         |
| 0.783         | 4.0   | 92   | 0.7431          | 0.8197  | 0.8193    | 0.8195         |
| 0.5677        | 5.0   | 115  | 0.9075          | 0.8135  | 0.8152    | 0.8144         |
| 0.4407        | 6.0   | 138  | 0.7474          | 0.8267  | 0.8272    | 0.8269         |
| 0.3821        | 7.0   | 161  | 0.6753          | 0.8391  | 0.8371    | 0.8381         |
| 0.3036        | 8.0   | 184  | 0.8726          | 0.8246  | 0.8260    | 0.8253         |
| 0.269         | 9.0   | 207  | 0.7331          | 0.8311  | 0.8293    | 0.8302         |
| 0.2191        | 10.0  | 230  | 0.7562          | 0.8383  | 0.8368    | 0.8375         |
| 0.1854        | 11.0  | 253  | 0.7022          | 0.8365  | 0.8343    | 0.8354         |
| 0.1718        | 12.0  | 276  | 0.6650          | 0.8407  | 0.8382    | 0.8394         |
| 0.1685        | 13.0  | 299  | 0.7270          | 0.8350  | 0.8333    | 0.8342         |
| 0.1368        | 14.0  | 322  | 0.7532          | 0.8392  | 0.8376    | 0.8384         |
| 0.1351        | 15.0  | 345  | 0.8710          | 0.8379  | 0.8379    | 0.8379         |
| 0.1459        | 16.0  | 368  | 0.7801          | 0.8416  | 0.8398    | 0.8407         |
| 0.106         | 17.0  | 391  | 0.6833          | 0.8393  | 0.8380    | 0.8387         |


### Framework versions

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