transformers tensorboard safetensors distilbert text-classification generated_from_trainer en dataset:glue base_model:gokulsrinivasagan/bert_base_lda_50_v1_book base_model:finetune:gokulsrinivasagan/bert_base_lda_50_v1_book model-index autotrain_compatible endpoints_compatible region:us
---
base_model: gokulsrinivasagan/bert_base_lda_50_v1_book
datasets:
- glue
language:
- en
library_name: transformers
metrics:
- spearmanr
tags:
- generated_from_trainer
model-index:
- name: bert_base_lda_50_v1_book_stsb
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: GLUE STSB
      type: glue
      args: stsb
    metrics:
    - type: spearmanr
      value: 0.8202760178820929
      name: Spearmanr
---

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# bert_base_lda_50_v1_book_stsb

This model is a fine-tuned version of [gokulsrinivasagan/bert_base_lda_50_v1_book](https://huggingface.co/gokulsrinivasagan/bert_base_lda_50_v1_book) on the GLUE STSB dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7425
- Pearson: 0.8226
- Spearmanr: 0.8203
- Combined Score: 0.8214

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|:--------------:|
| 2.997         | 1.0   | 23   | 2.6848          | 0.1958  | 0.1766    | 0.1862         |
| 1.6461        | 2.0   | 46   | 1.9298          | 0.6674  | 0.7129    | 0.6902         |
| 1.1009        | 3.0   | 69   | 1.1766          | 0.7428  | 0.7664    | 0.7546         |
| 0.7079        | 4.0   | 92   | 0.8637          | 0.7978  | 0.7957    | 0.7967         |
| 0.5176        | 5.0   | 115  | 0.8868          | 0.8157  | 0.8217    | 0.8187         |
| 0.3986        | 6.0   | 138  | 0.7433          | 0.8226  | 0.8206    | 0.8216         |
| 0.2938        | 7.0   | 161  | 0.7853          | 0.8187  | 0.8186    | 0.8186         |
| 0.2487        | 8.0   | 184  | 0.7425          | 0.8226  | 0.8203    | 0.8214         |
| 0.2102        | 9.0   | 207  | 1.0009          | 0.8075  | 0.8090    | 0.8082         |
| 0.2023        | 10.0  | 230  | 0.7501          | 0.8264  | 0.8247    | 0.8256         |
| 0.2119        | 11.0  | 253  | 0.8895          | 0.8213  | 0.8214    | 0.8213         |
| 0.1595        | 12.0  | 276  | 1.1409          | 0.8174  | 0.8251    | 0.8212         |
| 0.1493        | 13.0  | 299  | 0.7717          | 0.8197  | 0.8174    | 0.8185         |


### Framework versions

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