transformers tensorboard safetensors distilbert text-classification generated_from_trainer en dataset:glue base_model:gokulsrinivasagan/bert_base_lda_100_v1_book base_model:finetune:gokulsrinivasagan/bert_base_lda_100_v1_book model-index autotrain_compatible endpoints_compatible region:us
---
base_model: gokulsrinivasagan/bert_base_lda_100_v1_book
datasets:
- glue
language:
- en
library_name: transformers
metrics:
- spearmanr
tags:
- generated_from_trainer
model-index:
- name: bert_base_lda_100_v1_book_stsb
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: GLUE STSB
      type: glue
      args: stsb
    metrics:
    - type: spearmanr
      value: 0.8140948769697594
      name: Spearmanr
---

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# bert_base_lda_100_v1_book_stsb

This model is a fine-tuned version of [gokulsrinivasagan/bert_base_lda_100_v1_book](https://huggingface.co/gokulsrinivasagan/bert_base_lda_100_v1_book) on the GLUE STSB dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8007
- Pearson: 0.8114
- Spearmanr: 0.8141
- Combined Score: 0.8127

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|:--------------:|
| 3.6355        | 1.0   | 23   | 2.1916          | 0.2053  | 0.1963    | 0.2008         |
| 1.8578        | 2.0   | 46   | 1.3375          | 0.6783  | 0.6915    | 0.6849         |
| 1.1462        | 3.0   | 69   | 1.0175          | 0.7528  | 0.7571    | 0.7549         |
| 0.9851        | 4.0   | 92   | 1.2085          | 0.7574  | 0.7780    | 0.7677         |
| 0.8176        | 5.0   | 115  | 0.9462          | 0.7739  | 0.7839    | 0.7789         |
| 0.7319        | 6.0   | 138  | 1.0574          | 0.7453  | 0.7638    | 0.7546         |
| 0.5651        | 7.0   | 161  | 0.9378          | 0.8020  | 0.8111    | 0.8065         |
| 0.6897        | 8.0   | 184  | 1.1253          | 0.7927  | 0.8145    | 0.8036         |
| 0.4487        | 9.0   | 207  | 0.8598          | 0.8012  | 0.8052    | 0.8032         |
| 0.3729        | 10.0  | 230  | 0.8007          | 0.8114  | 0.8141    | 0.8127         |
| 0.3205        | 11.0  | 253  | 0.8871          | 0.8023  | 0.8054    | 0.8039         |
| 0.3007        | 12.0  | 276  | 1.0573          | 0.8073  | 0.8134    | 0.8104         |
| 0.2649        | 13.0  | 299  | 1.0932          | 0.8067  | 0.8085    | 0.8076         |
| 0.2537        | 14.0  | 322  | 0.8145          | 0.8148  | 0.8153    | 0.8151         |
| 0.1929        | 15.0  | 345  | 0.8869          | 0.8165  | 0.8171    | 0.8168         |


### Framework versions

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