transformers tensorboard safetensors distilbert text-classification generated_from_trainer en dataset:glue base_model:gokulsrinivasagan/bert_base_lda_5 base_model:finetune:gokulsrinivasagan/bert_base_lda_5 model-index autotrain_compatible endpoints_compatible region:us
---
base_model: gokulsrinivasagan/bert_base_lda_5
datasets:
- glue
language:
- en
library_name: transformers
metrics:
- accuracy
- f1
tags:
- generated_from_trainer
model-index:
- name: bert_base_lda_5_mrpc
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: GLUE MRPC
      type: glue
      args: mrpc
    metrics:
    - type: accuracy
      value: 0.6838235294117647
      name: Accuracy
    - type: f1
      value: 0.8122270742358079
      name: F1
---

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# bert_base_lda_5_mrpc

This model is a fine-tuned version of [gokulsrinivasagan/bert_base_lda_5](https://huggingface.co/gokulsrinivasagan/bert_base_lda_5) on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6226
- Accuracy: 0.6838
- F1: 0.8122
- Combined Score: 0.7480

## 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: 0.001
- 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: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Combined Score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:|
| 1.6067        | 1.0   | 15   | 0.6951          | 0.6838   | 0.8122 | 0.7480         |
| 0.6503        | 2.0   | 30   | 0.6270          | 0.6838   | 0.8122 | 0.7480         |
| 0.6289        | 3.0   | 45   | 0.6279          | 0.6838   | 0.8122 | 0.7480         |
| 0.6364        | 4.0   | 60   | 0.6244          | 0.6838   | 0.8122 | 0.7480         |
| 0.6368        | 5.0   | 75   | 0.6272          | 0.6838   | 0.8122 | 0.7480         |
| 0.6349        | 6.0   | 90   | 0.6252          | 0.6838   | 0.8122 | 0.7480         |
| 0.6311        | 7.0   | 105  | 0.6263          | 0.6838   | 0.8122 | 0.7480         |
| 0.6339        | 8.0   | 120  | 0.6240          | 0.6838   | 0.8122 | 0.7480         |
| 0.6296        | 9.0   | 135  | 0.6287          | 0.6838   | 0.8122 | 0.7480         |
| 0.6346        | 10.0  | 150  | 0.6260          | 0.6838   | 0.8122 | 0.7480         |
| 0.6359        | 11.0  | 165  | 0.6275          | 0.6838   | 0.8122 | 0.7480         |
| 0.6337        | 12.0  | 180  | 0.6242          | 0.6838   | 0.8122 | 0.7480         |
| 0.6302        | 13.0  | 195  | 0.6228          | 0.6838   | 0.8122 | 0.7480         |
| 0.6351        | 14.0  | 210  | 0.6258          | 0.6838   | 0.8122 | 0.7480         |
| 0.6324        | 15.0  | 225  | 0.6226          | 0.6838   | 0.8122 | 0.7480         |
| 0.6301        | 16.0  | 240  | 0.6243          | 0.6838   | 0.8122 | 0.7480         |
| 0.6316        | 17.0  | 255  | 0.6248          | 0.6838   | 0.8122 | 0.7480         |
| 0.63          | 18.0  | 270  | 0.6243          | 0.6838   | 0.8122 | 0.7480         |
| 0.6304        | 19.0  | 285  | 0.6230          | 0.6838   | 0.8122 | 0.7480         |
| 0.633         | 20.0  | 300  | 0.6250          | 0.6838   | 0.8122 | 0.7480         |


### Framework versions

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