transformers tensorboard safetensors distilbert text-classification generated_from_trainer en dataset:glue base_model:gokulsrinivasagan/bert_base_lda base_model:finetune:gokulsrinivasagan/bert_base_lda model-index autotrain_compatible endpoints_compatible region:us
---
base_model: gokulsrinivasagan/bert_base_lda
datasets:
- glue
language:
- en
library_name: transformers
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: bert_base_lda_sst2
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: GLUE SST2
      type: glue
      args: sst2
    metrics:
    - type: accuracy
      value: 0.8153669724770642
      name: Accuracy
---

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# bert_base_lda_sst2

This model is a fine-tuned version of [gokulsrinivasagan/bert_base_lda](https://huggingface.co/gokulsrinivasagan/bert_base_lda) on the GLUE SST2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4223
- Accuracy: 0.8154

## 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 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3938        | 1.0   | 264  | 0.4223          | 0.8154   |
| 0.2194        | 2.0   | 528  | 0.4661          | 0.8165   |
| 0.1586        | 3.0   | 792  | 0.5292          | 0.8005   |
| 0.1183        | 4.0   | 1056 | 0.5982          | 0.8200   |
| 0.0886        | 5.0   | 1320 | 0.5992          | 0.8177   |
| 0.0702        | 6.0   | 1584 | 0.7544          | 0.8177   |


### Framework versions

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