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_rte
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: GLUE RTE
      type: glue
      args: rte
    metrics:
    - type: accuracy
      value: 0.5306859205776173
      name: Accuracy
---

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# bert_base_lda_rte

This model is a fine-tuned version of [gokulsrinivasagan/bert_base_lda](https://huggingface.co/gokulsrinivasagan/bert_base_lda) on the GLUE RTE dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6939
- Accuracy: 0.5307

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7419        | 1.0   | 10   | 0.7051          | 0.5307   |
| 0.6966        | 2.0   | 20   | 0.6939          | 0.5307   |
| 0.6539        | 3.0   | 30   | 0.7159          | 0.4838   |
| 0.5729        | 4.0   | 40   | 0.7840          | 0.4729   |
| 0.4442        | 5.0   | 50   | 0.9599          | 0.5090   |
| 0.3185        | 6.0   | 60   | 1.1472          | 0.5235   |
| 0.1937        | 7.0   | 70   | 1.3855          | 0.5199   |


### Framework versions

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