---
library_name: transformers
language:
- en
base_model: gokulsrinivasagan/distilbert_lda_100_v1
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: distilbert_lda_100_v1_qnli
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: GLUE QNLI
      type: glue
      args: qnli
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8224418817499543
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# distilbert_lda_100_v1_qnli

This model is a fine-tuned version of [gokulsrinivasagan/distilbert_lda_100_v1](https://huggingface.co/gokulsrinivasagan/distilbert_lda_100_v1) on the GLUE QNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3980
- Accuracy: 0.8224

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5562        | 1.0   | 410  | 0.4807          | 0.7770   |
| 0.4173        | 2.0   | 820  | 0.3980          | 0.8224   |
| 0.3262        | 3.0   | 1230 | 0.4235          | 0.8184   |
| 0.243         | 4.0   | 1640 | 0.4810          | 0.8155   |
| 0.1721        | 5.0   | 2050 | 0.5393          | 0.8060   |
| 0.1225        | 6.0   | 2460 | 0.6459          | 0.8129   |
| 0.0914        | 7.0   | 2870 | 0.6759          | 0.8151   |


### Framework versions

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