transformers pytorch safetensors bert generated_from_trainer base_model:bert-base-uncased license:apache-2.0 endpoints_compatible region:us
---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: scenario-KD-SCR-DIV2-data-glue-qnli-model-bert-base-uncased-run-2
  results: []
---

<!-- 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. -->

# scenario-KD-SCR-DIV2-data-glue-qnli-model-bert-base-uncased-run-2

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6627
- Accuracy: 0.8658

## 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: 16
- eval_batch_size: 32
- seed: 43
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6969

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 2.6772        | 1.0   | 3273  | 1.9188          | 0.8415   |
| 1.6501        | 2.0   | 6547  | 1.6757          | 0.8596   |
| 1.1013        | 3.0   | 9820  | 1.6072          | 0.8589   |
| 0.8484        | 4.0   | 13094 | 1.5527          | 0.8629   |
| 0.7604        | 5.0   | 16367 | 1.5537          | 0.8638   |
| 0.6704        | 6.0   | 19641 | 1.5518          | 0.8611   |
| 0.5803        | 7.0   | 22914 | 1.5026          | 0.8655   |
| 0.5558        | 8.0   | 26188 | 1.5486          | 0.8631   |
| 0.5164        | 9.0   | 29461 | 1.6188          | 0.8572   |
| 0.4841        | 10.0  | 32735 | 1.6078          | 0.8655   |
| 0.4536        | 11.0  | 36008 | 1.6251          | 0.8658   |
| 0.4242        | 12.0  | 39282 | 1.5440          | 0.8655   |
| 0.4022        | 13.0  | 42555 | 1.4659          | 0.8695   |
| 0.3788        | 14.0  | 45829 | 1.7356          | 0.8631   |
| 0.3726        | 15.0  | 49102 | 1.6443          | 0.8649   |
| 0.3432        | 16.0  | 52376 | 1.5632          | 0.8682   |
| 0.3356        | 17.0  | 55649 | 1.5614          | 0.8710   |
| 0.3236        | 18.0  | 58923 | 1.6171          | 0.8730   |
| 0.3154        | 19.0  | 62196 | 1.6974          | 0.8671   |
| 0.314         | 20.0  | 65470 | 1.7042          | 0.8656   |
| 0.3002        | 21.0  | 68743 | 1.7116          | 0.8662   |
| 0.3058        | 22.0  | 72017 | 1.6969          | 0.8715   |
| 0.2956        | 23.0  | 75290 | 1.6627          | 0.8658   |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.2
- Datasets 2.16.0
- Tokenizers 0.15.0
