transformers pytorch 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-mnli-model-bert-base-uncased-run-1
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# scenario-KD-SCR-DIV2-data-glue-mnli-model-bert-base-uncased-run-1

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: 1.3510
- Accuracy: 0.7965

## 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: 42
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 2.689         | 0.16  | 2000  | 2.1808          | 0.7318   |
| 2.224         | 0.33  | 4000  | 1.9613          | 0.7586   |
| 2.0581        | 0.49  | 6000  | 1.7721          | 0.7651   |
| 1.9077        | 0.65  | 8000  | 1.6097          | 0.7775   |
| 1.8189        | 0.81  | 10000 | 1.5288          | 0.7800   |
| 1.7489        | 0.98  | 12000 | 1.5245          | 0.7800   |
| 1.3089        | 1.14  | 14000 | 1.4540          | 0.7833   |
| 1.3294        | 1.3   | 16000 | 1.4324          | 0.7845   |
| 1.3695        | 1.47  | 18000 | 1.4375          | 0.7893   |
| 1.3319        | 1.63  | 20000 | 1.4221          | 0.7890   |
| 1.3231        | 1.79  | 22000 | 1.3823          | 0.7936   |
| 1.3428        | 1.96  | 24000 | 1.4028          | 0.7958   |
| 1.0358        | 2.12  | 26000 | 1.3798          | 0.7969   |
| 1.0403        | 2.28  | 28000 | 1.3799          | 0.7940   |
| 1.0429        | 2.44  | 30000 | 1.3625          | 0.7994   |
| 1.0674        | 2.61  | 32000 | 1.4389          | 0.7946   |
| 1.0835        | 2.77  | 34000 | 1.3201          | 0.8016   |
| 1.0814        | 2.93  | 36000 | 1.3406          | 0.7979   |
| 0.8509        | 3.1   | 38000 | 1.3601          | 0.7969   |
| 0.8795        | 3.26  | 40000 | 1.3031          | 0.7972   |
| 0.915         | 3.42  | 42000 | 1.3104          | 0.7995   |
| 0.9138        | 3.59  | 44000 | 1.3510          | 0.7965   |


### Framework versions

- Transformers 4.33.3
- Pytorch 2.1.1
- Datasets 2.14.5
- Tokenizers 0.13.3
