transformers pytorch bert generated_from_trainer base_model:haryoaw/scenario-TCR-data-glue-mnli-model-bert-base-uncased license:apache-2.0 endpoints_compatible region:us
---
license: apache-2.0
base_model: haryoaw/scenario-TCR-data-glue-mnli-model-bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: scenario-KD-POST-DIV4-data-glue-mnli-model-haryoaw-scenario-TCR-data-glue-mnli-m
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# scenario-KD-POST-DIV4-data-glue-mnli-model-haryoaw-scenario-TCR-data-glue-mnli-m

This model is a fine-tuned version of [haryoaw/scenario-TCR-data-glue-mnli-model-bert-base-uncased](https://huggingface.co/haryoaw/scenario-TCR-data-glue-mnli-model-bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7527
- Accuracy: 0.7638

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 3.1104        | 0.16  | 2000  | 2.7761          | 0.6853   |
| 2.615         | 0.33  | 4000  | 2.5300          | 0.7171   |
| 2.432         | 0.49  | 6000  | 2.2488          | 0.7304   |
| 2.2969        | 0.65  | 8000  | 2.1184          | 0.7380   |
| 2.1818        | 0.81  | 10000 | 2.0838          | 0.7390   |
| 2.1125        | 0.98  | 12000 | 1.9837          | 0.7445   |
| 1.6421        | 1.14  | 14000 | 1.9456          | 0.7539   |
| 1.6847        | 1.3   | 16000 | 1.9005          | 0.7529   |
| 1.672         | 1.47  | 18000 | 1.8615          | 0.7553   |
| 1.6846        | 1.63  | 20000 | 1.8330          | 0.7554   |
| 1.6341        | 1.79  | 22000 | 1.8474          | 0.7577   |
| 1.6465        | 1.96  | 24000 | 1.8208          | 0.7618   |
| 1.2981        | 2.12  | 26000 | 1.8785          | 0.7586   |
| 1.3108        | 2.28  | 28000 | 1.8680          | 0.7570   |
| 1.3152        | 2.44  | 30000 | 1.8036          | 0.7659   |
| 1.3288        | 2.61  | 32000 | 1.8413          | 0.7575   |
| 1.3362        | 2.77  | 34000 | 1.7517          | 0.7680   |
| 1.3467        | 2.93  | 36000 | 1.7864          | 0.7625   |
| 1.0676        | 3.1   | 38000 | 1.8412          | 0.7608   |
| 1.0987        | 3.26  | 40000 | 1.7695          | 0.7652   |
| 1.1233        | 3.42  | 42000 | 1.7639          | 0.7628   |
| 1.1506        | 3.59  | 44000 | 1.7332          | 0.7701   |
| 1.1535        | 3.75  | 46000 | 1.7864          | 0.7637   |
| 1.1373        | 3.91  | 48000 | 1.8016          | 0.7674   |
| 0.9515        | 4.07  | 50000 | 1.8091          | 0.7612   |
| 0.9636        | 4.24  | 52000 | 1.7662          | 0.7681   |
| 1.0085        | 4.4   | 54000 | 1.7527          | 0.7638   |


### Framework versions

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