transformers safetensors 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-MNLI-H4L2-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-MNLI-H4L2-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: 2.9541
- Accuracy: 0.6927

## 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: 45
- 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 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 5.5217        | 0.16  | 2000   | 5.2383          | 0.4358   |
| 5.0564        | 0.33  | 4000   | 4.7697          | 0.5158   |
| 4.7909        | 0.49  | 6000   | 4.4682          | 0.5386   |
| 4.5667        | 0.65  | 8000   | 4.2661          | 0.5606   |
| 4.4504        | 0.81  | 10000  | 4.1279          | 0.5834   |
| 4.3622        | 0.98  | 12000  | 4.0073          | 0.5862   |
| 4.1496        | 1.14  | 14000  | 3.9384          | 0.5883   |
| 4.1205        | 1.3   | 16000  | 3.8863          | 0.6028   |
| 4.0753        | 1.47  | 18000  | 3.8476          | 0.5949   |
| 4.0102        | 1.63  | 20000  | 3.6954          | 0.6168   |
| 4.011         | 1.79  | 22000  | 3.6803          | 0.6199   |
| 3.9602        | 1.96  | 24000  | 3.5895          | 0.6275   |
| 3.6712        | 2.12  | 26000  | 3.6489          | 0.6246   |
| 3.6312        | 2.28  | 28000  | 3.5832          | 0.6298   |
| 3.6507        | 2.44  | 30000  | 3.5108          | 0.6312   |
| 3.6036        | 2.61  | 32000  | 3.4759          | 0.6324   |
| 3.5806        | 2.77  | 34000  | 3.4954          | 0.6457   |
| 3.583         | 2.93  | 36000  | 3.3750          | 0.6403   |
| 3.2589        | 3.1   | 38000  | 3.3964          | 0.6491   |
| 3.3163        | 3.26  | 40000  | 3.4521          | 0.6381   |
| 3.2517        | 3.42  | 42000  | 3.4268          | 0.6407   |
| 3.2476        | 3.59  | 44000  | 3.3862          | 0.6521   |
| 3.2851        | 3.75  | 46000  | 3.1957          | 0.6609   |
| 3.262         | 3.91  | 48000  | 3.2215          | 0.6596   |
| 2.915         | 4.07  | 50000  | 3.2215          | 0.6652   |
| 2.9258        | 4.24  | 52000  | 3.2148          | 0.6589   |
| 2.9076        | 4.4   | 54000  | 3.1425          | 0.6639   |
| 2.9701        | 4.56  | 56000  | 3.1129          | 0.6710   |
| 2.9331        | 4.73  | 58000  | 3.1200          | 0.6714   |
| 2.8987        | 4.89  | 60000  | 3.0199          | 0.6721   |
| 2.6363        | 5.05  | 62000  | 3.0897          | 0.6710   |
| 2.6279        | 5.22  | 64000  | 3.0893          | 0.6736   |
| 2.6759        | 5.38  | 66000  | 3.0091          | 0.6766   |
| 2.6472        | 5.54  | 68000  | 3.0723          | 0.6720   |
| 2.6963        | 5.7   | 70000  | 2.9726          | 0.6778   |
| 2.6556        | 5.87  | 72000  | 3.0276          | 0.6753   |
| 2.4618        | 6.03  | 74000  | 3.0233          | 0.6760   |
| 2.3741        | 6.19  | 76000  | 2.9656          | 0.6792   |
| 2.4124        | 6.36  | 78000  | 2.9727          | 0.6807   |
| 2.4589        | 6.52  | 80000  | 2.9903          | 0.6850   |
| 2.4902        | 6.68  | 82000  | 2.9511          | 0.6848   |
| 2.4861        | 6.84  | 84000  | 2.8986          | 0.6851   |
| 2.4205        | 7.01  | 86000  | 2.9647          | 0.6836   |
| 2.236         | 7.17  | 88000  | 2.9042          | 0.6893   |
| 2.1708        | 7.33  | 90000  | 2.9062          | 0.6877   |
| 2.235         | 7.5   | 92000  | 2.9385          | 0.6911   |
| 2.2304        | 7.66  | 94000  | 2.9790          | 0.6865   |
| 2.318         | 7.82  | 96000  | 2.9358          | 0.6848   |
| 2.2881        | 7.99  | 98000  | 2.9454          | 0.6930   |
| 2.0415        | 8.15  | 100000 | 2.8835          | 0.6907   |
| 2.0617        | 8.31  | 102000 | 2.9393          | 0.6872   |
| 2.0745        | 8.47  | 104000 | 2.8947          | 0.6941   |
| 2.174         | 8.64  | 106000 | 2.8296          | 0.6901   |
| 2.1454        | 8.8   | 108000 | 2.8965          | 0.6862   |
| 2.1184        | 8.96  | 110000 | 2.8481          | 0.6897   |
| 1.87          | 9.13  | 112000 | 2.9041          | 0.6949   |
| 1.9067        | 9.29  | 114000 | 2.8885          | 0.6913   |
| 1.9391        | 9.45  | 116000 | 2.9294          | 0.6982   |
| 1.9627        | 9.62  | 118000 | 2.8398          | 0.6967   |
| 2.0086        | 9.78  | 120000 | 2.8138          | 0.6951   |
| 2.0118        | 9.94  | 122000 | 2.9220          | 0.6902   |
| 1.7596        | 10.1  | 124000 | 2.9642          | 0.6920   |
| 1.7901        | 10.27 | 126000 | 2.9541          | 0.6927   |


### Framework versions

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