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-H4L1-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-H4L1-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: 2.8181
- Accuracy: 0.6990

## 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 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 5.5033        | 0.16  | 2000   | 5.2245          | 0.4291   |
| 5.132         | 0.33  | 4000   | 5.0139          | 0.5063   |
| 4.8395        | 0.49  | 6000   | 4.4954          | 0.5391   |
| 4.5922        | 0.65  | 8000   | 4.2858          | 0.5622   |
| 4.4713        | 0.81  | 10000  | 4.1399          | 0.5809   |
| 4.3701        | 0.98  | 12000  | 4.0314          | 0.5894   |
| 4.1704        | 1.14  | 14000  | 3.9298          | 0.5956   |
| 4.1305        | 1.3   | 16000  | 3.8552          | 0.6020   |
| 4.0818        | 1.47  | 18000  | 3.9001          | 0.5881   |
| 4.0364        | 1.63  | 20000  | 3.7377          | 0.6147   |
| 4.0159        | 1.79  | 22000  | 3.7425          | 0.6191   |
| 3.9678        | 1.96  | 24000  | 3.6362          | 0.6209   |
| 3.6678        | 2.12  | 26000  | 3.5925          | 0.6328   |
| 3.6058        | 2.28  | 28000  | 3.6473          | 0.6240   |
| 3.6286        | 2.44  | 30000  | 3.5882          | 0.6291   |
| 3.6163        | 2.61  | 32000  | 3.5909          | 0.6244   |
| 3.5708        | 2.77  | 34000  | 3.4142          | 0.6378   |
| 3.5973        | 2.93  | 36000  | 3.3857          | 0.6432   |
| 3.2388        | 3.1   | 38000  | 3.3720          | 0.6448   |
| 3.3045        | 3.26  | 40000  | 3.4333          | 0.6367   |
| 3.2319        | 3.42  | 42000  | 3.3239          | 0.6444   |
| 3.2522        | 3.59  | 44000  | 3.3811          | 0.6440   |
| 3.266         | 3.75  | 46000  | 3.1958          | 0.6522   |
| 3.1998        | 3.91  | 48000  | 3.2171          | 0.6604   |
| 2.866         | 4.07  | 50000  | 3.1041          | 0.6650   |
| 2.8707        | 4.24  | 52000  | 3.2015          | 0.6502   |
| 2.8507        | 4.4   | 54000  | 3.0880          | 0.6566   |
| 2.9263        | 4.56  | 56000  | 3.0881          | 0.6662   |
| 2.8661        | 4.73  | 58000  | 2.9711          | 0.6747   |
| 2.8413        | 4.89  | 60000  | 2.9298          | 0.6772   |
| 2.5462        | 5.05  | 62000  | 3.0301          | 0.6722   |
| 2.5729        | 5.22  | 64000  | 3.0389          | 0.6773   |
| 2.5682        | 5.38  | 66000  | 2.9416          | 0.6745   |
| 2.5741        | 5.54  | 68000  | 2.8951          | 0.6778   |
| 2.6033        | 5.7   | 70000  | 2.8394          | 0.6796   |
| 2.5816        | 5.87  | 72000  | 2.8807          | 0.6805   |
| 2.3758        | 6.03  | 74000  | 2.9582          | 0.6769   |
| 2.3716        | 6.19  | 76000  | 2.8386          | 0.6871   |
| 2.3236        | 6.36  | 78000  | 2.8773          | 0.6898   |
| 2.4192        | 6.52  | 80000  | 2.8291          | 0.6844   |
| 2.4089        | 6.68  | 82000  | 2.8078          | 0.6881   |
| 2.3782        | 6.84  | 84000  | 2.7847          | 0.6909   |
| 2.3476        | 7.01  | 86000  | 2.8770          | 0.6902   |
| 2.1167        | 7.17  | 88000  | 2.8360          | 0.6919   |
| 2.1065        | 7.33  | 90000  | 2.7724          | 0.6945   |
| 2.15          | 7.5   | 92000  | 2.7830          | 0.6951   |
| 2.1602        | 7.66  | 94000  | 2.8025          | 0.6974   |
| 2.25          | 7.82  | 96000  | 2.7367          | 0.6958   |
| 2.2181        | 7.99  | 98000  | 2.7419          | 0.6982   |
| 1.9671        | 8.15  | 100000 | 2.7299          | 0.6914   |
| 1.9762        | 8.31  | 102000 | 2.8413          | 0.6979   |
| 2.0095        | 8.47  | 104000 | 2.7720          | 0.6976   |
| 2.0731        | 8.64  | 106000 | 2.7915          | 0.6955   |
| 2.0609        | 8.8   | 108000 | 2.6870          | 0.7012   |
| 2.0432        | 8.96  | 110000 | 2.7679          | 0.6928   |
| 1.7805        | 9.13  | 112000 | 2.8114          | 0.6994   |
| 1.8042        | 9.29  | 114000 | 2.7839          | 0.6989   |
| 1.8261        | 9.45  | 116000 | 2.7324          | 0.7025   |
| 1.8783        | 9.62  | 118000 | 2.7665          | 0.6948   |
| 1.9042        | 9.78  | 120000 | 2.7078          | 0.7013   |
| 1.9314        | 9.94  | 122000 | 2.8003          | 0.7012   |
| 1.7013        | 10.1  | 124000 | 2.8941          | 0.6967   |
| 1.7274        | 10.27 | 126000 | 2.8181          | 0.6990   |


### Framework versions

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