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-DIV2-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-DIV2-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: 1.2222
- Accuracy: 0.8052

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.6969        | 0.16  | 2000  | 1.7370          | 0.7638   |
| 1.5189        | 0.33  | 4000  | 1.5735          | 0.7754   |
| 1.3996        | 0.49  | 6000  | 1.4475          | 0.7851   |
| 1.3876        | 0.65  | 8000  | 1.4315          | 0.7817   |
| 1.3136        | 0.81  | 10000 | 1.3642          | 0.7891   |
| 1.3305        | 0.98  | 12000 | 1.3596          | 0.7955   |
| 1.0063        | 1.14  | 14000 | 1.3433          | 0.7942   |
| 1.0225        | 1.3   | 16000 | 1.3626          | 0.7899   |
| 1.0249        | 1.47  | 18000 | 1.2934          | 0.7959   |
| 1.0575        | 1.63  | 20000 | 1.3055          | 0.7961   |
| 1.0773        | 1.79  | 22000 | 1.2968          | 0.7971   |
| 1.0523        | 1.96  | 24000 | 1.2978          | 0.7995   |
| 0.8393        | 2.12  | 26000 | 1.2853          | 0.7976   |
| 0.8503        | 2.28  | 28000 | 1.2624          | 0.8001   |
| 0.8692        | 2.44  | 30000 | 1.2787          | 0.7977   |
| 0.8801        | 2.61  | 32000 | 1.2443          | 0.8015   |
| 0.887         | 2.77  | 34000 | 1.2604          | 0.8008   |
| 0.8838        | 2.93  | 36000 | 1.3103          | 0.8034   |
| 0.7475        | 3.1   | 38000 | 1.2502          | 0.8044   |
| 0.7626        | 3.26  | 40000 | 1.2423          | 0.8014   |
| 0.7658        | 3.42  | 42000 | 1.2681          | 0.7995   |
| 0.7729        | 3.59  | 44000 | 1.2334          | 0.7994   |
| 0.7867        | 3.75  | 46000 | 1.2286          | 0.8012   |
| 0.7908        | 3.91  | 48000 | 1.2434          | 0.8069   |
| 0.6994        | 4.07  | 50000 | 1.1957          | 0.8082   |
| 0.6982        | 4.24  | 52000 | 1.1950          | 0.8061   |
| 0.7061        | 4.4   | 54000 | 1.2441          | 0.8016   |
| 0.7057        | 4.56  | 56000 | 1.2590          | 0.8023   |
| 0.7088        | 4.73  | 58000 | 1.2354          | 0.8072   |
| 0.729         | 4.89  | 60000 | 1.2222          | 0.8052   |


### Framework versions

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