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-DIV2-100000-data-glue-mnli-model-haryoaw-scenario-TCR-data-glue
  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-DIV2-100000-data-glue-mnli-model-haryoaw-scenario-TCR-data-glue

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: 1.3440
- Accuracy: 0.7921

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.7052        | 0.64  | 2000  | 1.7251          | 0.7618   |
| 1.1119        | 1.28  | 4000  | 1.5939          | 0.7744   |
| 1.1098        | 1.92  | 6000  | 1.5432          | 0.7851   |
| 0.8796        | 2.56  | 8000  | 1.4874          | 0.7823   |
| 0.7647        | 3.2   | 10000 | 1.5026          | 0.7781   |
| 0.7763        | 3.84  | 12000 | 1.4539          | 0.7872   |
| 0.7028        | 4.48  | 14000 | 1.4504          | 0.7836   |
| 0.6614        | 5.12  | 16000 | 1.4219          | 0.7856   |
| 0.6551        | 5.76  | 18000 | 1.4745          | 0.7891   |
| 0.6074        | 6.4   | 20000 | 1.3824          | 0.7870   |
| 0.6031        | 7.04  | 22000 | 1.3839          | 0.7900   |
| 0.5735        | 7.68  | 24000 | 1.3769          | 0.7863   |
| 0.5522        | 8.32  | 26000 | 1.4054          | 0.7916   |
| 0.5455        | 8.96  | 28000 | 1.3697          | 0.7942   |
| 0.5214        | 9.6   | 30000 | 1.3734          | 0.7894   |
| 0.5061        | 10.24 | 32000 | 1.3534          | 0.7927   |
| 0.51          | 10.88 | 34000 | 1.3752          | 0.7923   |
| 0.488         | 11.52 | 36000 | 1.3517          | 0.7972   |
| 0.4764        | 12.16 | 38000 | 1.3314          | 0.7943   |
| 0.4834        | 12.8  | 40000 | 1.3344          | 0.7934   |
| 0.4687        | 13.44 | 42000 | 1.3586          | 0.7954   |
| 0.4613        | 14.08 | 44000 | 1.3586          | 0.7921   |
| 0.4586        | 14.72 | 46000 | 1.3440          | 0.7921   |


### Framework versions

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