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-DIV3-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
should probably proofread and complete it, then remove this comment. -->

# scenario-KD-POST-DIV3-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: 1.5447
- Accuracy: 0.7798

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 2.4724        | 0.16  | 2000  | 2.2609          | 0.7261   |
| 2.0806        | 0.33  | 4000  | 2.0433          | 0.7469   |
| 1.9981        | 0.49  | 6000  | 1.8230          | 0.7606   |
| 1.8622        | 0.65  | 8000  | 1.7700          | 0.7640   |
| 1.7987        | 0.81  | 10000 | 1.7786          | 0.7632   |
| 1.7268        | 0.98  | 12000 | 1.6559          | 0.7698   |
| 1.3401        | 1.14  | 14000 | 1.6165          | 0.7728   |
| 1.3832        | 1.3   | 16000 | 1.6057          | 0.7729   |
| 1.377         | 1.47  | 18000 | 1.6249          | 0.7730   |
| 1.3801        | 1.63  | 20000 | 1.5751          | 0.7763   |
| 1.3535        | 1.79  | 22000 | 1.6194          | 0.7766   |
| 1.3343        | 1.96  | 24000 | 1.5936          | 0.7852   |
| 1.0764        | 2.12  | 26000 | 1.5828          | 0.7795   |
| 1.0865        | 2.28  | 28000 | 1.5491          | 0.7827   |
| 1.0718        | 2.44  | 30000 | 1.5610          | 0.7801   |
| 1.1151        | 2.61  | 32000 | 1.5856          | 0.7797   |
| 1.1079        | 2.77  | 34000 | 1.5447          | 0.7798   |


### Framework versions

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