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-H2L1-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-MNLI-H2L1-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: 2.3275
- Accuracy: 0.7257

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 5.3052        | 0.16  | 2000  | 4.7985          | 0.4953   |
| 4.7358        | 0.33  | 4000  | 4.3423          | 0.5651   |
| 4.3763        | 0.49  | 6000  | 4.0494          | 0.5808   |
| 3.9772        | 0.65  | 8000  | 3.6466          | 0.6328   |
| 3.7004        | 0.81  | 10000 | 3.3615          | 0.6490   |
| 3.4578        | 0.98  | 12000 | 3.1435          | 0.6589   |
| 3.0901        | 1.14  | 14000 | 2.9697          | 0.6710   |
| 3.0178        | 1.3   | 16000 | 2.9240          | 0.6787   |
| 2.9696        | 1.47  | 18000 | 2.8070          | 0.6745   |
| 2.8587        | 1.63  | 20000 | 2.6771          | 0.6905   |
| 2.8202        | 1.79  | 22000 | 2.5974          | 0.7030   |
| 2.755         | 1.96  | 24000 | 2.5676          | 0.7019   |
| 2.344         | 2.12  | 26000 | 2.5321          | 0.7105   |
| 2.3242        | 2.28  | 28000 | 2.6221          | 0.7027   |
| 2.3145        | 2.44  | 30000 | 2.5341          | 0.7062   |
| 2.3716        | 2.61  | 32000 | 2.4859          | 0.7160   |
| 2.3383        | 2.77  | 34000 | 2.4158          | 0.7157   |
| 2.3425        | 2.93  | 36000 | 2.3786          | 0.7246   |
| 1.9345        | 3.1   | 38000 | 2.4474          | 0.7178   |
| 1.9601        | 3.26  | 40000 | 2.4602          | 0.7165   |
| 2.0055        | 3.42  | 42000 | 2.4129          | 0.7181   |
| 1.9737        | 3.59  | 44000 | 2.4837          | 0.7165   |
| 2.0263        | 3.75  | 46000 | 2.3240          | 0.7276   |
| 2.0032        | 3.91  | 48000 | 2.3451          | 0.7298   |
| 1.6451        | 4.07  | 50000 | 2.4157          | 0.7242   |
| 1.7155        | 4.24  | 52000 | 2.4108          | 0.7252   |
| 1.7176        | 4.4   | 54000 | 2.4396          | 0.7210   |
| 1.7442        | 4.56  | 56000 | 2.4788          | 0.7260   |
| 1.7717        | 4.73  | 58000 | 2.3275          | 0.7257   |


### Framework versions

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