---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: multimodal-traj-class-no-numtransform
  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. -->

# multimodal-traj-class-no-numtransform

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1964
- Acc: 0.7237
- Relacc: 0.8446
- Num Fours: 617
- Mcc: 0.6029

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Acc    | Relacc | Num Fours | Mcc    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:---------:|:------:|
| 0.9461        | 1.0   | 1212  | 0.8645          | 0.6752 | 0.7586 | 680       | 0.5115 |
| 0.8387        | 2.0   | 2424  | 0.7880          | 0.6979 | 0.7889 | 630       | 0.5526 |
| 0.7565        | 3.0   | 3636  | 0.7489          | 0.7183 | 0.8015 | 636       | 0.5851 |
| 0.6997        | 4.0   | 4848  | 0.7542          | 0.7061 | 0.7908 | 574       | 0.5569 |
| 0.6516        | 5.0   | 6060  | 0.6806          | 0.7388 | 0.8192 | 660       | 0.6176 |
| 0.6049        | 6.0   | 7272  | 0.6898          | 0.7406 | 0.8395 | 638       | 0.6269 |
| 0.5526        | 7.0   | 8484  | 0.6848          | 0.7408 | 0.8413 | 648       | 0.6288 |
| 0.5343        | 8.0   | 9696  | 0.6904          | 0.7359 | 0.8413 | 645       | 0.6207 |
| 0.4855        | 9.0   | 10908 | 0.7219          | 0.7400 | 0.8456 | 587       | 0.6253 |
| 0.4618        | 10.0  | 12120 | 0.7310          | 0.7464 | 0.8448 | 624       | 0.6314 |
| 0.4326        | 11.0  | 13332 | 0.7298          | 0.7575 | 0.8508 | 658       | 0.6536 |
| 0.4098        | 12.0  | 14544 | 0.8706          | 0.7266 | 0.8395 | 611       | 0.6026 |
| 0.3707        | 13.0  | 15756 | 0.8682          | 0.7431 | 0.8415 | 629       | 0.6260 |
| 0.3377        | 14.0  | 16968 | 0.9299          | 0.7371 | 0.8467 | 590       | 0.6220 |
| 0.315         | 15.0  | 18180 | 0.9393          | 0.7365 | 0.8463 | 635       | 0.6190 |
| 0.2984        | 16.0  | 19392 | 1.0106          | 0.7348 | 0.8426 | 593       | 0.6134 |
| 0.2804        | 17.0  | 20604 | 1.0719          | 0.7307 | 0.8465 | 623       | 0.6118 |
| 0.2644        | 18.0  | 21816 | 1.1245          | 0.7280 | 0.8446 | 642       | 0.6117 |
| 0.2469        | 19.0  | 23028 | 1.1745          | 0.7258 | 0.8430 | 619       | 0.6044 |
| 0.2273        | 20.0  | 24240 | 1.1964          | 0.7237 | 0.8446 | 617       | 0.6029 |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.7.1
- Tokenizers 0.13.2