---
license: mit
base_model: haryoaw/scenario-MDBT-TCR_data-cardiffnlp_tweet_sentiment_multilingual_all
tags:
- generated_from_trainer
datasets:
- tweet_sentiment_multilingual
metrics:
- accuracy
- f1
model-index:
- name: scenario-KD-SCR-CDF-ALL-D2_data-cardiffnlp_tweet_sentiment_multilingual_all55
  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-SCR-CDF-ALL-D2_data-cardiffnlp_tweet_sentiment_multilingual_all55

This model is a fine-tuned version of [haryoaw/scenario-MDBT-TCR_data-cardiffnlp_tweet_sentiment_multilingual_all](https://huggingface.co/haryoaw/scenario-MDBT-TCR_data-cardiffnlp_tweet_sentiment_multilingual_all) on the tweet_sentiment_multilingual dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Accuracy: 0.3333
- F1: 0.1667

## 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: 55
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 1.0358        | 1.09  | 500   | nan             | 0.3333   | 0.1667 |
| 0.0           | 2.17  | 1000  | nan             | 0.3333   | 0.1667 |
| 0.0           | 3.26  | 1500  | nan             | 0.3333   | 0.1667 |
| 0.0           | 4.35  | 2000  | nan             | 0.3333   | 0.1667 |
| 0.0           | 5.43  | 2500  | nan             | 0.3333   | 0.1667 |
| 0.0           | 6.52  | 3000  | nan             | 0.3333   | 0.1667 |
| 0.0           | 7.61  | 3500  | nan             | 0.3333   | 0.1667 |
| 0.0           | 8.7   | 4000  | nan             | 0.3333   | 0.1667 |
| 0.0           | 9.78  | 4500  | nan             | 0.3333   | 0.1667 |
| 0.0           | 10.87 | 5000  | nan             | 0.3333   | 0.1667 |
| 0.0           | 11.96 | 5500  | nan             | 0.3333   | 0.1667 |
| 0.0           | 13.04 | 6000  | nan             | 0.3333   | 0.1667 |
| 0.0           | 14.13 | 6500  | nan             | 0.3333   | 0.1667 |
| 0.0           | 15.22 | 7000  | nan             | 0.3333   | 0.1667 |
| 0.0           | 16.3  | 7500  | nan             | 0.3333   | 0.1667 |
| 0.0           | 17.39 | 8000  | nan             | 0.3333   | 0.1667 |
| 0.0           | 18.48 | 8500  | nan             | 0.3333   | 0.1667 |
| 0.0           | 19.57 | 9000  | nan             | 0.3333   | 0.1667 |
| 0.0           | 20.65 | 9500  | nan             | 0.3333   | 0.1667 |
| 0.0           | 21.74 | 10000 | nan             | 0.3333   | 0.1667 |
| 0.0           | 22.83 | 10500 | nan             | 0.3333   | 0.1667 |
| 0.0           | 23.91 | 11000 | nan             | 0.3333   | 0.1667 |
| 0.0           | 25.0  | 11500 | nan             | 0.3333   | 0.1667 |
| 0.0           | 26.09 | 12000 | nan             | 0.3333   | 0.1667 |
| 0.0           | 27.17 | 12500 | nan             | 0.3333   | 0.1667 |
| 0.0           | 28.26 | 13000 | nan             | 0.3333   | 0.1667 |
| 0.0           | 29.35 | 13500 | nan             | 0.3333   | 0.1667 |
| 0.0           | 30.43 | 14000 | nan             | 0.3333   | 0.1667 |
| 0.0           | 31.52 | 14500 | nan             | 0.3333   | 0.1667 |
| 0.0           | 32.61 | 15000 | nan             | 0.3333   | 0.1667 |
| 0.0           | 33.7  | 15500 | nan             | 0.3333   | 0.1667 |
| 0.0           | 34.78 | 16000 | nan             | 0.3333   | 0.1667 |
| 0.0           | 35.87 | 16500 | nan             | 0.3333   | 0.1667 |
| 0.0           | 36.96 | 17000 | nan             | 0.3333   | 0.1667 |
| 0.0           | 38.04 | 17500 | nan             | 0.3333   | 0.1667 |
| 0.0           | 39.13 | 18000 | nan             | 0.3333   | 0.1667 |
| 0.0           | 40.22 | 18500 | nan             | 0.3333   | 0.1667 |
| 0.0           | 41.3  | 19000 | nan             | 0.3333   | 0.1667 |
| 0.0           | 42.39 | 19500 | nan             | 0.3333   | 0.1667 |
| 0.0           | 43.48 | 20000 | nan             | 0.3333   | 0.1667 |
| 0.0           | 44.57 | 20500 | nan             | 0.3333   | 0.1667 |
| 0.0           | 45.65 | 21000 | nan             | 0.3333   | 0.1667 |
| 0.0           | 46.74 | 21500 | nan             | 0.3333   | 0.1667 |
| 0.0           | 47.83 | 22000 | nan             | 0.3333   | 0.1667 |
| 0.0           | 48.91 | 22500 | nan             | 0.3333   | 0.1667 |
| 0.0           | 50.0  | 23000 | nan             | 0.3333   | 0.1667 |


### Framework versions

- Transformers 4.33.3
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.13.3