---
library_name: transformers
license: mit
base_model: microsoft/mdeberta-v3-base
tags:
- generated_from_trainer
datasets:
- tweet_sentiment_multilingual
metrics:
- accuracy
- f1
model-index:
- name: scenario-NON-KD-SCR-COPY-CDF-ALL-D2_data-cardiffnlp_tweet_sentiment_multilingual
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: tweet_sentiment_multilingual
      type: tweet_sentiment_multilingual
      config: all
      split: validation
      args: all
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.4903549382716049
    - name: F1
      type: f1
      value: 0.490123758683559
---

<!-- 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-NON-KD-SCR-COPY-CDF-ALL-D2_data-cardiffnlp_tweet_sentiment_multilingual

This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the tweet_sentiment_multilingual dataset.
It achieves the following results on the evaluation set:
- Loss: 6.8615
- Accuracy: 0.4904
- F1: 0.4901

## 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: 66
- 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     |
|:-------------:|:-------:|:-----:|:---------------:|:--------:|:------:|
| 1.0475        | 1.0870  | 500   | 1.0371          | 0.4985   | 0.4949 |
| 0.7462        | 2.1739  | 1000  | 1.2759          | 0.5123   | 0.5122 |
| 0.421         | 3.2609  | 1500  | 1.6791          | 0.5139   | 0.5126 |
| 0.2321        | 4.3478  | 2000  | 2.1227          | 0.4946   | 0.4940 |
| 0.1534        | 5.4348  | 2500  | 2.4070          | 0.4958   | 0.4966 |
| 0.0987        | 6.5217  | 3000  | 2.8761          | 0.4904   | 0.4900 |
| 0.0734        | 7.6087  | 3500  | 2.8613          | 0.4911   | 0.4881 |
| 0.0697        | 8.6957  | 4000  | 3.5593          | 0.4969   | 0.4932 |
| 0.0586        | 9.7826  | 4500  | 3.4005          | 0.4900   | 0.4883 |
| 0.0462        | 10.8696 | 5000  | 3.6698          | 0.4861   | 0.4866 |
| 0.0321        | 11.9565 | 5500  | 4.1118          | 0.4877   | 0.4883 |
| 0.0267        | 13.0435 | 6000  | 4.1028          | 0.4965   | 0.4959 |
| 0.0257        | 14.1304 | 6500  | 4.3167          | 0.4842   | 0.4815 |
| 0.0185        | 15.2174 | 7000  | 4.3273          | 0.4923   | 0.4876 |
| 0.0178        | 16.3043 | 7500  | 4.7543          | 0.4958   | 0.4959 |
| 0.0149        | 17.3913 | 8000  | 4.3035          | 0.4927   | 0.4929 |
| 0.0125        | 18.4783 | 8500  | 4.5842          | 0.4904   | 0.4884 |
| 0.0116        | 19.5652 | 9000  | 5.3172          | 0.4853   | 0.4833 |
| 0.0114        | 20.6522 | 9500  | 4.8280          | 0.4857   | 0.4825 |
| 0.0036        | 21.7391 | 10000 | 5.6275          | 0.4850   | 0.4820 |
| 0.0094        | 22.8261 | 10500 | 5.1559          | 0.4842   | 0.4815 |
| 0.0054        | 23.9130 | 11000 | 5.3889          | 0.4846   | 0.4826 |
| 0.0085        | 25.0    | 11500 | 4.8587          | 0.4888   | 0.4861 |
| 0.0068        | 26.0870 | 12000 | 5.3553          | 0.4896   | 0.4881 |
| 0.0054        | 27.1739 | 12500 | 5.3446          | 0.4853   | 0.4845 |
| 0.0042        | 28.2609 | 13000 | 5.3437          | 0.4838   | 0.4832 |
| 0.003         | 29.3478 | 13500 | 5.9054          | 0.4796   | 0.4784 |
| 0.0032        | 30.4348 | 14000 | 5.7871          | 0.4884   | 0.4881 |
| 0.0038        | 31.5217 | 14500 | 5.9122          | 0.4803   | 0.4787 |
| 0.0041        | 32.6087 | 15000 | 5.4601          | 0.4834   | 0.4786 |
| 0.0025        | 33.6957 | 15500 | 5.1979          | 0.4884   | 0.4853 |
| 0.0018        | 34.7826 | 16000 | 5.5286          | 0.4896   | 0.4869 |
| 0.0006        | 35.8696 | 16500 | 5.7718          | 0.4877   | 0.4859 |
| 0.0015        | 36.9565 | 17000 | 6.0193          | 0.4834   | 0.4832 |
| 0.0003        | 38.0435 | 17500 | 6.2210          | 0.4838   | 0.4828 |
| 0.0004        | 39.1304 | 18000 | 6.3234          | 0.4880   | 0.4879 |
| 0.0002        | 40.2174 | 18500 | 6.3829          | 0.4888   | 0.4885 |
| 0.0001        | 41.3043 | 19000 | 6.5514          | 0.4892   | 0.4889 |
| 0.0001        | 42.3913 | 19500 | 6.6261          | 0.4892   | 0.4891 |
| 0.0003        | 43.4783 | 20000 | 6.6971          | 0.4861   | 0.4849 |
| 0.0013        | 44.5652 | 20500 | 6.7077          | 0.4865   | 0.4849 |
| 0.0001        | 45.6522 | 21000 | 6.7350          | 0.4911   | 0.4903 |
| 0.0001        | 46.7391 | 21500 | 6.7889          | 0.4896   | 0.4888 |
| 0.0002        | 47.8261 | 22000 | 6.8318          | 0.4900   | 0.4902 |
| 0.0006        | 48.9130 | 22500 | 6.8526          | 0.4904   | 0.4901 |
| 0.0001        | 50.0    | 23000 | 6.8615          | 0.4904   | 0.4901 |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.19.1