---
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: 0.4957561728395062
    - name: F1
      type: f1
      value: 0.4963309489168229
---

<!-- 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: 6.7830
- Accuracy: 0.4958
- F1: 0.4963

## 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     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 1.0563        | 1.09  | 500   | 1.0531          | 0.4950   | 0.4889 |
| 0.7646        | 2.17  | 1000  | 1.2908          | 0.5131   | 0.5122 |
| 0.4473        | 3.26  | 1500  | 1.6933          | 0.5158   | 0.5165 |
| 0.2465        | 4.35  | 2000  | 2.2069          | 0.5135   | 0.5126 |
| 0.1511        | 5.43  | 2500  | 2.5482          | 0.5081   | 0.5082 |
| 0.121         | 6.52  | 3000  | 2.8075          | 0.5123   | 0.5107 |
| 0.0834        | 7.61  | 3500  | 3.1416          | 0.5085   | 0.5082 |
| 0.061         | 8.7   | 4000  | 2.8363          | 0.5093   | 0.5083 |
| 0.0496        | 9.78  | 4500  | 3.3908          | 0.5174   | 0.5162 |
| 0.0394        | 10.87 | 5000  | 3.6362          | 0.5123   | 0.5126 |
| 0.0305        | 11.96 | 5500  | 4.0351          | 0.5035   | 0.5047 |
| 0.0283        | 13.04 | 6000  | 4.0528          | 0.5031   | 0.5042 |
| 0.0184        | 14.13 | 6500  | 4.2723          | 0.5039   | 0.5045 |
| 0.0217        | 15.22 | 7000  | 4.2612          | 0.4981   | 0.4977 |
| 0.0193        | 16.3  | 7500  | 4.3257          | 0.4907   | 0.4915 |
| 0.0196        | 17.39 | 8000  | 4.6089          | 0.4904   | 0.4906 |
| 0.0154        | 18.48 | 8500  | 4.6472          | 0.4927   | 0.4935 |
| 0.014         | 19.57 | 9000  | 4.4510          | 0.4981   | 0.4982 |
| 0.0177        | 20.65 | 9500  | 4.2732          | 0.4907   | 0.4911 |
| 0.0114        | 21.74 | 10000 | 4.5261          | 0.4931   | 0.4921 |
| 0.0099        | 22.83 | 10500 | 4.9751          | 0.4888   | 0.4901 |
| 0.0073        | 23.91 | 11000 | 4.4316          | 0.4927   | 0.4923 |
| 0.0081        | 25.0  | 11500 | 4.8393          | 0.4942   | 0.4940 |
| 0.0039        | 26.09 | 12000 | 5.2291          | 0.4988   | 0.4958 |
| 0.0052        | 27.17 | 12500 | 5.1648          | 0.4931   | 0.4942 |
| 0.0065        | 28.26 | 13000 | 5.1350          | 0.4919   | 0.4924 |
| 0.0042        | 29.35 | 13500 | 5.2707          | 0.4988   | 0.4971 |
| 0.0033        | 30.43 | 14000 | 5.2902          | 0.4896   | 0.4911 |
| 0.0041        | 31.52 | 14500 | 5.3182          | 0.4958   | 0.4971 |
| 0.002         | 32.61 | 15000 | 5.4473          | 0.4961   | 0.4968 |
| 0.001         | 33.7  | 15500 | 5.7540          | 0.4942   | 0.4952 |
| 0.0016        | 34.78 | 16000 | 5.8709          | 0.4958   | 0.4929 |
| 0.001         | 35.87 | 16500 | 6.1489          | 0.4938   | 0.4936 |
| 0.0012        | 36.96 | 17000 | 6.4545          | 0.4942   | 0.4942 |
| 0.0011        | 38.04 | 17500 | 6.4864          | 0.4946   | 0.4936 |
| 0.0024        | 39.13 | 18000 | 6.2903          | 0.5012   | 0.4998 |
| 0.001         | 40.22 | 18500 | 6.2566          | 0.4954   | 0.4950 |
| 0.0002        | 41.3  | 19000 | 6.3660          | 0.4954   | 0.4955 |
| 0.001         | 42.39 | 19500 | 6.4778          | 0.4954   | 0.4923 |
| 0.0001        | 43.48 | 20000 | 6.5401          | 0.4985   | 0.4981 |
| 0.0002        | 44.57 | 20500 | 6.6695          | 0.5      | 0.4992 |
| 0.0           | 45.65 | 21000 | 6.7149          | 0.5012   | 0.5004 |
| 0.0001        | 46.74 | 21500 | 6.7514          | 0.5015   | 0.5011 |
| 0.0008        | 47.83 | 22000 | 6.7485          | 0.4958   | 0.4964 |
| 0.0001        | 48.91 | 22500 | 6.7745          | 0.4961   | 0.4968 |
| 0.0001        | 50.0  | 23000 | 6.7830          | 0.4958   | 0.4963 |


### Framework versions

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