metadata
license: mit
base_model: microsoft/mdeberta-v3-base
tags:
- generated_from_trainer
datasets:
- tweet_sentiment_multilingual
metrics:
- accuracy
- f1
model-index:
- name: scenario-MDBT-TCR_data-cardiffnlp_tweet_sentiment_multilingual_all
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: 0.6512345679012346
- name: F1
type: f1
value: 0.6483011417314103
scenario-MDBT-TCR_data-cardiffnlp_tweet_sentiment_multilingual_all
This model is a fine-tuned version of microsoft/mdeberta-v3-base on the tweet_sentiment_multilingual dataset. It achieves the following results on the evaluation set:
- Loss: 1.7268
- Accuracy: 0.6512
- F1: 0.6483
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: 64
- seed: 66
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.8937 | 1.09 | 500 | 0.8922 | 0.6304 | 0.6189 |
| 0.6912 | 2.17 | 1000 | 0.8900 | 0.6551 | 0.6516 |
| 0.527 | 3.26 | 1500 | 0.9088 | 0.6593 | 0.6583 |
| 0.3874 | 4.35 | 2000 | 1.1089 | 0.6516 | 0.6470 |
| 0.2977 | 5.43 | 2500 | 1.2137 | 0.6408 | 0.6433 |
| 0.2397 | 6.52 | 3000 | 1.2022 | 0.6431 | 0.6409 |
| 0.203 | 7.61 | 3500 | 1.4913 | 0.6454 | 0.6469 |
| 0.1658 | 8.7 | 4000 | 1.7268 | 0.6512 | 0.6483 |
Framework versions
- Transformers 4.33.3
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.13.3