Hugging Face's logo

haryoaw
/
scenario-KD-SCR-CDF-ALL-D2_data-cardiffnlp_tweet_sentiment_multilingual_all55

Transformers
PyTorch
deberta-v2
Generated from Trainer
Inference Endpoints
Model card Files Community
scenario-KD-SCR-CDF-ALL-D2_data-cardiffnlp_tweet_sentiment_multilingual_all55 / README.md
haryoaw's picture
haryoaw
Initial Commit
1229577 verified
preview code
|
raw
history blame
4.99 kB
metadata
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: []

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 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