Hugging Face's logo

haryoaw
/
scenario-TCR-XLMV_data-cl-cardiff_cl_only_beta2

Text Classification
Transformers
PyTorch
xlm-roberta
Generated from Trainer
Inference Endpoints
Model card Files Community
scenario-TCR-XLMV_data-cl-cardiff_cl_only_beta2 / README.md
haryoaw's picture
haryoaw
Initial Commit
6417d30 verified
preview code
|
raw
history blame contribute delete
3.31 kB
metadata
license: mit
base_model: facebook/xlm-v-base
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - f1
model-index:
  - name: scenario-TCR-XLMV_data-cl-cardiff_cl_only_beta2
    results: []

scenario-TCR-XLMV_data-cl-cardiff_cl_only_beta2

This model is a fine-tuned version of facebook/xlm-v-base on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 1.0989
  • 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: 67
  • 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
No log 1.09 250 1.0903 0.3673 0.2479
1.091 2.17 500 1.1006 0.3333 0.1667
1.091 3.26 750 1.0991 0.3333 0.1667
1.0999 4.35 1000 1.0988 0.3333 0.1667
1.0999 5.43 1250 1.0989 0.3333 0.1667
1.0995 6.52 1500 1.0998 0.3333 0.1667
1.0995 7.61 1750 1.0987 0.3333 0.1667
1.0993 8.7 2000 1.0987 0.3333 0.1667
1.0993 9.78 2250 1.0987 0.3333 0.1667
1.1002 10.87 2500 1.0987 0.3333 0.1667
1.1002 11.96 2750 1.0988 0.3333 0.1667
1.0996 13.04 3000 1.0987 0.3333 0.1667
1.0996 14.13 3250 1.0990 0.3333 0.1667
1.0991 15.22 3500 1.0987 0.3333 0.1667
1.0991 16.3 3750 1.0987 0.3333 0.1667
1.0992 17.39 4000 1.0986 0.3333 0.1667
1.0992 18.48 4250 1.0987 0.3333 0.1667
1.0991 19.57 4500 1.0987 0.3333 0.1667
1.0991 20.65 4750 1.0987 0.3333 0.1667
1.099 21.74 5000 1.0988 0.3333 0.1667
1.099 22.83 5250 1.0987 0.3333 0.1667
1.099 23.91 5500 1.0987 0.3333 0.1667
1.099 25.0 5750 1.0986 0.3333 0.1667
1.0988 26.09 6000 1.0991 0.3333 0.1667
1.0988 27.17 6250 1.0991 0.3333 0.1667
1.0988 28.26 6500 1.0990 0.3333 0.1667
1.0988 29.35 6750 1.0989 0.3333 0.1667

Framework versions

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