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multi-e5-base_lmd-comments_v1

Text Classification
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xlm-roberta
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multi-e5-base_lmd-comments_v1 / README.md
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metadata
license: mit
base_model: intfloat/multilingual-e5-base
tags:
  - generated_from_trainer
metrics:
  - f1
  - accuracy
model-index:
  - name: multi-e5-base_lmd-comments_v1
    results: []

multi-e5-base_lmd-comments_v1

This model is a fine-tuned version of intfloat/multilingual-e5-base on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 1.1145
  • F1: 0.7338
  • Accuracy: 0.7410

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: 2e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 2

Training results

Training Loss Epoch Step Validation Loss F1 Accuracy
1.1179 0.04 100 1.1781 0.3969 0.4748
1.0342 0.08 200 1.0924 0.5137 0.5899
0.7423 0.12 300 0.9700 0.6454 0.6691
0.7046 0.17 400 0.8990 0.6462 0.6691
0.6419 0.21 500 0.9583 0.6220 0.6475
0.6679 0.25 600 0.8621 0.6757 0.6835
0.6244 0.29 700 0.8017 0.7399 0.7410
0.5747 0.33 800 0.8040 0.6950 0.6906
0.5575 0.37 900 1.1045 0.6774 0.6906
0.5994 0.41 1000 1.1592 0.6725 0.6978
0.5565 0.46 1100 0.9960 0.7303 0.7338
0.511 0.5 1200 1.0861 0.7377 0.7482
0.5448 0.54 1300 0.7945 0.7155 0.7122
0.6059 0.58 1400 0.8167 0.6879 0.6906
0.4865 0.62 1500 1.1002 0.7181 0.7266
0.566 0.66 1600 0.7388 0.6678 0.6691
0.4756 0.7 1700 1.1751 0.7385 0.7482
0.5595 0.75 1800 1.0169 0.7204 0.7266
0.5838 0.79 1900 0.7718 0.7005 0.6978
0.573 0.83 2000 0.9156 0.7174 0.7266
0.5623 0.87 2100 0.8405 0.7416 0.7482
0.4929 0.91 2200 0.8329 0.7484 0.7554
0.5135 0.95 2300 1.1845 0.7008 0.7194
0.5217 0.99 2400 1.1482 0.7204 0.7338
0.4342 1.04 2500 1.3326 0.7078 0.7266
0.4975 1.08 2600 1.0527 0.7048 0.7194
0.4135 1.12 2700 0.9742 0.7431 0.7482
0.3562 1.16 2800 1.0554 0.7359 0.7410
0.3892 1.2 2900 1.1289 0.7403 0.7482
0.5041 1.24 3000 0.9690 0.7642 0.7698
0.4808 1.28 3100 0.9745 0.7378 0.7410
0.3532 1.33 3200 1.0141 0.7521 0.7554
0.4679 1.37 3300 0.9923 0.7410 0.7482
0.432 1.41 3400 1.0650 0.7486 0.7554
0.4543 1.45 3500 1.1235 0.7474 0.7554
0.4716 1.49 3600 1.0688 0.7316 0.7410
0.4251 1.53 3700 1.0290 0.7415 0.7482
0.3676 1.57 3800 1.1651 0.7546 0.7626
0.4031 1.62 3900 0.9981 0.7559 0.7626
0.4356 1.66 4000 0.9815 0.7558 0.7626
0.4355 1.7 4100 1.0349 0.7443 0.7482
0.4113 1.74 4200 1.1226 0.7333 0.7410
0.4447 1.78 4300 0.9854 0.7423 0.7482
0.4601 1.82 4400 1.0193 0.7348 0.7410
0.4474 1.86 4500 1.0177 0.7423 0.7482
0.3585 1.91 4600 1.0460 0.7276 0.7338
0.4064 1.95 4700 1.0995 0.7276 0.7338
0.4443 1.99 4800 1.1145 0.7338 0.7410

Framework versions

  • Transformers 4.38.1
  • Pytorch 2.1.2
  • Datasets 2.1.0
  • Tokenizers 0.15.2