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fedcsis-slot_baseline-xlm_r-pl

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metadata
license: mit
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: fedcsis-slot_baseline-xlm_r-pl
    results: []
datasets:
  - cartesinus/leyzer-fedcsis
language:
  - pl

fedcsis-slot_baseline-xlm_r-pl

This model is a fine-tuned version of xlm-roberta-base on the leyzer-fedcsis dataset.

Results on test set:

  • Precision: 0.9621
  • Recall: 0.9583
  • F1: 0.9602
  • Accuracy: 0.9857

It achieves the following results on the evaluation set:

  • Loss: 0.1009
  • Precision: 0.9579
  • Recall: 0.9512
  • F1: 0.9546
  • Accuracy: 0.9860

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: 16
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 10

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
1.1608 1.0 798 0.2575 0.8881 0.8916 0.8898 0.9532
0.1561 2.0 1596 0.1188 0.9459 0.9389 0.9424 0.9806
0.0979 3.0 2394 0.1060 0.9507 0.9486 0.9497 0.9838
0.0579 4.0 3192 0.0916 0.9573 0.9475 0.9524 0.9851
0.0507 5.0 3990 0.1109 0.9527 0.9506 0.9516 0.9839
0.0344 6.0 4788 0.0987 0.9575 0.9488 0.9531 0.9855
0.0266 7.0 5586 0.1010 0.9584 0.9501 0.9542 0.9854
0.0211 8.0 6384 0.1051 0.9575 0.9498 0.9536 0.9855
0.0168 9.0 7182 0.1009 0.9577 0.9516 0.9546 0.9861
0.016 10.0 7980 0.1009 0.9579 0.9512 0.9546 0.9860

Per slot evaluation

slot_name precision recall f1 tc_size
album 0.2000 0.3333 0.2500 9
all_lang 1.0000 1.0000 1.0000 5
artist 0.9341 0.9444 0.9392 90
av_alias 0.6667 0.8000 0.7273 5
caption 0.9651 0.9432 0.9540 88
category 0.0000 0.0000 0.0000 1
category_a 1.0000 0.9167 0.9565 12
category_b 1.0000 1.0000 1.0000 25
channel 0.9492 0.9333 0.9412 60
channel_id 0.9701 0.9644 0.9673 337
count 1.0000 0.9167 0.9565 12
date 0.9764 0.9841 0.9802 126
date_day 1.0000 0.9500 0.9744 20
date_month 0.9677 1.0000 0.9836 30
device_name 0.9091 1.0000 0.9524 10
email 1.0000 0.9913 0.9956 115
event_name 0.8788 0.9355 0.9063 31
file_name 0.9778 0.9778 0.9778 45
file_size 1.0000 1.0000 1.0000 12
filename 0.9722 0.9589 0.9655 73
filter 1.0000 1.0000 1.0000 35
from 0.9811 0.9123 0.9455 57
hashtag 1.0000 1.0000 1.0000 28
img_query 0.9707 0.9678 0.9693 342
label 1.0000 1.0000 1.0000 5
location 0.9766 0.9728 0.9747 257
mail 1.0000 1.0000 1.0000 3
message 0.9250 0.9487 0.9367 117
mime_type 0.9375 1.0000 0.9677 15
name 0.9412 0.9796 0.9600 49
pathname 0.8889 0.8889 0.8889 18
percent 1.0000 1.0000 1.0000 3
phone_number 0.9774 0.9774 0.9774 177
phone_type 1.0000 1.0000 1.0000 21
picture_url 0.9846 0.9412 0.9624 68
playlist 0.9516 0.9672 0.9593 122
portal 0.9869 0.9869 0.9869 153
priority 0.7500 1.0000 0.8571 6
purpose 0.0000 0.0000 0.0000 5
query 0.9663 0.9690 0.9677 355
rating 0.9630 0.9286 0.9455 28
review_count 1.0000 1.0000 1.0000 20
section 0.9730 0.9730 0.9730 74
seek_time 1.0000 1.0000 1.0000 3
sender 1.0000 1.0000 1.0000 6
sender_address 1.0000 0.9444 0.9714 18
song 0.8824 0.8898 0.8861 118
src_lang_de 0.9880 0.9762 0.9820 84
src_lang_en 0.9455 0.9630 0.9541 54
src_lang_es 0.9853 0.9306 0.9571 72
src_lang_fr 0.9733 0.9733 0.9733 75
src_lang_it 0.9872 0.9506 0.9686 81
src_lang_pl 0.9818 1.0000 0.9908 54
status 0.8810 0.9487 0.9136 39
subject 0.9636 0.9725 0.9680 109
text_de 0.9762 0.9762 0.9762 84
text_en 0.9796 0.9697 0.9746 99
text_es 0.8734 0.9583 0.9139 72
text_fr 0.9733 0.9733 0.9733 75
text_it 0.9872 0.9506 0.9686 81
text_multi 0.0000 0.0000 0.0000 4
text_pl 0.9310 1.0000 0.9643 54
time 0.9063 0.8788 0.8923 33
to 0.9648 0.9648 0.9648 199
topic 0.0000 0.0000 0.0000 3
translator 0.9838 0.9838 0.9838 185
trg_lang_de 0.9474 0.9730 0.9600 37
trg_lang_en 1.0000 0.9565 0.9778 46
trg_lang_es 0.9792 0.9792 0.9792 48
trg_lang_fr 0.9808 1.0000 0.9903 51
trg_lang_general 0.9500 0.9500 0.9500 20
trg_lang_it 0.9825 0.9492 0.9655 59
trg_lang_pl 0.9302 0.9756 0.9524 41
txt_query 0.9375 0.9146 0.9259 82
username 0.9615 0.8929 0.9259 28
value 0.8750 0.8750 0.8750 8
weight 1.0000 1.0000 1.0000 3

Framework versions

  • Transformers 4.27.4
  • Pytorch 1.13.1+cu116
  • Datasets 2.11.0
  • Tokenizers 0.13.2