Hugging Face's logo

Narkantak
/
Intent-classification-12kv2

Text Classification
Transformers
Safetensors
bert
Generated from Trainer
Inference Endpoints
Model card Files Community
Intent-classification-12kv2 / README.md
Narkantak's picture
Narkantak
End of training
503a645 verified
preview code
|
raw
history blame
No virus
10.3 kB
metadata
license: apache-2.0
base_model: google-bert/bert-base-uncased
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - f1
  - precision
  - recall
model-index:
  - name: Intent-classification-12kv2
    results: []

Intent-classification-12kv2

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

  • Loss: 0.0074
  • Accuracy: 0.9984
  • F1: 0.9983
  • Precision: 0.9983
  • Recall: 0.9983

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: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 10
  • num_epochs: 5

Training results

Training Loss Epoch Step Validation Loss Accuracy F1 Precision Recall
1.742 0.05 10 1.4822 0.6954 0.6918 0.7288 0.6966
1.2849 0.11 20 0.9533 0.8713 0.8699 0.8899 0.8729
0.8226 0.16 30 0.5235 0.9786 0.9786 0.9790 0.9785
0.399 0.21 40 0.2295 0.9812 0.9812 0.9811 0.9817
0.1871 0.26 50 0.1168 0.9839 0.9839 0.9844 0.9836
0.0855 0.32 60 0.0508 0.9928 0.9928 0.9928 0.9928
0.0546 0.37 70 0.0300 0.9947 0.9947 0.9948 0.9947
0.0226 0.42 80 0.0271 0.9947 0.9948 0.9947 0.9948
0.0306 0.47 90 0.0416 0.9888 0.9887 0.9894 0.9883
0.0336 0.53 100 0.0157 0.9970 0.9970 0.9970 0.9971
0.0373 0.58 110 0.0214 0.9951 0.9951 0.9952 0.9951
0.0094 0.63 120 0.0121 0.9970 0.9971 0.9971 0.9970
0.0077 0.68 130 0.0094 0.9980 0.9980 0.9980 0.9981
0.0253 0.74 140 0.0077 0.9987 0.9987 0.9987 0.9987
0.0233 0.79 150 0.0075 0.9987 0.9987 0.9987 0.9987
0.0068 0.84 160 0.0080 0.9987 0.9987 0.9987 0.9987
0.0286 0.89 170 0.0141 0.9964 0.9964 0.9964 0.9964
0.0139 0.95 180 0.0104 0.9970 0.9970 0.9970 0.9971
0.0043 1.0 190 0.0074 0.9977 0.9977 0.9977 0.9976
0.0122 1.05 200 0.0065 0.9987 0.9987 0.9987 0.9987
0.0071 1.11 210 0.0059 0.9980 0.9980 0.9981 0.9980
0.0025 1.16 220 0.0083 0.9984 0.9984 0.9984 0.9983
0.0232 1.21 230 0.0057 0.9984 0.9984 0.9984 0.9984
0.0035 1.26 240 0.0056 0.9987 0.9987 0.9987 0.9987
0.0246 1.32 250 0.0053 0.9984 0.9984 0.9984 0.9983
0.0023 1.37 260 0.0063 0.9980 0.9980 0.9981 0.9980
0.0021 1.42 270 0.0048 0.9984 0.9984 0.9984 0.9983
0.002 1.47 280 0.0028 0.9997 0.9997 0.9997 0.9997
0.022 1.53 290 0.0023 0.9997 0.9997 0.9997 0.9997
0.0135 1.58 300 0.0046 0.9987 0.9987 0.9987 0.9987
0.0026 1.63 310 0.0082 0.9977 0.9977 0.9979 0.9976
0.0019 1.68 320 0.0043 0.9990 0.9990 0.9991 0.9990
0.0017 1.74 330 0.0035 0.9993 0.9994 0.9994 0.9994
0.0019 1.79 340 0.0015 1.0 1.0 1.0 1.0
0.0014 1.84 350 0.0013 1.0 1.0 1.0 1.0
0.0014 1.89 360 0.0013 1.0 1.0 1.0 1.0
0.0013 1.95 370 0.0012 1.0 1.0 1.0 1.0
0.0013 2.0 380 0.0011 1.0 1.0 1.0 1.0
0.0012 2.05 390 0.0011 1.0 1.0 1.0 1.0
0.0011 2.11 400 0.0011 1.0 1.0 1.0 1.0
0.0011 2.16 410 0.0010 1.0 1.0 1.0 1.0
0.0011 2.21 420 0.0010 1.0 1.0 1.0 1.0
0.0014 2.26 430 0.0009 1.0 1.0 1.0 1.0
0.001 2.32 440 0.0009 1.0 1.0 1.0 1.0
0.001 2.37 450 0.0009 1.0 1.0 1.0 1.0
0.0009 2.42 460 0.0009 1.0 1.0 1.0 1.0
0.0009 2.47 470 0.0008 1.0 1.0 1.0 1.0
0.0009 2.53 480 0.0008 1.0 1.0 1.0 1.0
0.0009 2.58 490 0.0008 1.0 1.0 1.0 1.0
0.0009 2.63 500 0.0008 1.0 1.0 1.0 1.0
0.0008 2.68 510 0.0008 1.0 1.0 1.0 1.0
0.0008 2.74 520 0.0008 1.0 1.0 1.0 1.0
0.0008 2.79 530 0.0007 1.0 1.0 1.0 1.0
0.0008 2.84 540 0.0007 1.0 1.0 1.0 1.0
0.0008 2.89 550 0.0007 1.0 1.0 1.0 1.0
0.0008 2.95 560 0.0007 1.0 1.0 1.0 1.0
0.0007 3.0 570 0.0007 1.0 1.0 1.0 1.0
0.0009 3.05 580 0.0007 1.0 1.0 1.0 1.0
0.0007 3.11 590 0.0006 1.0 1.0 1.0 1.0
0.0007 3.16 600 0.0006 1.0 1.0 1.0 1.0
0.0007 3.21 610 0.0006 1.0 1.0 1.0 1.0
0.0007 3.26 620 0.0006 1.0 1.0 1.0 1.0
0.0007 3.32 630 0.0006 1.0 1.0 1.0 1.0
0.0007 3.37 640 0.0006 1.0 1.0 1.0 1.0
0.0006 3.42 650 0.0006 1.0 1.0 1.0 1.0
0.0006 3.47 660 0.0006 1.0 1.0 1.0 1.0
0.0006 3.53 670 0.0006 1.0 1.0 1.0 1.0
0.0006 3.58 680 0.0006 1.0 1.0 1.0 1.0
0.0006 3.63 690 0.0006 1.0 1.0 1.0 1.0
0.0006 3.68 700 0.0006 1.0 1.0 1.0 1.0
0.0006 3.74 710 0.0006 1.0 1.0 1.0 1.0
0.0006 3.79 720 0.0006 1.0 1.0 1.0 1.0
0.0006 3.84 730 0.0006 1.0 1.0 1.0 1.0
0.0006 3.89 740 0.0005 1.0 1.0 1.0 1.0
0.0006 3.95 750 0.0005 1.0 1.0 1.0 1.0
0.0006 4.0 760 0.0005 1.0 1.0 1.0 1.0
0.0006 4.05 770 0.0005 1.0 1.0 1.0 1.0
0.0006 4.11 780 0.0005 1.0 1.0 1.0 1.0
0.0006 4.16 790 0.0005 1.0 1.0 1.0 1.0
0.0006 4.21 800 0.0005 1.0 1.0 1.0 1.0
0.0006 4.26 810 0.0005 1.0 1.0 1.0 1.0
0.0006 4.32 820 0.0005 1.0 1.0 1.0 1.0
0.0006 4.37 830 0.0005 1.0 1.0 1.0 1.0
0.0006 4.42 840 0.0005 1.0 1.0 1.0 1.0
0.0006 4.47 850 0.0005 1.0 1.0 1.0 1.0
0.0006 4.53 860 0.0005 1.0 1.0 1.0 1.0
0.0006 4.58 870 0.0005 1.0 1.0 1.0 1.0
0.0005 4.63 880 0.0005 1.0 1.0 1.0 1.0
0.0006 4.68 890 0.0005 1.0 1.0 1.0 1.0
0.0005 4.74 900 0.0005 1.0 1.0 1.0 1.0
0.0005 4.79 910 0.0005 1.0 1.0 1.0 1.0
0.0006 4.84 920 0.0005 1.0 1.0 1.0 1.0
0.0005 4.89 930 0.0005 1.0 1.0 1.0 1.0
0.0006 4.95 940 0.0005 1.0 1.0 1.0 1.0
0.0005 5.0 950 0.0005 1.0 1.0 1.0 1.0

Framework versions

  • Transformers 4.38.2
  • Pytorch 2.1.2
  • Datasets 2.1.0
  • Tokenizers 0.15.2