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vazish/paraphrase-multilingual-MiniLM-L12-v2
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metadata
library_name: transformers
license: apache-2.0
base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
model-index:
  - name: fine-tuned-distilbert-autofill
    results: []

fine-tuned-distilbert-autofill

This model is a fine-tuned version of sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2367
  • Precision: 0.9484
  • Recall: 0.9473
  • F1: 0.9473
  • Confusion Matrix: [[ 94 5 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0] [ 14 44 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0] [ 0 0 130 0 0 0 0 0 0 0 0 0 0 0 0 0 9] [ 0 0 0 33 0 0 0 0 0 0 0 0 0 0 2 0 0] [ 0 0 2 0 64 0 0 0 0 0 3 0 0 0 0 0 7] [ 0 0 0 0 0 53 0 0 0 0 0 0 0 0 2 0 0] [ 0 0 0 0 0 0 37 1 0 0 0 0 0 0 0 0 3] [ 0 0 0 0 0 0 4 35 0 0 0 0 0 0 0 0 2] [ 1 0 0 0 0 1 0 0 43 0 0 0 0 0 2 0 0] [ 0 0 0 0 0 0 0 0 0 31 0 0 0 0 1 0 0] [ 0 0 0 0 2 0 0 2 0 0 10 0 0 0 0 0 0] [ 0 0 0 0 0 0 0 1 0 0 1 16 0 0 0 0 0] [ 0 0 0 0 0 0 0 0 0 1 0 0 5 0 0 0 0] [ 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0] [ 0 0 0 1 0 0 1 0 0 0 0 0 0 0 73 0 0] [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0] [ 1 0 9 1 4 0 0 0 2 0 2 0 1 2 1 0 977]]

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: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 5

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Confusion Matrix
0.7726 1.0 987 0.3096 0.8920 0.9141 0.8988 [[100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 58 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 129 0 0 0 0 0 0 0 0 0 0 0 0 0 10]
[ 0 0 0 32 0 0 0 0 1 1 0 0 0 0 0 0 1]
[ 0 0 4 0 63 0 0 0 0 0 0 0 0 0 0 0 9]
[ 0 0 0 0 0 52 0 0 0 2 0 0 0 0 0 0 1]
[ 0 0 0 0 0 0 36 0 0 0 2 0 0 0 0 0 3]
[ 0 0 0 0 0 0 2 33 0 0 4 0 0 0 0 0 2]
[ 1 0 0 0 0 1 0 0 43 2 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 32 0 0 0 0 0 0 0]
[ 0 0 0 0 2 0 0 0 0 0 12 0 0 0 0 0 0]
[ 0 0 0 0 0 0 4 0 0 0 1 13 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 1 0 1 2 0 0 0 0 71 0 0]
[ 0 0 0 0 2 0 0 0 0 1 0 0 0 0 0 0 0]
[ 1 0 7 1 5 0 0 0 1 3 1 0 0 0 1 0 980]]
0.2616 2.0 1974 0.2645 0.9356 0.9273 0.9179 [[ 99 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0]
[ 43 7 5 0 0 0 0 0 0 0 0 0 0 0 0 0 3]
[ 0 0 128 0 0 0 0 0 0 0 0 0 0 0 0 0 11]
[ 0 0 0 33 0 0 0 0 1 0 0 0 0 0 0 0 1]
[ 0 0 0 0 64 0 0 0 0 0 0 0 0 0 0 0 12]
[ 0 0 0 1 0 53 0 0 0 0 0 0 0 0 0 0 1]
[ 0 0 0 0 0 0 36 2 0 0 0 0 0 0 0 0 3]
[ 0 0 0 0 0 0 3 36 0 0 0 0 0 0 0 0 2]
[ 1 0 0 0 0 2 0 0 43 0 0 0 0 0 0 0 1]
[ 0 0 0 1 0 0 0 0 0 31 0 0 0 0 0 0 0]
[ 0 0 0 0 2 0 0 3 0 0 9 0 0 0 0 0 0]
[ 0 0 0 0 0 0 1 3 0 0 1 13 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 1 0 0 5 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0]
[ 0 0 0 1 0 0 1 0 1 1 0 0 0 0 71 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0]
[ 1 0 6 1 3 0 0 0 1 0 2 0 1 2 1 0 982]]
0.1814 3.0 2961 0.2332 0.9437 0.9422 0.9420 [[ 94 5 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0]
[ 15 43 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 127 0 1 0 0 0 0 0 0 0 0 0 0 0 11]
[ 0 0 0 34 0 0 0 0 0 0 0 0 0 0 0 0 1]
[ 0 0 1 0 63 0 0 0 0 0 2 0 0 0 0 0 10]
[ 0 0 0 1 0 52 0 0 0 1 1 0 0 0 0 0 0]
[ 0 0 0 0 0 0 37 1 0 0 0 0 0 0 0 0 3]
[ 0 0 0 0 0 0 4 35 0 0 0 0 0 0 0 0 2]
[ 1 0 0 0 0 1 0 0 43 2 0 0 0 0 0 0 0]
[ 0 0 0 1 0 0 0 0 0 31 0 0 0 0 0 0 0]
[ 0 0 0 0 2 0 0 2 0 0 10 0 0 0 0 0 0]
[ 0 0 0 0 0 0 2 2 0 0 1 13 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 1 0 0 5 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0]
[ 0 0 0 1 0 0 1 0 0 1 0 0 1 0 71 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0]
[ 1 0 8 1 4 0 0 1 1 0 2 0 1 2 1 0 978]]
0.1248 4.0 3948 0.2255 0.9501 0.9479 0.9482 [[ 95 4 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0]
[ 13 45 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 130 0 0 0 0 0 0 0 0 0 0 0 0 0 9]
[ 0 0 0 33 0 0 0 0 0 0 0 0 0 0 2 0 0]
[ 0 0 2 0 65 0 0 0 0 0 4 0 0 0 0 0 5]
[ 0 0 0 0 0 52 0 0 0 0 1 0 0 0 2 0 0]
[ 0 0 0 0 0 0 38 0 0 0 0 0 0 0 0 0 3]
[ 0 0 0 0 0 0 5 34 0 0 0 0 0 0 0 0 2]
[ 1 0 0 0 0 1 0 0 43 0 0 0 0 0 2 0 0]
[ 0 0 0 0 0 0 0 0 1 30 0 0 0 0 1 0 0]
[ 0 0 0 0 2 0 2 0 0 0 10 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 1 0 0 1 16 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 1 0 0 5 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0]
[ 0 0 0 1 0 0 1 0 0 0 0 0 0 0 73 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0]
[ 1 0 9 1 4 0 0 0 2 0 2 0 1 2 1 0 977]]
0.1032 5.0 4935 0.2367 0.9484 0.9473 0.9473 [[ 94 5 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0]
[ 14 44 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 130 0 0 0 0 0 0 0 0 0 0 0 0 0 9]
[ 0 0 0 33 0 0 0 0 0 0 0 0 0 0 2 0 0]
[ 0 0 2 0 64 0 0 0 0 0 3 0 0 0 0 0 7]
[ 0 0 0 0 0 53 0 0 0 0 0 0 0 0 2 0 0]
[ 0 0 0 0 0 0 37 1 0 0 0 0 0 0 0 0 3]
[ 0 0 0 0 0 0 4 35 0 0 0 0 0 0 0 0 2]
[ 1 0 0 0 0 1 0 0 43 0 0 0 0 0 2 0 0]
[ 0 0 0 0 0 0 0 0 0 31 0 0 0 0 1 0 0]
[ 0 0 0 0 2 0 0 2 0 0 10 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 1 0 0 1 16 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 1 0 0 5 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0]
[ 0 0 0 1 0 0 1 0 0 0 0 0 0 0 73 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0]
[ 1 0 9 1 4 0 0 0 2 0 2 0 1 2 1 0 977]]

Framework versions

  • Transformers 4.44.2
  • Pytorch 2.1.2
  • Datasets 2.19.2
  • Tokenizers 0.19.1