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distilbert-base-uncased-finetuned-sst-2-english_07112024T125645

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metadata
library_name: transformers
license: apache-2.0
base_model: distilbert/distilbert-base-uncased-finetuned-sst-2-english
tags:
  - generated_from_trainer
metrics:
  - f1
model-index:
  - name: distilbert-base-uncased-finetuned-sst-2-english_07112024T125645
    results: []

distilbert-base-uncased-finetuned-sst-2-english_07112024T125645

This model is a fine-tuned version of distilbert/distilbert-base-uncased-finetuned-sst-2-english on the MR Analysis Phase-3 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.5776
  • F1: 0.8426
  • Learning Rate: 0.0

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
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 32
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_steps: 10
  • num_epochs: 20
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss F1 Rate
No log 1.0 141 1.1776 0.5721 0.0000
No log 2.0 282 0.9785 0.6619 0.0000
No log 3.0 423 0.8326 0.7194 0.0000
1.1084 4.0 564 0.6920 0.7808 0.0000
1.1084 5.0 705 0.6907 0.7973 0.0000
1.1084 6.0 846 0.6107 0.8284 0.0000
1.1084 7.0 987 0.5776 0.8426 0.0000
0.4572 8.0 1128 0.6100 0.8523 0.0000
0.4572 9.0 1269 0.6279 0.8570 0.0000
0.4572 10.0 1410 0.6638 0.8587 0.0000
0.1637 11.0 1551 0.7340 0.8568 0.0000
0.1637 12.0 1692 0.7564 0.8596 7e-06
0.1637 13.0 1833 0.8077 0.8568 0.0000
0.1637 14.0 1974 0.7234 0.8667 0.0000
0.069 15.0 2115 0.7535 0.8664 3e-06
0.069 16.0 2256 0.7818 0.8659 0.0000
0.069 17.0 2397 0.8064 0.8646 0.0000
0.0376 18.0 2538 0.8203 0.8626 5e-07
0.0376 19.0 2679 0.8233 0.8629 1e-07
0.0376 20.0 2820 0.8235 0.8632 0.0

Testing Results

class precision recall f1-score
change_request 0.918 0.651 0.762
discussion_participation 0.839 0.882 0.860
discussion_trigger 0.879 0.902 0.890
acknowledgement 0.847 0.920 0.882
critical 0.686 0.940 0.793
reference 0.802 0.947 0.869
----------- ---------- -------- ---------
accuracy 0.828
macro avg 0.828 0.874 0.843
weighted avg 0.845 0.828 0.825

Framework versions

  • Transformers 4.44.2
  • Pytorch 2.5.1+cu124
  • Datasets 3.1.0
  • Tokenizers 0.19.1