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Final model for experiment Serbian
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metadata
library_name: transformers
tags:
  - Serbian
  - generated_from_trainer
model-index:
  - name: childes-segmentation-100k-gpt2_lm-model
    results: []

childes-segmentation-100k-gpt2_lm-model

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

  • epoch: 4000.0
  • eval_absolute_seg_boundary_fscore_Boundary Prediction: 0.5470
  • eval_absolute_seg_boundary_fscore_Entropy: 0.4522
  • eval_absolute_seg_boundary_fscore_Increase in Boundary Prediction: 0.5803
  • eval_absolute_seg_boundary_fscore_Increase in Entropy: 0.4833
  • eval_absolute_seg_boundary_fscore_Increase in Loss: 0.5784
  • eval_absolute_seg_boundary_fscore_Increase in Rank: 0.6139
  • eval_absolute_seg_boundary_fscore_Loss: 0.4960
  • eval_absolute_seg_boundary_fscore_Majority Vote Cutoff: 0.6259
  • eval_absolute_seg_boundary_fscore_Majority Vote Spike: 0.6291
  • eval_absolute_seg_boundary_fscore_Rank: 0.5341
  • eval_absolute_seg_type_fscore_Boundary Prediction: 0.3005
  • eval_absolute_seg_type_fscore_Entropy: 0.2714
  • eval_absolute_seg_type_fscore_Increase in Boundary Prediction: 0.3422
  • eval_absolute_seg_type_fscore_Increase in Entropy: 0.2764
  • eval_absolute_seg_type_fscore_Increase in Loss: 0.3523
  • eval_absolute_seg_type_fscore_Increase in Rank: 0.3931
  • eval_absolute_seg_type_fscore_Loss: 0.2706
  • eval_absolute_seg_type_fscore_Majority Vote Cutoff: 0.4061
  • eval_absolute_seg_type_fscore_Majority Vote Spike: 0.3590
  • eval_absolute_seg_type_fscore_Rank: 0.2985
  • eval_bpc: 4.5128
  • eval_loss: 3.1280
  • eval_model_preparation_time: 0.0008
  • eval_perplexity: 22.8288
  • eval_runtime: 12.2624
  • eval_samples_per_second: 12.151
  • eval_spike_seg_boundary_fscore_Boundary Prediction: 0.5811
  • eval_spike_seg_boundary_fscore_Entropy: 0.4902
  • eval_spike_seg_boundary_fscore_Increase in Boundary Prediction: 0.5768
  • eval_spike_seg_boundary_fscore_Increase in Entropy: 0.4836
  • eval_spike_seg_boundary_fscore_Increase in Loss: 0.5501
  • eval_spike_seg_boundary_fscore_Increase in Rank: 0.5845
  • eval_spike_seg_boundary_fscore_Loss: 0.5259
  • eval_spike_seg_boundary_fscore_Majority Vote Cutoff: 0.6380
  • eval_spike_seg_boundary_fscore_Majority Vote Spike: 0.6029
  • eval_spike_seg_boundary_fscore_Rank: 0.5890
  • eval_spike_seg_type_fscore_Boundary Prediction: 0.2831
  • eval_spike_seg_type_fscore_Entropy: 0.2583
  • eval_spike_seg_type_fscore_Increase in Boundary Prediction: 0.2799
  • eval_spike_seg_type_fscore_Increase in Entropy: 0.2175
  • eval_spike_seg_type_fscore_Increase in Loss: 0.2712
  • eval_spike_seg_type_fscore_Increase in Rank: 0.3042
  • eval_spike_seg_type_fscore_Loss: 0.2594
  • eval_spike_seg_type_fscore_Majority Vote Cutoff: 0.3492
  • eval_spike_seg_type_fscore_Majority Vote Spike: 0.2747
  • eval_spike_seg_type_fscore_Rank: 0.3375
  • eval_steps_per_second: 0.408
  • step: 100000

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: 0.001
  • 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: 30000
  • training_steps: 100000

Framework versions

  • Transformers 4.44.2
  • Pytorch 2.4.0+cu118
  • Datasets 2.18.0
  • Tokenizers 0.19.1