Samaksh Khatri
Saving model gmra_distilbert/distilbert-base-uncased-distilled-squad_07112024T120722
10fca1f verified
metadata
library_name: transformers
license: apache-2.0
base_model: distilbert/distilbert-base-uncased-distilled-squad
tags:
  - generated_from_trainer
metrics:
  - f1
model-index:
  - name: distilbert-base-uncased-distilled-squad_07112024T120722
    results: []

distilbert-base-uncased-distilled-squad_07112024T120722

This model is a fine-tuned version of distilbert/distilbert-base-uncased-distilled-squad on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.7930
  • F1: 0.7643
  • 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 1.0 121 1.1749 0.5577 0.0000
No log 2.0 242 0.9801 0.6593 0.0000
No log 3.0 363 0.9184 0.6951 0.0000
No log 4.0 484 0.8064 0.7480 0.0000
0.9941 5.0 605 0.7930 0.7643 0.0000
0.9941 6.0 726 0.8313 0.7793 0.0000
0.9941 7.0 847 0.8955 0.7814 0.0000
0.9941 8.0 968 0.9010 0.7963 0.0000
0.3299 9.0 1089 0.9855 0.7945 0.0000
0.3299 10.0 1210 0.9753 0.8036 0.0000
0.3299 11.0 1331 1.0115 0.8095 0.0000
0.3299 12.0 1452 1.0508 0.8052 7e-06
0.0991 13.0 1573 1.1102 0.8125 0.0000
0.0991 14.0 1694 1.1321 0.8146 0.0000
0.0991 15.0 1815 1.2166 0.8085 3e-06
0.0991 16.0 1936 1.1935 0.8109 0.0000
0.0349 17.0 2057 1.2277 0.8074 0.0000
0.0349 18.0 2178 1.2145 0.8091 5e-07
0.0349 19.0 2299 1.2306 0.8082 1e-07
0.0349 20.0 2420 1.2334 0.8080 0.0

Framework versions

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