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xtremedistil-l12-h384-uncased-zeroshot-v1.1

Zero-Shot Classification
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text-classification
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metadata
license: mit
base_model: microsoft/xtremedistil-l12-h384-uncased
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: xtremedistil-l12-h384-uncased-zeroshot-v1.1-none
    results: []
pipeline_tag: zero-shot-classification

xtremedistil-l12-h384-uncased-zeroshot-v1.1-none

A slightly larger sibling to https://hf.co/MoritzLaurer/xtremedistil-l6-h256-zeroshot-v1.1-all-33

Model description

This model is a fine-tuned version of microsoft/xtremedistil-l12-h384-uncased on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2063
  • F1 Macro: 0.5570
  • F1 Micro: 0.6385
  • Accuracy Balanced: 0.6104
  • Accuracy: 0.6385
  • Precision Macro: 0.5705
  • Recall Macro: 0.6104
  • Precision Micro: 0.6385
  • Recall Micro: 0.6385

Training and evaluation data

See https://github.com/MoritzLaurer/zeroshot-classifier/blob/main/datasets_overview.csv

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 5e-05
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 80085
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 64
  • optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.04
  • num_epochs: 3

Training results

Training Loss Epoch Step Validation Loss F1 Macro F1 Micro Accuracy Balanced Accuracy Precision Macro Recall Macro Precision Micro Recall Micro
0.2756 0.32 5000 0.4155 0.8146 0.8255 0.8215 0.8255 0.8101 0.8215 0.8255 0.8255
0.2395 0.65 10000 0.4166 0.8182 0.8303 0.8222 0.8303 0.8151 0.8222 0.8303 0.8303
0.2464 0.97 15000 0.4114 0.8204 0.8325 0.8239 0.8325 0.8175 0.8239 0.8325 0.8325
0.2105 1.3 20000 0.4051 0.8236 0.8363 0.8254 0.8363 0.8219 0.8254 0.8363 0.8363
0.2267 1.62 25000 0.4030 0.8244 0.8373 0.8257 0.8373 0.8231 0.8257 0.8373 0.8373
0.2312 1.95 30000 0.4088 0.8233 0.836 0.8250 0.836 0.8217 0.8250 0.836 0.836
0.2241 2.27 35000 0.4061 0.8257 0.8375 0.8291 0.8375 0.8229 0.8291 0.8375 0.8375
0.2183 2.6 40000 0.4043 0.8259 0.838 0.8285 0.838 0.8235 0.8285 0.838 0.838
0.2285 2.92 45000 0.4041 0.8241 0.8365 0.8263 0.8365 0.8220 0.8263 0.8365 0.8365

Framework versions

  • Transformers 4.36.2
  • Pytorch 2.1.2+cu121
  • Datasets 2.16.1
  • Tokenizers 0.15.0