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persuasive_essays_distilbert_cased – AI Model by przvl | AlphaNeural AI
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przvl
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persuasive_essays_distilbert_cased
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transformers
safetensors
distilbert
text-classification
generated_from_trainer
en
distilbert/distilbert-base-cased
finetune
apache-2.0
autotrain_compatible
endpoints_compatible
us
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persuasive_essays_distilbert_cased
Model description
This model is a fine-tuned version of
distilbert-base-cased
on the
emnlp2017-claim-identification/persuasive_essays
dataset. It achieves the following results on the evaluation set:
Loss: 0.4249
Accuracy: 0.8101
Macro F1: 0.7662
Claim F1: 0.665
Intended uses & limitations
Text classification for claims on full sentences. The model perfoms better at in-domain classification. Cross-domain classification is severely limited.
Training and evaluation data
Based on
Stab and Gurevych (2017)
persuasive essays corpus, preprocessed by [Daxenberger et al. (2017)]((
https://github.com/UKPLab/emnlp2017-claim-identification
).
Original dataset
docs: 402
tokens: 147,271
total instances: 7,116 (65 duplicates)
#claims: 2,108 (29.62%)
Trimmed datast used for training
total instances:
7051
(65 duplicates removed)
#claims:
2093
(29.68%)
train/test split: 80/20, stratified
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
learning_rate: 2e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 2
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
Macro F1
Claim F1
No log
1.0
353
0.4369
0.7931
0.7574
0.6644
0.4492
2.0
706
0.4249
0.8101
0.7662
0.665
Framework versions
Transformers 4.37.2
Pytorch 2.2.0
Datasets 2.17.0
Tokenizers 0.15.2
