grammar-classifier – AI Model by dv347 | AlphaNeural AI

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dv347/
grammar-classifier

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text-classification

generated_from_trainer

microsoft/deberta-v3-large

text-embeddings-inference

endpoints_compatible

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grammar-classifier

This model is a fine-tuned version of microsoft/deberta-v3-large on the None dataset. It achieves the following results on the evaluation set:

Loss: 4.0967
Exact Match: 0.0
Micro F1: 0.3075
Macro F1: 0.0334
Hamming Accuracy: 0.8806
Avg Pred Positives: 34.0
Avg Gold Positives: 13.5736

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: 32
eval_batch_size: 64
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 64
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 0.2
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Exact Match	Micro F1	Macro F1	Hamming Accuracy	Avg Pred Positives	Avg Gold Positives
0.2930	0.5	164	0.5663	0.0	0.3139	0.0288	0.9041	25.0	13.5736
0.1928	1.0	328	0.2720	0.0	0.4392	0.0256	0.9460	13.0	13.5736
0.0751	1.5	492	0.0559	0.0	0.5244	0.0234	0.9628	8.0	13.5736
33.3599	2.0	656	13.5265	0.0	0.2931	0.0226	0.9114	21.0	13.5736
19.0859	2.5	820	10.5864	0.0	0.2214	0.0302	0.8376	44.0	13.5736
8.6145	3.0	984	4.0967	0.0	0.3075	0.0334	0.8806	34.0	13.5736

Framework versions

Transformers 5.2.0
Pytorch 2.10.0+cu128
Datasets 4.5.0
Tokenizers 0.22.2