README.md

metadata

library_name: transformers
language:
  - en
license: mit
base_model: microsoft/deberta-v3-small
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
  - matthews_correlation
model-index:
  - name: wordnet-network-predictor
    results: []

wordnet-network-predictor

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

• Loss: 0.0483
• Precision: 0.9808
• Recall: 0.9944
• F1: 0.9875
• Accuracy: 0.9874
• Matthews Correlation: 0.9749

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: 1e-05
• train_batch_size: 64
• eval_batch_size: 64
• seed: 42
• gradient_accumulation_steps: 5
• total_train_batch_size: 320
• 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
• num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy	Matthews Correlation
No log	0	0	0.7276	0.4368	0.7188	0.5434	0.3935	-0.2846
0.3924	1.0	2144	0.0697	0.9622	0.9893	0.9756	0.9751	0.9506
0.2978	2.0	4288	0.0522	0.9770	0.9883	0.9826	0.9825	0.9650
0.2273	3.0	6432	0.0534	0.9739	0.9932	0.9834	0.9832	0.9666
0.1735	4.0	8576	0.0483	0.9786	0.9925	0.9855	0.9853	0.9707
0.1457	5.0	10720	0.0463	0.9790	0.9935	0.9862	0.9860	0.9722
0.1212	6.0	12864	0.0456	0.9798	0.9940	0.9869	0.9867	0.9735
0.0956	7.0	15008	0.0483	0.9800	0.9945	0.9872	0.9870	0.9742
0.1035	8.0	17152	0.0462	0.9820	0.9937	0.9878	0.9877	0.9755
0.0810	9.0	19296	0.0482	0.9807	0.9942	0.9874	0.9873	0.9746
0.0831	10.0	21440	0.0483	0.9808	0.9944	0.9875	0.9874	0.9749

Framework versions

• Transformers 5.3.0
• Pytorch 2.10.0+cu128
• Datasets 4.5.0
• Tokenizers 0.22.2