metadata

license: mit
base_model: microsoft/deberta-v3-large
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: checkpoints_28_9_microsoft_deberta_V5
    results: []

checkpoints_28_9_microsoft_deberta_V5

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: 0.6408
Map@3: 0.8542
Accuracy: 0.76

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: 4
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 32
total_train_batch_size: 128
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.2
num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss	Map@3	Accuracy
1.6111	0.05	25	1.6092	0.5092	0.325
1.6139	0.11	50	1.6085	0.7	0.575
1.6096	0.16	75	1.5867	0.7583	0.645
1.2905	0.21	100	1.1496	0.7767	0.66
1.0263	0.27	125	0.8628	0.8067	0.705
0.9475	0.32	150	0.7252	0.8458	0.75
0.841	0.37	175	0.7018	0.8492	0.76
0.8301	0.43	200	0.7137	0.8492	0.755
0.823	0.48	225	0.6633	0.8525	0.755
0.8263	0.53	250	0.6751	0.8608	0.765
0.7962	0.59	275	0.6704	0.8542	0.755
0.8013	0.64	300	0.6583	0.8525	0.755
0.789	0.69	325	0.6497	0.8533	0.76
0.7979	0.75	350	0.6512	0.8525	0.755
0.7751	0.8	375	0.6445	0.8583	0.765
0.7993	0.85	400	0.6424	0.8558	0.765
0.7685	0.91	425	0.6408	0.8542	0.76
0.7807	0.96	450	0.6408	0.8542	0.76

Framework versions

Transformers 4.32.1
Pytorch 2.0.0
Datasets 2.9.0
Tokenizers 0.13.3