metadata

license: mit
base_model: microsoft/xtremedistil-l6-h384-uncased
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: xtremedistil-l6-h384-uncased-v1.1
    results: []

xtremedistil-l6-h384-uncased-v1.1

This model is a fine-tuned version of microsoft/xtremedistil-l6-h384-uncased on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.5278
F1 Macro: 0.6999
F1 Micro: 0.7000
Accuracy Balanced: 0.7017
Accuracy: 0.7000
Precision Macro: 0.7009
Recall Macro: 0.7017
Precision Micro: 0.7000
Recall Micro: 0.7000

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: 16
eval_batch_size: 128
seed: 40
gradient_accumulation_steps: 2
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.06
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
0.6275	0.17	200	0.6177	0.3647	0.5463	0.5039	0.5463	0.6163	0.5039	0.5463	0.5463
0.5811	0.34	400	0.5808	0.5807	0.6194	0.5976	0.6194	0.6331	0.5976	0.6194	0.6194
0.5769	0.51	600	0.5680	0.6564	0.6585	0.6703	0.6585	0.6796	0.6703	0.6585	0.6585
0.5647	0.68	800	0.5634	0.6703	0.6728	0.6855	0.6728	0.6976	0.6855	0.6728	0.6728
0.5607	0.85	1000	0.5720	0.6176	0.6448	0.6264	0.6448	0.6569	0.6264	0.6448	0.6448
0.5645	1.02	1200	0.5617	0.6523	0.6601	0.6521	0.6601	0.6581	0.6521	0.6601	0.6601
0.5665	1.19	1400	0.5479	0.6802	0.6840	0.6986	0.6840	0.7172	0.6986	0.6840	0.6840
0.5432	1.35	1600	0.5540	0.6642	0.6665	0.6644	0.6665	0.6641	0.6644	0.6665	0.6665
0.5427	1.52	1800	0.5520	0.6533	0.6617	0.6532	0.6617	0.6601	0.6532	0.6617	0.6617
0.5453	1.69	2000	0.5487	0.6756	0.6781	0.6755	0.6781	0.6757	0.6755	0.6781	0.6781
0.5528	1.86	2200	0.5492	0.6720	0.6771	0.6713	0.6771	0.6747	0.6713	0.6771	0.6771
0.531	2.03	2400	0.5476	0.6799	0.6803	0.6882	0.6803	0.6911	0.6882	0.6803	0.6803
0.5199	2.2	2600	0.5454	0.6823	0.6824	0.6863	0.6824	0.6856	0.6863	0.6824	0.6824
0.535	2.37	2800	0.5441	0.6797	0.6803	0.6817	0.6803	0.6804	0.6817	0.6803	0.6803
0.5246	2.54	3000	0.5453	0.6746	0.6750	0.6771	0.6750	0.6759	0.6771	0.6750	0.6750
0.5405	2.71	3200	0.5408	0.6824	0.6861	0.6819	0.6861	0.6836	0.6819	0.6861	0.6861
0.5414	2.88	3400	0.5404	0.6826	0.6834	0.6841	0.6834	0.6828	0.6841	0.6834	0.6834

Framework versions

Transformers 4.33.3
Pytorch 2.5.1+cu121
Datasets 2.14.7
Tokenizers 0.13.3