README.md

metadata

base_model: nreimers/MiniLMv2-L6-H384-distilled-from-RoBERTa-Large
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - f1
model-index:
  - name: MiniLMv2-L6-H384-distilled-from-RoBERTa-Large-userflow-distil
    results: []

MiniLMv2-L6-H384-distilled-from-RoBERTa-Large-userflow-distil

This model is a fine-tuned version of nreimers/MiniLMv2-L6-H384-distilled-from-RoBERTa-Large on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.6738
• Accuracy: 0.7236
• F1: 0.7313

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: 7e-05
• train_batch_size: 10
• eval_batch_size: 10
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 8

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
No log	0.25	100	2.3745	0.3923	0.2210
No log	0.51	200	2.1198	0.4126	0.2567
No log	0.76	300	1.8704	0.4756	0.3979
No log	1.01	400	1.5780	0.5305	0.4551
2.1769	1.26	500	1.3717	0.5650	0.5037
2.1769	1.52	600	1.2590	0.5935	0.5543
2.1769	1.77	700	1.0973	0.6280	0.5804
2.1769	2.02	800	0.9814	0.6423	0.5978
2.1769	2.27	900	0.9589	0.6463	0.6152
0.9806	2.53	1000	0.9098	0.6565	0.6483
0.9806	2.78	1100	0.8747	0.6321	0.6194
0.9806	3.03	1200	0.8172	0.6931	0.6902
0.9806	3.28	1300	0.7862	0.7033	0.7017
0.9806	3.54	1400	0.7975	0.6890	0.6952
0.4166	3.79	1500	0.7674	0.6951	0.6913
0.4166	4.04	1600	0.7521	0.6911	0.6997
0.4166	4.29	1700	0.7944	0.6951	0.7055
0.4166	4.55	1800	0.7366	0.7093	0.7127
0.4166	4.8	1900	0.7412	0.6911	0.6944
0.2158	5.05	2000	0.7246	0.7012	0.7083
0.2158	5.3	2100	0.7097	0.7195	0.7253
0.2158	5.56	2200	0.6914	0.7134	0.7197
0.2158	5.81	2300	0.6875	0.7175	0.7266
0.2158	6.06	2400	0.6544	0.7236	0.7296
0.1423	6.31	2500	0.6738	0.7236	0.7313
0.1423	6.57	2600	0.6640	0.7175	0.7253
0.1423	6.82	2700	0.6617	0.7154	0.7233
0.1423	7.07	2800	0.6582	0.7154	0.7205
0.1423	7.32	2900	0.6678	0.7033	0.7093
0.1204	7.58	3000	0.6596	0.7154	0.7197
0.1204	7.83	3100	0.6598	0.7154	0.7217

Framework versions

• Transformers 4.37.0
• Pytorch 2.1.2
• Datasets 2.1.0
• Tokenizers 0.15.1