016-microsoft-MiniLM-finetuned-yahoo-80_20

This model is a fine-tuned version of microsoft/MiniLM-L12-H384-uncased on the None dataset. It achieves the following results on the evaluation set:

• Loss: 1.6861
• F1: 0.4657
• Accuracy: 0.5
• Precision: 0.5267
• Recall: 0.5
• System Ram Used: 3.8760
• System Ram Total: 83.4807
• Gpu Ram Allocated: 0.3991
• Gpu Ram Cached: 1.9316
• Gpu Ram Total: 39.5640
• Gpu Utilization: 35
• Disk Space Used: 24.5397
• Disk Space Total: 78.1898

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: 32
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 100

Training results

Training Loss	Epoch	Step	Validation Loss	F1	Accuracy	Precision	Recall	System Ram Used	System Ram Total	Gpu Ram Allocated	Gpu Ram Cached	Gpu Ram Total	Gpu Utilization	Disk Space Used	Disk Space Total
2.3016	5.0	15	2.3016	0.0182	0.1	0.01	0.1	3.8589	83.4807	0.3990	1.9219	39.5640	38	24.5396	78.1898
2.2944	10.0	30	2.2979	0.0182	0.1	0.01	0.1	3.8753	83.4807	0.3991	1.9219	39.5640	36	24.5396	78.1898
2.2693	15.0	45	2.2696	0.2030	0.25	0.2472	0.25	3.8814	83.4807	0.3990	1.9316	39.5640	35	24.5396	78.1898
2.1627	20.0	60	2.2004	0.1808	0.25	0.1932	0.25	3.8785	83.4807	0.3990	1.9316	39.5640	39	24.5396	78.1898
1.9951	25.0	75	2.0773	0.2649	0.35	0.2922	0.35	3.8796	83.4807	0.3990	1.9316	39.5640	38	24.5396	78.1898
1.8128	30.0	90	1.9729	0.3619	0.45	0.3533	0.45	3.8802	83.4807	0.3990	1.9316	39.5640	36	24.5396	78.1898
1.6805	35.0	105	1.9061	0.4405	0.5	0.465	0.5	3.8803	83.4807	0.3990	1.9316	39.5640	37	24.5396	78.1898
1.5773	40.0	120	1.8512	0.3824	0.45	0.3767	0.45	3.8846	83.4807	0.3990	1.9316	39.5640	38	24.5396	78.1898
1.4916	45.0	135	1.8222	0.5190	0.55	0.5600	0.55	3.8846	83.4807	0.3991	1.9316	39.5640	40	24.5397	78.1898
1.4142	50.0	150	1.8056	0.4657	0.5	0.5267	0.5	3.8850	83.4807	0.3990	1.9316	39.5640	38	24.5397	78.1898
1.3555	55.0	165	1.7700	0.4657	0.5	0.5267	0.5	3.8850	83.4807	0.3991	1.9316	39.5640	41	24.5397	78.1898
1.3029	60.0	180	1.7568	0.4657	0.5	0.5267	0.5	3.8795	83.4807	0.3991	1.9316	39.5640	35	24.5397	78.1898
1.2572	65.0	195	1.7462	0.4371	0.45	0.5067	0.45	3.8802	83.4807	0.3991	1.9316	39.5640	40	24.5397	78.1898
1.2207	70.0	210	1.7215	0.4371	0.45	0.5067	0.45	3.8880	83.4807	0.3990	1.9316	39.5640	37	24.5397	78.1898
1.1915	75.0	225	1.7103	0.4657	0.5	0.5267	0.5	3.8760	83.4807	0.3991	1.9316	39.5640	39	24.5397	78.1898
1.1649	80.0	240	1.7069	0.4371	0.45	0.5067	0.45	3.8761	83.4807	0.3990	1.9316	39.5640	40	24.5397	78.1898
1.1484	85.0	255	1.6911	0.4657	0.5	0.5267	0.5	3.8747	83.4807	0.3991	1.9316	39.5640	35	24.5397	78.1898
1.135	90.0	270	1.6888	0.4657	0.5	0.5267	0.5	3.8753	83.4807	0.3990	1.9316	39.5640	37	24.5397	78.1898
1.1226	95.0	285	1.6860	0.4657	0.5	0.5267	0.5	3.8755	83.4807	0.3990	1.9316	39.5640	39	24.5397	78.1898
1.1217	100.0	300	1.6861	0.4657	0.5	0.5267	0.5	3.8755	83.4807	0.3990	1.9316	39.5640	38	24.5397	78.1898

Framework versions

• Transformers 4.31.0
• Pytorch 2.0.1+cu118
• Datasets 2.13.1
• Tokenizers 0.13.3