README.md

metadata

license: mit
base_model: microsoft/deberta-v3-base
tags:
  - generated_from_trainer
metrics:
  - f1
  - accuracy
  - precision
  - recall
model-index:
  - name: 008-microsoft-deberta-v3-base-finetuned-yahoo-800_200
    results: []

008-microsoft-deberta-v3-base-finetuned-yahoo-800_200

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

• Loss: 1.1139
• F1: 0.6463
• Accuracy: 0.65
• Precision: 0.6514
• Recall: 0.65
• System Ram Used: 4.2190
• System Ram Total: 83.4807
• Gpu Ram Allocated: 2.0914
• Gpu Ram Cached: 24.6602
• Gpu Ram Total: 39.5640
• Gpu Utilization: 33
• Disk Space Used: 31.6928
• 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: 10

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
2.2992	0.52	13	2.3031	0.0182	0.1	0.01	0.1	3.9340	83.4807	2.0915	24.6484	39.5640	50	24.7853	78.1898
2.3096	1.04	26	2.2984	0.0182	0.1	0.01	0.1	4.1195	83.4807	2.0915	24.6602	39.5640	43	29.6206	78.1898
2.2906	1.56	39	2.2852	0.0648	0.145	0.0525	0.145	4.2050	83.4807	2.0915	24.6602	39.5640	51	29.6206	78.1898
2.2723	2.08	52	2.2198	0.1283	0.225	0.1625	0.225	4.2165	83.4807	2.0915	24.6602	39.5640	43	31.6924	78.1898
2.1387	2.6	65	2.0293	0.2580	0.335	0.2655	0.335	4.2218	83.4807	2.0916	24.6602	39.5640	56	31.6925	78.1898
1.9534	3.12	78	1.8757	0.3730	0.4	0.4419	0.4	4.2092	83.4807	2.0915	24.6602	39.5640	41	31.6925	78.1898
1.7689	3.64	91	1.7209	0.4443	0.48	0.5198	0.48	4.2303	83.4807	2.0915	24.6602	39.5640	46	31.6925	78.1898
1.6052	4.16	104	1.6318	0.5044	0.525	0.5139	0.525	4.2297	83.4807	2.0915	24.6602	39.5640	45	31.6926	78.1898
1.4606	4.68	117	1.4969	0.5539	0.575	0.5788	0.575	4.2315	83.4807	2.0915	24.6602	39.5640	47	31.6926	78.1898
1.2963	5.2	130	1.3920	0.6037	0.61	0.6063	0.61	4.2420	83.4807	2.0916	24.6602	39.5640	43	31.6926	78.1898
1.1948	5.72	143	1.3030	0.6251	0.63	0.6292	0.63	4.2687	83.4807	2.0915	24.6602	39.5640	48	31.6926	78.1898
1.0248	6.24	156	1.2568	0.6184	0.625	0.6354	0.625	4.2596	83.4807	2.0915	24.6602	39.5640	50	31.6927	78.1898
0.9509	6.76	169	1.1911	0.6448	0.65	0.6552	0.65	4.2625	83.4807	2.0915	24.6602	39.5640	44	31.6927	78.1898
0.9081	7.28	182	1.1784	0.6441	0.655	0.6450	0.655	4.1955	83.4807	2.0915	24.6602	39.5640	50	31.6927	78.1898
0.7629	7.8	195	1.1354	0.6598	0.655	0.6737	0.655	4.1868	83.4807	2.0915	24.6602	39.5640	44	31.6927	78.1898
0.7348	8.32	208	1.1369	0.6430	0.65	0.6483	0.65	4.2168	83.4807	2.0915	24.6602	39.5640	43	31.6927	78.1898
0.7443	8.84	221	1.1274	0.6531	0.66	0.6576	0.66	4.2273	83.4807	2.0915	24.6602	39.5640	51	31.6927	78.1898
0.5945	9.36	234	1.1228	0.6640	0.67	0.6694	0.67	4.1791	83.4807	2.0915	24.6602	39.5640	44	31.6928	78.1898
0.6885	9.88	247	1.1145	0.6463	0.65	0.6514	0.65	4.1849	83.4807	2.0915	24.6602	39.5640	48	31.6928	78.1898

Framework versions

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