README.md

metadata

license: mit
tags:
  - generated_from_trainer
model-index:
  - name: mlcovid19-classifier
    results: []

mlcovid19-classifier

This model is a fine-tuned version of oscarwu/mlcovid19-classifier on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.4565
• F1 Macro: 0.6596
• F1 Misinformation: 0.8776
• F1 Factual: 0.8823
• F1 Other: 0.2188
• Prec Macro: 0.7181
• Prec Misinformation: 0.834
• Prec Factual: 0.8952
• Prec Other: 0.4251

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-06
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• gradient_accumulation_steps: 32
• total_train_batch_size: 1024
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 2165
• num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	F1 Macro	F1 Misinformation	F1 Factual	F1 Other	Prec Macro	Prec Misinformation	Prec Factual	Prec Other
1.1986	0.94	16	1.0988	0.6120	0.8270	0.7623	0.2466	0.6567	0.7636	0.8578	0.3487
1.1885	1.94	32	1.0851	0.6163	0.8271	0.7645	0.2574	0.6588	0.7660	0.8557	0.3547
1.1538	2.94	48	1.0625	0.6198	0.8275	0.7683	0.2635	0.6593	0.7695	0.8521	0.3565
1.1386	3.94	64	1.0307	0.6235	0.8259	0.7722	0.2725	0.6564	0.7738	0.8452	0.3502
1.0935	4.94	80	0.9911	0.6276	0.8259	0.7797	0.2771	0.6549	0.7803	0.8392	0.3452
1.055	5.94	96	0.9445	0.6304	0.8271	0.7912	0.2730	0.6521	0.7893	0.8344	0.3327
0.9925	6.94	112	0.8945	0.6340	0.8270	0.8001	0.2749	0.6518	0.7976	0.8251	0.3327
0.9446	7.94	128	0.8448	0.6390	0.8303	0.8106	0.2760	0.6545	0.8088	0.8186	0.3360
0.8813	8.94	144	0.7970	0.6448	0.8355	0.8238	0.2752	0.6598	0.8185	0.8214	0.3395
0.8259	9.94	160	0.7475	0.6480	0.8405	0.8330	0.2704	0.6644	0.8243	0.8256	0.3434
0.7721	10.94	176	0.6971	0.6532	0.8483	0.8430	0.2684	0.6746	0.8281	0.8375	0.3583
0.7107	11.94	192	0.6542	0.6510	0.8527	0.8496	0.2507	0.6765	0.8290	0.8448	0.3557
0.6742	12.94	208	0.6126	0.6527	0.8554	0.8544	0.2484	0.6793	0.8298	0.8521	0.3560
0.6296	13.94	224	0.5735	0.6560	0.8603	0.8586	0.2491	0.6902	0.8298	0.8602	0.3804
0.5947	14.94	240	0.5416	0.6592	0.8641	0.8624	0.2512	0.6986	0.8299	0.8689	0.3970
0.5728	15.94	256	0.5164	0.6584	0.8678	0.8674	0.2402	0.7028	0.8312	0.8745	0.4026
0.5424	16.94	272	0.4950	0.6620	0.8711	0.8720	0.2428	0.7110	0.8315	0.8836	0.4178
0.5277	17.94	288	0.4798	0.6594	0.8727	0.8751	0.2305	0.7107	0.8316	0.8874	0.4130
0.5204	18.94	304	0.4679	0.6613	0.8749	0.8767	0.2323	0.7183	0.8335	0.8868	0.4346
0.5061	19.94	320	0.4565	0.6596	0.8776	0.8823	0.2188	0.7181	0.834	0.8952	0.4251

Framework versions

• Transformers 4.23.1
• Pytorch 1.12.1+cu113
• Datasets 2.6.1
• Tokenizers 0.13.1