learning_rate: 2e-05
train_batch_size: 64
eval_batch_size: 64
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1	Age Precision	Age Recall	Age F1-score	Date Precision	Date Recall	Date F1-score	Gender Precision	Gender Recall	Gender F1-score	Job Precision	Job Recall	Job F1-score	Location Precision	Location Recall	Location F1-score	Name Precision	Name Recall	Name F1-score	Organization Precision	Organization Recall	Organization F1-score	Patient Id Precision	Patient Id Recall	Patient Id F1-score	Symptom And Disease Precision	Symptom And Disease Recall	Symptom And Disease F1-score	Transportation Precision	Transportation Recall	Transportation F1-score	Micro avg Precision	Micro avg Recall	Micro avg F1-score	Macro avg Precision	Macro avg Recall	Macro avg F1-score	Weighted avg Precision	Weighted avg Recall	Weighted avg F1-score
No log	1.0	79	0.2398	0.9339	0.3077	0.3770	0.3371	0.0	0.0	0.0	0.9524	0.9788	0.9654	0.0	0.0	0.0	0.0	0.0	0.0	0.7503	0.9187	0.8260	0.0	0.0	0.0	0.1895	0.2062	0.1975	0.6422	0.9786	0.7755	0.5427	0.6875	0.6066	0.0	0.0	0.0	0.6897	0.7329	0.7107	0.3077	0.3770	0.3371	0.5929	0.7329	0.6529
No log	2.0	158	0.1244	0.9685	0.7778	0.7950	0.7848	0.8416	0.9674	0.9001	0.9797	0.9927	0.9862	0.8705	0.9459	0.9066	0.0	0.0	0.0	0.8883	0.9385	0.9127	0.9061	0.7893	0.8437	0.7564	0.8093	0.7820	0.9246	0.9845	0.9536	0.8207	0.8380	0.8293	0.7904	0.6839	0.7333	0.8876	0.9155	0.9013	0.7778	0.7950	0.7848	0.8749	0.9155	0.8943
No log	3.0	237	0.1018	0.9729	0.7973	0.8606	0.8271	0.8754	0.9656	0.9183	0.9838	0.9933	0.9886	0.8905	0.9502	0.9194	0.4241	0.4682	0.4451	0.9130	0.9379	0.9252	0.7326	0.8270	0.7770	0.7680	0.8846	0.8222	0.9709	0.9820	0.9764	0.8104	0.8618	0.8353	0.6043	0.7358	0.6636	0.8899	0.9310	0.9100	0.7973	0.8606	0.8271	0.8935	0.9310	0.9115
No log	4.0	316	0.0947	0.9749	0.8018	0.8746	0.8355	0.8807	0.9639	0.9204	0.9850	0.9940	0.9895	0.8956	0.9654	0.9292	0.4948	0.5491	0.5205	0.9252	0.9363	0.9307	0.6864	0.8396	0.7553	0.7847	0.8936	0.8357	0.9729	0.9830	0.9779	0.8110	0.8803	0.8442	0.5813	0.7409	0.6515	0.8950	0.9352	0.9146	0.8018	0.8746	0.8355	0.8997	0.9352	0.9165
No log	5.0	395	0.0929	0.9759	0.8147	0.8899	0.8496	0.8849	0.9639	0.9227	0.9833	0.9940	0.9886	0.8909	0.9719	0.9296	0.5566	0.6821	0.6130	0.9246	0.9417	0.9331	0.7026	0.8396	0.7650	0.8061	0.8898	0.8459	0.9691	0.9840	0.9765	0.8203	0.8759	0.8472	0.6083	0.7565	0.6744	0.8986	0.9392	0.9184	0.8147	0.8899	0.8496	0.9026	0.9392	0.9201

Framework versions

Transformers 4.51.3
Pytorch 2.6.0+cu124
Datasets 3.6.0
Tokenizers 0.21.1

quanxuantruong/xlm-base-finetuned-ner-covidmed-v1

xlm-base-finetuned-ner-covidmed-v1

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

quanxuantruong/
xlm-base-finetuned-ner-covidmed-v1