learning_rate: 2e-05
train_batch_size: 16
eval_batch_size: 16
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: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1	Date Precision	Date Recall	Date F1-score	Habitat Precision	Habitat Recall	Habitat F1-score	Id Feature Precision	Id Feature Recall	Id Feature F1-score	Location Precision	Location Recall	Location F1-score	Organization Precision	Organization Recall	Organization F1-score	Species Precision	Species Recall	Species 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	19	1.3285	0.6067	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
No log	2.0	38	0.8474	0.7597	0.2115	0.2502	0.2281	0.0	0.0	0.0	0.1860	0.1684	0.1768	0.1429	0.1477	0.1453	0.3439	0.3803	0.3612	0.0	0.0	0.0	0.5964	0.8048	0.6851	0.4368	0.4404	0.4386	0.2115	0.2502	0.2281	0.3508	0.4404	0.3891
No log	3.0	57	0.6548	0.8122	0.3355	0.4133	0.3702	0.0	0.0	0.0	0.5221	0.6211	0.5673	0.125	0.1477	0.1354	0.5956	0.7676	0.6708	0.1	0.1077	0.1037	0.6703	0.8356	0.7439	0.5076	0.5983	0.5493	0.3355	0.4133	0.3702	0.4812	0.5983	0.5333
No log	4.0	76	0.5435	0.8410	0.4820	0.5110	0.4879	0.5	0.275	0.3548	0.5607	0.6316	0.5941	0.2079	0.2386	0.2222	0.6813	0.8732	0.7654	0.2188	0.2154	0.2171	0.7232	0.8322	0.7739	0.5825	0.6551	0.6167	0.4820	0.5110	0.4879	0.5730	0.6551	0.6080
No log	5.0	95	0.4966	0.8569	0.5571	0.5952	0.5709	0.6207	0.45	0.5217	0.5727	0.6632	0.6146	0.2135	0.2159	0.2147	0.7062	0.8803	0.7837	0.5152	0.5231	0.5191	0.7143	0.8390	0.7717	0.6192	0.6981	0.6562	0.5571	0.5952	0.5709	0.6099	0.6981	0.6489
No log	6.0	114	0.4664	0.8632	0.5565	0.6074	0.5767	0.5806	0.45	0.5070	0.5739	0.6947	0.6286	0.1875	0.2386	0.21	0.7022	0.8803	0.7813	0.5645	0.5385	0.5512	0.7300	0.8425	0.7822	0.6120	0.7078	0.6564	0.5565	0.6074	0.5767	0.6147	0.7078	0.6560
No log	7.0	133	0.4499	0.8696	0.5695	0.6272	0.5947	0.5	0.425	0.4595	0.5798	0.7263	0.6449	0.2277	0.2614	0.2434	0.7619	0.9014	0.8258	0.6094	0.6	0.6047	0.7381	0.8493	0.7898	0.6375	0.7258	0.6788	0.5695	0.6272	0.5947	0.6350	0.7258	0.6762
No log	8.0	152	0.4453	0.8688	0.5753	0.6279	0.5978	0.5455	0.45	0.4932	0.5862	0.7158	0.6445	0.2130	0.2614	0.2347	0.7560	0.8944	0.8194	0.6094	0.6	0.6047	0.7417	0.8459	0.7904	0.6350	0.7230	0.6762	0.5753	0.6279	0.5978	0.6368	0.7230	0.6760
No log	9.0	171	0.4459	0.8731	0.5746	0.6313	0.5996	0.5143	0.45	0.48	0.5776	0.7053	0.6351	0.2190	0.2614	0.2383	0.7619	0.9014	0.8258	0.6190	0.6	0.6094	0.7560	0.8699	0.8089	0.6428	0.7327	0.6848	0.5746	0.6313	0.5996	0.6425	0.7327	0.6836
No log	10.0	190	0.4451	0.8726	0.5728	0.6473	0.6063	0.5135	0.475	0.4935	0.5620	0.7158	0.6296	0.2202	0.2727	0.2437	0.7530	0.8803	0.8117	0.6471	0.6769	0.6617	0.7412	0.8630	0.7975	0.6326	0.7368	0.6807	0.5728	0.6473	0.6063	0.6353	0.7368	0.6816

Framework versions

Transformers 4.51.3
Pytorch 2.6.0+cu124
Datasets 3.6.0
Tokenizers 0.21.1

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

xlm-base-finetuned-ner-chim-v1

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

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