finbert-ft-icar-a-v0.11-aps

This model is a fine-tuned version of project-aps/finbert-finetune on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 1.2343
• Accuracy: 0.8904
• Precision: 0.8787
• Recall: 0.8542
• F1: 0.8647

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: 3e-06
• train_batch_size: 1
• eval_batch_size: 1
• seed: 42
• gradient_accumulation_steps: 3
• total_train_batch_size: 3
• 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: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
3.6033	1.0	704	1.0430	0.8034	0.8146	0.7110	0.7320
1.9526	2.0	1408	0.9009	0.8261	0.8087	0.7546	0.7711
1.4631	3.0	2112	0.9722	0.8299	0.8069	0.7731	0.7824
1.0839	4.0	2816	0.8984	0.8526	0.8544	0.7870	0.8080
0.8472	5.0	3520	0.9364	0.8526	0.8534	0.7901	0.8112
0.6508	6.0	4224	0.8462	0.8677	0.8448	0.8274	0.8350
0.4698	7.0	4928	0.9436	0.8658	0.8570	0.8159	0.8313
0.3059	8.0	5632	0.9649	0.8620	0.8582	0.8190	0.8349
0.2214	9.0	6336	1.0001	0.8733	0.8521	0.8416	0.8466
0.1601	10.0	7040	1.0287	0.8620	0.8391	0.8248	0.8312
0.1301	11.0	7744	1.0760	0.8639	0.8366	0.8317	0.8340
0.1296	12.0	8448	1.1559	0.8677	0.8583	0.8250	0.8383
0.077	13.0	9152	1.1705	0.8601	0.8339	0.8315	0.8326
0.094	14.0	9856	1.1400	0.8752	0.8594	0.8409	0.8490
0.0663	15.0	10560	1.2298	0.8696	0.8565	0.8218	0.8345
0.065	16.0	11264	1.2036	0.8696	0.8429	0.8365	0.8394
0.0848	17.0	11968	1.1844	0.8752	0.8537	0.8372	0.8443
0.0742	18.0	12672	1.2367	0.8715	0.8512	0.8328	0.8403
0.0917	19.0	13376	1.2492	0.8715	0.8618	0.8261	0.8392
0.0501	20.0	14080	1.2344	0.8601	0.8325	0.8239	0.8275
0.0432	21.0	14784	1.2575	0.8752	0.8497	0.8483	0.8490
0.0407	22.0	15488	1.2300	0.8771	0.8603	0.8392	0.8484
0.0641	23.0	16192	1.2257	0.8828	0.8753	0.8400	0.8538
0.0561	24.0	16896	1.2529	0.8828	0.8718	0.8509	0.8601
0.0455	25.0	17600	1.3678	0.8639	0.8450	0.8198	0.8294
0.0374	26.0	18304	1.3127	0.8771	0.8571	0.8470	0.8517
0.0439	27.0	19008	1.2788	0.8828	0.8632	0.8552	0.8590
0.0403	28.0	19712	1.2715	0.8771	0.8572	0.8435	0.8498
0.0266	29.0	20416	1.2829	0.8752	0.8562	0.8374	0.8456
0.03	30.0	21120	1.3335	0.8733	0.8581	0.8387	0.8474
0.0455	31.0	21824	1.3037	0.8752	0.8580	0.8391	0.8471
0.0484	32.0	22528	1.2934	0.8771	0.8517	0.8541	0.8526
0.0413	33.0	23232	1.2343	0.8904	0.8787	0.8542	0.8647
0.0438	34.0	23936	1.3027	0.8847	0.8778	0.8407	0.8554
0.0319	35.0	24640	1.2800	0.8752	0.8497	0.8506	0.8501
0.0311	36.0	25344	1.2994	0.8790	0.8644	0.8368	0.8480
0.0368	37.0	26048	1.3318	0.8715	0.8594	0.8214	0.8359
0.0308	38.0	26752	1.2342	0.8885	0.8708	0.8657	0.8682
0.0412	39.0	27456	1.2783	0.8790	0.8691	0.8411	0.8528
0.0387	40.0	28160	1.2715	0.8828	0.8624	0.8539	0.8578
0.031	41.0	28864	1.2464	0.8828	0.8607	0.8533	0.8568
0.0289	42.0	29568	1.2761	0.8809	0.8569	0.8569	0.8568
0.0331	43.0	30272	1.2748	0.8809	0.8569	0.8569	0.8568
0.0276	44.0	30976	1.2644	0.8809	0.8642	0.8466	0.8544
0.0295	45.0	31680	1.2594	0.8828	0.8646	0.8528	0.8582
0.0227	46.0	32384	1.2658	0.8847	0.8671	0.8528	0.8592
0.0241	47.0	33088	1.2809	0.8828	0.8678	0.8485	0.8570
0.0314	48.0	33792	1.2853	0.8828	0.8678	0.8485	0.8570

Framework versions

• Transformers 4.52.4
• Pytorch 2.6.0+cu124
• Datasets 4.4.2
• Tokenizers 0.21.2