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ferrazzipietro/
crfTask-unsup-Qwen3-8B-datav3-only_mask

Name: crfTask-unsup-Qwen3-8B-datav3-only_mask
Availability: InStock

ferrazzipietro/unsup-Qwen3-8B-datav3-only_mask

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crfTask-unsup-Qwen3-8B-datav3-only_mask

This model is a fine-tuned version of ferrazzipietro/unsup-Qwen3-8B-datav3-only_mask on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.7805
F1 Micro: 0.9487
F1 Macro: 0.3724
F1 Weighted: 0.9523
F1 Per Item: {}

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: 0.0003
train_batch_size: 16
eval_batch_size: 64
seed: 42
distributed_type: multi-GPU
gradient_accumulation_steps: 4
total_train_batch_size: 64
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-07 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss	F1 Micro	F1 Macro	F1 Weighted	F1 Per Item
12.125	0.0024	2	3.1594	0.0	0.0	0.0	{}
12.125	0.0047	4	3.1594	0.0	0.0	0.0	{}
12.0156	0.0071	6	3.1562	0.0	0.0	0.0	{}
12.0156	0.0095	8	3.1453	0.0	0.0	0.0	{}
12.0469	0.0118	10	3.1219	0.0	0.0	0.0	{}
12.0469	0.0142	12	3.0875	0.0	0.0	0.0	{}
12.0469	0.0166	14	3.0422	0.0	0.0	0.0	{}
11.9062	0.0189	16	2.9750	0.0	0.0	0.0	{}
11.9062	0.0213	18	2.8984	0.0	0.0	0.0	{}
11.4313	0.0236	20	2.8203	0.0	0.0	0.0	{}
11.4313	0.0260	22	2.7359	0.0	0.0	0.0	{}
11.4313	0.0284	24	2.6453	0.0	0.0	0.0	{}
10.8812	0.0307	26	2.5500	0.0	0.0	0.0	{}
10.8812	0.0331	28	2.4516	0.0	0.0	0.0	{}
10.1656	0.0355	30	2.3422	0.0	0.0	0.0	{}
10.1656	0.0378	32	2.2156	0.0	0.0	0.0	{}
10.1656	0.0402	34	2.0938	0.0	0.0	0.0	{}
9.1875	0.0426	36	1.9828	0.0	0.0	0.0	{}
9.1875	0.0449	38	1.8937	0.0	0.0	0.0	{}
8.4938	0.0473	40	1.8094	0.0	0.0	0.0	{}
8.4938	0.0497	42	1.7359	0.0	0.0	0.0	{}
8.4938	0.0520	44	1.6797	0.0	0.0	0.0	{}
7.6047	0.0544	46	1.6227	0.0	0.0	0.0	{}
7.6047	0.0568	48	1.5500	0.2483	0.0016	0.2713	{}
7.1406	0.0591	50	1.4883	0.545	0.0358	0.4422	{}
7.1406	0.0615	52	1.4555	0.4017	0.0384	0.3055	{}
7.1406	0.0638	54	1.4352	0.405	0.0528	0.3128	{}
6.8438	0.0662	56	1.4164	0.54	0.0454	0.4389	{}
6.8438	0.0686	58	1.4000	0.565	0.0417	0.4231	{}
6.4906	0.0709	60	1.3797	0.54	0.1047	0.4738	{}
6.4906	0.0733	62	1.3641	0.45	0.1093	0.3839	{}
6.4906	0.0757	64	1.3531	0.6417	0.1411	0.6523	{}
6.4125	0.0780	66	1.3367	0.6083	0.1296	0.5822	{}
6.4125	0.0804	68	1.3188	0.6867	0.1699	0.7036	{}
6.2766	0.0828	70	1.3016	0.595	0.1761	0.6159	{}
6.2766	0.0851	72	1.2891	0.5883	0.1877	0.6066	{}
6.2766	0.0875	74	1.2758	0.7817	0.1865	0.7811	{}
6.1063	0.0899	76	1.2680	0.8	0.1935	0.7905	{}
6.1063	0.0922	78	1.2586	0.6883	0.1914	0.6979	{}
5.9797	0.0946	80	1.2461	0.805	0.1957	0.7990	{}
5.9797	0.0970	82	1.2383	0.7967	0.1988	0.7766	{}
5.9797	0.0993	84	1.2328	0.79	0.1888	0.7900	{}
6.0141	0.1017	86	1.2258	0.84	0.1958	0.8374	{}
6.0141	0.1040	88	1.2156	0.8417	0.1943	0.8336	{}
5.9047	0.1064	90	1.2078	0.7967	0.1794	0.8059	{}
5.9047	0.1088	92	1.2031	0.8517	0.1911	0.8478	{}
5.9047	0.1111	94	1.1992	0.8533	0.1945	0.8502	{}
5.9266	0.1135	96	1.1938	0.8617	0.1898	0.8534	{}
5.9266	0.1159	98	1.1867	0.8517	0.1541	0.8381	{}
5.875	0.1182	100	1.1820	0.82	0.1431	0.8129	{}
5.875	0.1206	102	1.1789	0.8767	0.1614	0.8660	{}
5.875	0.1230	104	1.1766	0.8383	0.1529	0.8196	{}
5.9156	0.1253	106	1.1727	0.8867	0.2252	0.8834	{}
5.9156	0.1277	108	1.1688	0.8917	0.2410	0.8907	{}
5.6406	0.1301	110	1.1648	0.8983	0.2780	0.9000	{}
5.6406	0.1324	112	1.1602	0.8933	0.2764	0.8926	{}
5.6406	0.1348	114	1.1594	0.8867	0.2743	0.8848	{}
5.6391	0.1372	116	1.1539	0.9017	0.3564	0.8998	{}
5.6391	0.1395	118	1.1523	0.9017	0.4095	0.9043	{}
5.7266	0.1419	120	1.1461	0.915	0.5326	0.9181	{}
5.7266	0.1443	122	1.1406	0.9083	0.5112	0.9134	{}
5.7266	0.1466	124	1.1383	0.9167	0.4491	0.9197	{}
5.5047	0.1490	126	1.1367	0.9083	0.5326	0.9125	{}
5.5047	0.1513	128	1.1320	0.9117	0.5187	0.9166	{}
5.5563	0.1537	130	1.1297	0.9167	0.3821	0.9161	{}
5.5563	0.1561	132	1.1266	0.925	0.3932	0.9250	{}
5.5563	0.1584	134	1.1227	0.915	0.4787	0.9169	{}
5.5938	0.1608	136	1.1203	0.9233	0.5744	0.9260	{}
5.5938	0.1632	138	1.1164	0.9267	0.4471	0.9293	{}
5.7047	0.1655	140	1.1141	0.9067	0.4412	0.9091	{}
5.7047	0.1679	142	1.1117	0.9283	0.4948	0.9313	{}
5.7047	0.1703	144	1.1102	0.9183	0.5128	0.9225	{}
5.5187	0.1726	146	1.1063	0.92	0.5121	0.9238	{}
5.5187	0.1750	148	1.1031	0.9367	0.5517	0.9370	{}
5.4297	0.1774	150	1.1023	0.9317	0.5106	0.9302	{}
5.4297	0.1797	152	1.0984	0.9383	0.5486	0.9374	{}
5.4297	0.1821	154	1.0969	0.91	0.5255	0.9125	{}
5.2875	0.1845	156	1.0938	0.9183	0.5293	0.9223	{}
5.2875	0.1868	158	1.0914	0.9367	0.5862	0.9393	{}
5.5422	0.1892	160	1.0898	0.905	0.5550	0.9048	{}
5.5422	0.1915	162	1.0891	0.9317	0.5823	0.9368	{}
5.5422	0.1939	164	1.0883	0.9233	0.5687	0.9297	{}
5.4562	0.1963	166	1.0852	0.8933	0.4419	0.9022	{}
5.4562	0.1986	168	1.0820	0.925	0.5811	0.9318	{}
5.2938	0.2010	170	1.0805	0.9367	0.5698	0.9397	{}
5.2938	0.2034	172	1.0789	0.9267	0.5340	0.9262	{}
5.2938	0.2057	174	1.0734	0.9383	0.4437	0.9395	{}
5.3094	0.2081	176	1.0742	0.9333	0.4363	0.9349	{}
5.3094	0.2105	178	1.0734	0.9283	0.5599	0.9294	{}
5.3094	0.2128	180	1.0703	0.935	0.5422	0.9350	{}
5.3094	0.2152	182	1.0672	0.9367	0.5423	0.9372	{}
5.3094	0.2176	184	1.0672	0.9317	0.5405	0.9322	{}
5.05	0.2199	186	1.0645	0.9417	0.5372	0.9417	{}
5.05	0.2223	188	1.0629	0.94	0.5930	0.9439	{}
5.1781	0.2247	190	1.0613	0.925	0.5750	0.9315	{}
5.1781	0.2270	192	1.0605	0.92	0.5674	0.9253	{}
5.1781	0.2294	194	1.0566	0.93	0.5731	0.9340	{}
5.1781	0.2317	196	1.0543	0.9317	0.5573	0.9338	{}
5.1781	0.2341	198	1.0535	0.935	0.5301	0.9350	{}
5.2203	0.2365	200	1.0496	0.9433	0.5040	0.9442	{}
5.2203	0.2388	202	1.0488	0.9433	0.5253	0.9443	{}
5.2203	0.2412	204	1.0473	0.935	0.5120	0.9362	{}
5.4203	0.2436	206	1.0441	0.9267	0.4977	0.9276	{}
5.4203	0.2459	208	1.0434	0.9267	0.4828	0.9303	{}
5.1797	0.2483	210	1.0410	0.9217	0.4842	0.9251	{}
5.1797	0.2507	212	1.0395	0.9267	0.5130	0.9278	{}
5.1797	0.2530	214	1.0379	0.9267	0.4795	0.9263	{}
5.0414	0.2554	216	1.0371	0.9317	0.4836	0.9306	{}
5.0414	0.2578	218	1.0344	0.9383	0.4954	0.9372	{}
5.1375	0.2601	220	1.0336	0.9433	0.5649	0.9432	{}
5.1375	0.2625	222	1.0312	0.945	0.5716	0.9445	{}
5.1375	0.2649	224	1.0305	0.9383	0.5376	0.9391	{}
5.0016	0.2672	226	1.0289	0.9383	0.5734	0.9398	{}
5.0016	0.2696	228	1.0281	0.9367	0.5724	0.9400	{}
5.1297	0.2719	230	1.0262	0.9367	0.5721	0.9388	{}
5.1297	0.2743	232	1.0242	0.9367	0.5690	0.9393	{}
5.1297	0.2767	234	1.0234	0.94	0.5681	0.9426	{}
5.175	0.2790	236	1.0211	0.9383	0.5686	0.9417	{}
5.175	0.2814	238	1.0180	0.9317	0.5656	0.9355	{}
4.9844	0.2838	240	1.0172	0.9367	0.5886	0.9409	{}
4.9844	0.2861	242	1.0156	0.935	0.5337	0.9401	{}
4.9844	0.2885	244	1.0125	0.9367	0.5727	0.9385	{}
5.0125	0.2909	246	1.0117	0.9367	0.5717	0.9365	{}
5.0125	0.2932	248	1.0102	0.9417	0.5395	0.9408	{}
4.9742	0.2956	250	1.0094	0.94	0.5687	0.9397	{}
4.9742	0.2980	252	1.0086	0.9383	0.5447	0.9390	{}
4.9742	0.3003	254	1.0078	0.9317	0.5598	0.9334	{}
5.0484	0.3027	256	1.0066	0.9367	0.5735	0.9387	{}
5.0484	0.3051	258	1.0035	0.9383	0.5410	0.9403	{}
4.9453	0.3074	260	1.0027	0.9367	0.5295	0.9379	{}
4.9453	0.3098	262	1.0027	0.9383	0.5759	0.9367	{}
4.9453	0.3121	264	1.0051	0.95	0.5777	0.9486	{}
4.9313	0.3145	266	1.0012	0.9417	0.6185	0.9432	{}
4.9313	0.3169	268	0.9988	0.945	0.7734	0.9467	{}
4.875	0.3192	270	0.9988	0.945	0.5622	0.9464	{}
4.875	0.3216	272	0.9973	0.945	0.5451	0.9452	{}
4.875	0.3240	274	0.9949	0.95	0.5670	0.9522	{}
4.7617	0.3263	276	0.9937	0.945	0.5642	0.9475	{}
4.7617	0.3287	278	0.9918	0.9467	0.5632	0.9484	{}
4.95	0.3311	280	0.9902	0.9467	0.5458	0.9478	{}
4.95	0.3334	282	0.9887	0.9467	0.5447	0.9480	{}
4.95	0.3358	284	0.9859	0.9417	0.5446	0.9431	{}
4.9297	0.3382	286	0.9859	0.9433	0.5471	0.9444	{}
4.9297	0.3405	288	0.9844	0.945	0.5491	0.9457	{}
4.7609	0.3429	290	0.9840	0.9383	0.5076	0.9409	{}
4.7609	0.3453	292	0.9832	0.9417	0.5422	0.9435	{}
4.7609	0.3476	294	0.9812	0.94	0.5437	0.9412	{}
4.7859	0.3500	296	0.9809	0.9433	0.5483	0.9437	{}
4.7859	0.3523	298	0.9793	0.9417	0.5445	0.9411	{}
4.818	0.3547	300	0.9750	0.945	0.5467	0.9447	{}
4.818	0.3571	302	0.9746	0.9417	0.6402	0.9431	{}
4.818	0.3594	304	0.9746	0.9467	0.6436	0.9488	{}
4.7836	0.3618	306	0.9750	0.9433	0.6394	0.9446	{}
4.7836	0.3642	308	0.9723	0.9367	0.6321	0.9376	{}
4.7094	0.3665	310	0.9734	0.9383	0.5400	0.9401	{}
4.7094	0.3689	312	0.9711	0.9367	0.5432	0.9381	{}
4.7094	0.3713	314	0.9680	0.9417	0.6429	0.9436	{}
4.7289	0.3736	316	0.9660	0.945	0.6458	0.9466	{}
4.7289	0.3760	318	0.9637	0.9383	0.5995	0.9412	{}
4.7078	0.3784	320	0.9633	0.94	0.5970	0.9422	{}
4.7078	0.3807	322	0.9598	0.9383	0.6254	0.9400	{}
4.7078	0.3831	324	0.9578	0.9417	0.6290	0.9428	{}
4.6703	0.3855	326	0.9566	0.945	0.6286	0.9466	{}
4.6703	0.3878	328	0.9559	0.9433	0.6345	0.9440	{}
4.5773	0.3902	330	0.9527	0.9467	0.6416	0.9466	{}
4.5773	0.3926	332	0.9527	0.945	0.6373	0.9445	{}
4.5773	0.3949	334	0.9492	0.9483	0.6829	0.9467	{}
4.6508	0.3973	336	0.9457	0.9417	0.5606	0.9402	{}
4.6508	0.3996	338	0.9453	0.9317	0.5351	0.9283	{}
4.6211	0.4020	340	0.9434	0.9383	0.6442	0.9365	{}
4.6211	0.4044	342	0.9402	0.945	0.6182	0.9452	{}
4.6211	0.4067	344	0.9398	0.9383	0.6224	0.9387	{}
4.6508	0.4091	346	0.9391	0.9433	0.6338	0.9437	{}
4.6508	0.4115	348	0.9371	0.9517	0.6456	0.9515	{}
4.4875	0.4138	350	0.9355	0.9467	0.6450	0.9470	{}
4.4875	0.4162	352	0.9328	0.9467	0.6450	0.9470	{}
4.4875	0.4186	354	0.9336	0.9483	0.6465	0.9495	{}
4.6273	0.4209	356	0.9312	0.9483	0.6470	0.9505	{}
4.6273	0.4233	358	0.9301	0.9483	0.6445	0.9507	{}
4.6984	0.4257	360	0.9289	0.945	0.6405	0.9485	{}
4.6984	0.4280	362	0.9258	0.945	0.6405	0.9484	{}
4.6984	0.4304	364	0.9250	0.9383	0.6366	0.9424	{}
4.5977	0.4328	366	0.9219	0.94	0.6369	0.9440	{}
4.5977	0.4351	368	0.9219	0.94	0.6346	0.9451	{}
4.3398	0.4375	370	0.9191	0.9383	0.6306	0.9444	{}
4.3398	0.4398	372	0.9184	0.9367	0.6305	0.9426	{}
4.3398	0.4422	374	0.9164	0.9367	0.6341	0.9410	{}
4.3844	0.4446	376	0.9148	0.9417	0.6814	0.9437	{}
4.3844	0.4469	378	0.9145	0.9433	0.6846	0.9439	{}
4.4781	0.4493	380	0.9129	0.945	0.6848	0.9456	{}
4.4781	0.4517	382	0.9090	0.95	0.6883	0.9492	{}
4.4781	0.4540	384	0.9082	0.9517	0.7485	0.9522	{}
4.475	0.4564	386	0.9070	0.9517	0.7428	0.9528	{}
4.475	0.4588	388	0.9059	0.9417	0.7242	0.9440	{}
4.4383	0.4611	390	0.9039	0.9417	0.7141	0.9439	{}
4.4383	0.4635	392	0.9039	0.9517	0.7200	0.9529	{}
4.4383	0.4659	394	0.9031	0.95	0.7223	0.9509	{}
4.4836	0.4682	396	0.9023	0.9483	0.6040	0.9485	{}
4.4836	0.4706	398	0.9004	0.9533	0.7228	0.9542	{}
4.0969	0.4730	400	0.8992	0.9517	0.7206	0.9525	{}
4.0969	0.4753	402	0.8980	0.9517	0.6929	0.9534	{}
4.0969	0.4777	404	0.8969	0.9567	0.7067	0.9579	{}
4.2711	0.4800	406	0.8965	0.9533	0.6073	0.9545	{}
4.2711	0.4824	408	0.8961	0.95	0.5482	0.9502	{}
4.4742	0.4848	410	0.8945	0.945	0.5475	0.9458	{}
4.4742	0.4871	412	0.8945	0.9483	0.6445	0.9493	{}
4.4742	0.4895	414	0.8918	0.9467	0.6421	0.9494	{}
4.5086	0.4919	416	0.8898	0.945	0.7463	0.9485	{}
4.5086	0.4942	418	0.8902	0.9483	0.7480	0.9516	{}
4.0836	0.4966	420	0.8879	0.945	0.7479	0.9478	{}
4.0836	0.4990	422	0.8871	0.9533	0.6471	0.9539	{}
4.0836	0.5013	424	0.8867	0.9483	0.5983	0.9492	{}
4.3781	0.5037	426	0.8852	0.945	0.5493	0.9446	{}
4.3781	0.5061	428	0.8844	0.9467	0.5495	0.9463	{}
4.2242	0.5084	430	0.8824	0.945	0.5492	0.9447	{}
4.2242	0.5108	432	0.8801	0.9467	0.5495	0.9463	{}
4.2242	0.5132	434	0.8789	0.9517	0.7711	0.9514	{}
4.2578	0.5155	436	0.8770	0.9533	0.7736	0.9532	{}
4.2578	0.5179	438	0.8766	0.9533	0.7736	0.9532	{}
4.1828	0.5202	440	0.8746	0.9517	0.7820	0.9514	{}
4.1828	0.5226	442	0.8730	0.9533	0.7466	0.9533	{}
4.1828	0.5250	444	0.8719	0.9567	0.8111	0.9570	{}
3.9883	0.5273	446	0.8695	0.9533	0.7546	0.9556	{}
3.9883	0.5297	448	0.8684	0.9533	0.7553	0.9558	{}
4.3484	0.5321	450	0.8680	0.95	0.7517	0.9526	{}
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3.243	0.9696	820	0.7223	0.95	0.7523	0.9515	{}
3.243	0.9719	822	0.7227	0.9517	0.7526	0.9532	{}
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3.2125	0.9837	832	0.7219	0.9517	0.7526	0.9532	{}
3.2125	0.9861	834	0.7219	0.9517	0.7538	0.9530	{}
3.4047	0.9885	836	0.7219	0.9517	0.7538	0.9530	{}
3.4047	0.9908	838	0.7219	0.95	0.7536	0.9513	{}
3.532	0.9932	840	0.7219	0.9517	0.7538	0.9530	{}
3.532	0.9956	842	0.7219	0.9517	0.7538	0.9530	{}
3.532	0.9979	844	0.7223	0.9517	0.7538	0.9530	{}

Framework versions

PEFT 0.18.1
Transformers 4.51.0
Pytorch 2.8.0+cu128
Datasets 3.6.0
Tokenizers 0.21.0

ferrazzipietro/crfTask-unsup-Qwen3-8B-datav3-only_mask

crfTask-unsup-Qwen3-8B-datav3-only_mask

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

ferrazzipietro/
crfTask-unsup-Qwen3-8B-datav3-only_mask