learning_rate: 2e-05
train_batch_size: 1
eval_batch_size: 1
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: 40
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
1.9664	0.1012	200	1.6373
1.8479	0.2023	400	1.5448
1.7346	0.3035	600	1.4981
1.6646	0.4047	800	1.4699
1.7652	0.5058	1000	1.4416
1.6482	0.6070	1200	1.4232
1.6232	0.7081	1400	1.4059
1.6663	0.8093	1600	1.3927
1.6392	0.9105	1800	1.3796
1.5706	1.0116	2000	1.3685
1.5516	1.1128	2200	1.3630
1.4989	1.2140	2400	1.3545
1.5427	1.3151	2600	1.3473
1.5742	1.4163	2800	1.3387
1.4773	1.5175	3000	1.3315
1.5533	1.6186	3200	1.3202
1.522	1.7198	3400	1.3174
1.5131	1.8209	3600	1.3203
1.4758	1.9221	3800	1.3039
1.5195	2.0233	4000	1.2988
1.4134	2.1244	4200	1.3007
1.4578	2.2256	4400	1.2943
1.4839	2.3268	4600	1.2922
1.3859	2.4279	4800	1.2835
1.4397	2.5291	5000	1.2839
1.4392	2.6302	5200	1.2808
1.4348	2.7314	5400	1.2685
1.4376	2.8326	5600	1.2679
1.4433	2.9337	5800	1.2665
1.4712	3.0349	6000	1.2667
1.4009	3.1361	6200	1.2630
1.3355	3.2372	6400	1.2591
1.3932	3.3384	6600	1.2566
1.3751	3.4396	6800	1.2533
1.3201	3.5407	7000	1.2550
1.3698	3.6419	7200	1.2506
1.4137	3.7430	7400	1.2452
1.3821	3.8442	7600	1.2469
1.3258	3.9454	7800	1.2427
1.3382	4.0465	8000	1.2419
1.3297	4.1477	8200	1.2391
1.3575	4.2489	8400	1.2308
1.3013	4.3500	8600	1.2277
1.3181	4.4512	8800	1.2261
1.3194	4.5524	9000	1.2272
1.3027	4.6535	9200	1.2310
1.3017	4.7547	9400	1.2242
1.3105	4.8558	9600	1.2269
1.3222	4.9570	9800	1.2163
1.309	5.0582	10000	1.2171
1.2944	5.1593	10200	1.2078
1.2968	5.2605	10400	1.2102
1.2824	5.3617	10600	1.2115
1.2581	5.4628	10800	1.2173
1.2682	5.5640	11000	1.2124
1.2704	5.6651	11200	1.2126
1.2858	5.7663	11400	1.2036
1.2483	5.8675	11600	1.2090
1.3131	5.9686	11800	1.2041
1.2148	6.0698	12000	1.2006
1.2455	6.1710	12200	1.2046
1.2568	6.2721	12400	1.2053
1.2171	6.3733	12600	1.2013
1.2149	6.4745	12800	1.1979
1.2106	6.5756	13000	1.2003
1.244	6.6768	13200	1.1933
1.246	6.7779	13400	1.1920
1.2931	6.8791	13600	1.1925
1.1615	6.9803	13800	1.1913
1.2136	7.0814	14000	1.1914
1.1436	7.1826	14200	1.1939
1.2137	7.2838	14400	1.1911
1.1871	7.3849	14600	1.1886
1.2506	7.4861	14800	1.1831
1.1921	7.5873	15000	1.1833
1.2177	7.6884	15200	1.1799
1.1888	7.7896	15400	1.1817
1.1947	7.8907	15600	1.1792
1.1784	7.9919	15800	1.1797
1.2106	8.0931	16000	1.1794
1.2427	8.1942	16200	1.1768
1.1888	8.2954	16400	1.1817
1.1271	8.3966	16600	1.1811
1.1584	8.4977	16800	1.1743
1.1506	8.5989	17000	1.1764
1.146	8.7001	17200	1.1761
1.1299	8.8012	17400	1.1750
1.1653	8.9024	17600	1.1704
1.1112	9.0035	17800	1.1681
1.1845	9.1047	18000	1.1687
1.135	9.2059	18200	1.1721
1.111	9.3070	18400	1.1666
1.1325	9.4082	18600	1.1662
1.1284	9.5094	18800	1.1671
1.0956	9.6105	19000	1.1754
1.1614	9.7117	19200	1.1707
1.1138	9.8128	19400	1.1686
1.1287	9.9140	19600	1.1709
1.134	10.0152	19800	1.1694
1.1109	10.1163	20000	1.1700
1.1186	10.2175	20200	1.1684
1.1232	10.3187	20400	1.1698
1.1287	10.4198	20600	1.1702
1.1139	10.5210	20800	1.1718
1.1484	10.6222	21000	1.1710
1.072	10.7233	21200	1.1639
1.0957	10.8245	21400	1.1611
1.128	10.9256	21600	1.1640
1.0693	11.0268	21800	1.1590
1.0505	11.1280	22000	1.1619
1.0466	11.2291	22200	1.1654
1.079	11.3303	22400	1.1631
1.1081	11.4315	22600	1.1615
1.0918	11.5326	22800	1.1593
1.092	11.6338	23000	1.1579
1.1203	11.7350	23200	1.1609
1.0742	11.8361	23400	1.1551
1.067	11.9373	23600	1.1557
1.0514	12.0384	23800	1.1635
1.0464	12.1396	24000	1.1600
1.0569	12.2408	24200	1.1602
1.0431	12.3419	24400	1.1612
1.0772	12.4431	24600	1.1628
1.0517	12.5443	24800	1.1578
1.0648	12.6454	25000	1.1577
1.0513	12.7466	25200	1.1621
1.0594	12.8477	25400	1.1592
1.0768	12.9489	25600	1.1604
1.1028	13.0501	25800	1.1500
1.0269	13.1512	26000	1.1566
1.0388	13.2524	26200	1.1595
0.9966	13.3536	26400	1.1595
1.0287	13.4547	26600	1.1604
1.0528	13.5559	26800	1.1565
1.0425	13.6571	27000	1.1536
1.0547	13.7582	27200	1.1567
1.0125	13.8594	27400	1.1606
1.0743	13.9605	27600	1.1513
0.9734	14.0617	27800	1.1579
1.0261	14.1629	28000	1.1528
1.0001	14.2640	28200	1.1584
1.014	14.3652	28400	1.1596
1.0252	14.4664	28600	1.1622
1.0505	14.5675	28800	1.1538
1.0142	14.6687	29000	1.1567
1.0291	14.7699	29200	1.1515
1.0313	14.8710	29400	1.1550
0.9889	14.9722	29600	1.1554
1.0173	15.0733	29800	1.1588
1.0081	15.1745	30000	1.1587
0.9559	15.2757	30200	1.1539
1.0332	15.3768	30400	1.1573
1.0004	15.4780	30600	1.1553
0.9586	15.5792	30800	1.1496
1.0138	15.6803	31000	1.1513
1.0256	15.7815	31200	1.1545
1.0243	15.8827	31400	1.1546
1.0135	15.9838	31600	1.1540
0.9589	16.0850	31800	1.1524
0.9672	16.1861	32000	1.1574
1.0335	16.2873	32200	1.1539
0.9464	16.3885	32400	1.1637
0.9669	16.4896	32600	1.1579
0.9629	16.5908	32800	1.1611
0.9586	16.6920	33000	1.1575
0.9939	16.7931	33200	1.1582
0.9956	16.8943	33400	1.1544
1.0249	16.9954	33600	1.1520
0.9461	17.0966	33800	1.1541
0.9876	17.1978	34000	1.1520
0.9656	17.2989	34200	1.1530
0.9443	17.4001	34400	1.1565
0.9348	17.5013	34600	1.1570
0.9906	17.6024	34800	1.1532
0.9281	17.7036	35000	1.1553
0.9346	17.8048	35200	1.1542
1.0175	17.9059	35400	1.1506
0.9547	18.0071	35600	1.1544
0.9089	18.1082	35800	1.1571
0.9506	18.2094	36000	1.1559
0.9625	18.3106	36200	1.1532
0.9206	18.4117	36400	1.1532
0.8997	18.5129	36600	1.1530
0.9625	18.6141	36800	1.1553
0.9358	18.7152	37000	1.1516
1.0168	18.8164	37200	1.1531
0.965	18.9176	37400	1.1489
0.9527	19.0187	37600	1.1463
0.9437	19.1199	37800	1.1511
0.916	19.2210	38000	1.1523
0.9264	19.3222	38200	1.1521
0.957	19.4234	38400	1.1507
0.9539	19.5245	38600	1.1533
0.9256	19.6257	38800	1.1498
0.908	19.7269	39000	1.1497
0.8984	19.8280	39200	1.1525
0.9754	19.9292	39400	1.1479
0.9266	20.0303	39600	1.1521
0.942	20.1315	39800	1.1513
0.9249	20.2327	40000	1.1485
0.8982	20.3338	40200	1.1505
0.8932	20.4350	40400	1.1541
0.926	20.5362	40600	1.1554
0.9393	20.6373	40800	1.1538
0.9498	20.7385	41000	1.1513
0.9262	20.8397	41200	1.1546
0.9085	20.9408	41400	1.1519
0.924	21.0420	41600	1.1526
0.8869	21.1431	41800	1.1568
0.9009	21.2443	42000	1.1549
0.9097	21.3455	42200	1.1505
0.9179	21.4466	42400	1.1495
0.9519	21.5478	42600	1.1492
0.9473	21.6490	42800	1.1480
0.9488	21.7501	43000	1.1521
0.8719	21.8513	43200	1.1501
0.8593	21.9525	43400	1.1544
0.9029	22.0536	43600	1.1572
0.9005	22.1548	43800	1.1532
0.919	22.2559	44000	1.1521
0.9448	22.3571	44200	1.1506
0.9157	22.4583	44400	1.1575
0.8985	22.5594	44600	1.1528
0.8632	22.6606	44800	1.1571
0.8519	22.7618	45000	1.1521
0.8813	22.8629	45200	1.1543
0.9135	22.9641	45400	1.1524
0.9258	23.0653	45600	1.1533
0.8879	23.1664	45800	1.1509
0.8898	23.2676	46000	1.1557
0.8521	23.3687	46200	1.1578
0.8519	23.4699	46400	1.1574
0.9005	23.5711	46600	1.1552
0.8927	23.6722	46800	1.1534
0.8729	23.7734	47000	1.1565
0.9021	23.8746	47200	1.1533
0.9276	23.9757	47400	1.1560
0.8924	24.0769	47600	1.1545
0.8545	24.1780	47800	1.1525
0.8724	24.2792	48000	1.1501
0.9042	24.3804	48200	1.1554
0.9064	24.4815	48400	1.1523
0.8832	24.5827	48600	1.1512
0.8979	24.6839	48800	1.1532
0.8324	24.7850	49000	1.1528
0.8757	24.8862	49200	1.1550
0.8614	24.9874	49400	1.1545
0.8691	25.0885	49600	1.1545
0.8828	25.1897	49800	1.1557
0.8455	25.2908	50000	1.1565
0.8352	25.3920	50200	1.1526
0.8775	25.4932	50400	1.1527
0.832	25.5943	50600	1.1562
0.9212	25.6955	50800	1.1560
0.8921	25.7967	51000	1.1520
0.8611	25.8978	51200	1.1531
0.8633	25.9990	51400	1.1522
0.8508	26.1002	51600	1.1541
0.823	26.2013	51800	1.1550
0.9097	26.3025	52000	1.1558
0.8726	26.4036	52200	1.1537
0.8891	26.5048	52400	1.1545
0.8276	26.6060	52600	1.1559
0.8404	26.7071	52800	1.1543
0.8556	26.8083	53000	1.1510
0.8846	26.9095	53200	1.1544
0.8461	27.0106	53400	1.1533
0.8265	27.1118	53600	1.1516
0.8807	27.2129	53800	1.1529
0.8459	27.3141	54000	1.1554
0.8648	27.4153	54200	1.1556
0.8559	27.5164	54400	1.1592
0.8797	27.6176	54600	1.1546
0.8502	27.7188	54800	1.1565
0.83	27.8199	55000	1.1568
0.863	27.9211	55200	1.1582
0.8294	28.0223	55400	1.1564
0.8217	28.1234	55600	1.1565
0.8321	28.2246	55800	1.1568
0.8231	28.3257	56000	1.1559
0.8355	28.4269	56200	1.1539
0.8347	28.5281	56400	1.1546
0.8538	28.6292	56600	1.1555
0.8558	28.7304	56800	1.1572
0.8243	28.8316	57000	1.1576
0.9286	28.9327	57200	1.1575
0.8884	29.0339	57400	1.1558
0.8399	29.1351	57600	1.1578
0.8306	29.2362	57800	1.1567
0.8208	29.3374	58000	1.1566
0.8072	29.4385	58200	1.1585
0.8278	29.5397	58400	1.1591
0.8489	29.6409	58600	1.1579
0.8756	29.7420	58800	1.1542
0.8384	29.8432	59000	1.1560
0.8134	29.9444	59200	1.1584
0.8215	30.0455	59400	1.1604
0.8562	30.1467	59600	1.1584
0.8449	30.2479	59800	1.1612
0.8796	30.3490	60000	1.1585
0.9045	30.4502	60200	1.1548
0.7957	30.5513	60400	1.1574
0.7785	30.6525	60600	1.1571
0.8368	30.7537	60800	1.1592
0.8423	30.8548	61000	1.1588
0.7635	30.9560	61200	1.1597
0.8168	31.0572	61400	1.1588
0.8612	31.1583	61600	1.1590
0.83	31.2595	61800	1.1591
0.8217	31.3606	62000	1.1577
0.8413	31.4618	62200	1.1560
0.8382	31.5630	62400	1.1579
0.804	31.6641	62600	1.1590
0.8481	31.7653	62800	1.1586
0.8373	31.8665	63000	1.1577
0.778	31.9676	63200	1.1590
0.8349	32.0688	63400	1.1605
0.7616	32.1700	63600	1.1588
0.8354	32.2711	63800	1.1600
0.8107	32.3723	64000	1.1595
0.8092	32.4734	64200	1.1605
0.808	32.5746	64400	1.1596
0.8734	32.6758	64600	1.1586
0.806	32.7769	64800	1.1589
0.7934	32.8781	65000	1.1579
0.924	32.9793	65200	1.1592
0.8016	33.0804	65400	1.1600
0.8136	33.1816	65600	1.1608
0.8735	33.2828	65800	1.1603
0.8068	33.3839	66000	1.1602
0.8051	33.4851	66200	1.1618
0.8049	33.5862	66400	1.1623
0.8062	33.6874	66600	1.1621
0.7888	33.7886	66800	1.1636
0.8115	33.8897	67000	1.1653
0.8149	33.9909	67200	1.1639
0.7969	34.0921	67400	1.1621
0.8319	34.1932	67600	1.1621
0.8	34.2944	67800	1.1613
0.8332	34.3955	68000	1.1610
0.802	34.4967	68200	1.1601
0.8242	34.5979	68400	1.1618
0.7646	34.6990	68600	1.1634
0.7749	34.8002	68800	1.1634
0.8498	34.9014	69000	1.1619
0.8597	35.0025	69200	1.1619
0.8015	35.1037	69400	1.1607
0.8733	35.2049	69600	1.1604
0.8199	35.3060	69800	1.1621
0.7654	35.4072	70000	1.1626
0.7932	35.5083	70200	1.1628
0.8133	35.6095	70400	1.1611
0.7802	35.7107	70600	1.1612
0.8061	35.8118	70800	1.1604
0.7915	35.9130	71000	1.1610
0.8092	36.0142	71200	1.1619
0.7976	36.1153	71400	1.1627
0.8017	36.2165	71600	1.1627
0.79	36.3177	71800	1.1628
0.7558	36.4188	72000	1.1627
0.7938	36.5200	72200	1.1628
0.8048	36.6211	72400	1.1624
0.8593	36.7223	72600	1.1630
0.8314	36.8235	72800	1.1629
0.8283	36.9246	73000	1.1614
0.8174	37.0258	73200	1.1609
0.7722	37.1270	73400	1.1608
0.749	37.2281	73600	1.1611
0.7754	37.3293	73800	1.1607
0.781	37.4305	74000	1.1617
0.8335	37.5316	74200	1.1623
0.8072	37.6328	74400	1.1633
0.8042	37.7339	74600	1.1630
0.8393	37.8351	74800	1.1633
0.829	37.9363	75000	1.1627
0.8514	38.0374	75200	1.1626
0.7967	38.1386	75400	1.1632
0.7447	38.2398	75600	1.1634
0.78	38.3409	75800	1.1641
0.8281	38.4421	76000	1.1635
0.8021	38.5432	76200	1.1633
0.8328	38.6444	76400	1.1629
0.8464	38.7456	76600	1.1629
0.837	38.8467	76800	1.1625
0.7686	38.9479	77000	1.1627
0.8235	39.0491	77200	1.1625
0.8161	39.1502	77400	1.1626
0.8016	39.2514	77600	1.1627
0.7946	39.3526	77800	1.1630
0.7941	39.4537	78000	1.1633
0.76	39.5549	78200	1.1632
0.8394	39.6560	78400	1.1632
0.7558	39.7572	78600	1.1632
0.8374	39.8584	78800	1.1631
0.8077	39.9595	79000	1.1631

Framework versions

Transformers 4.48.0
Pytorch 2.5.1+cu124
Datasets 3.3.0
Tokenizers 0.21.0

machinelearningzuu/ptsd-summarization

ptsd-summarization

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

machinelearningzuu/
ptsd-summarization