Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1	Ratio
1.3001	0.14	10	0.8533	0.6452	0.6683	0.6459	0.6525	0.2903
0.9775	0.27	20	0.7170	0.6918	0.7191	0.6944	0.6639	0.3513
0.8096	0.41	30	0.6432	0.7204	0.7222	0.7218	0.7213	0.3297
0.9883	0.55	40	0.6914	0.7204	0.7382	0.7228	0.7064	0.3297
0.9221	0.68	50	0.6597	0.7563	0.7582	0.7574	0.7570	0.3297
0.8253	0.82	60	0.6639	0.7563	0.7802	0.7568	0.7440	0.3297
0.9096	0.96	70	0.6612	0.7384	0.7484	0.7392	0.7319	0.3297
0.8466	1.1	80	0.6884	0.7168	0.7534	0.7195	0.6884	0.3297
0.8457	1.23	90	0.6884	0.7563	0.7898	0.7567	0.7397	0.3297
0.836	1.37	100	0.6409	0.7563	0.7711	0.7569	0.7488	0.3297
0.7878	1.51	110	0.6876	0.7276	0.7324	0.7286	0.7249	0.3297
0.8107	1.64	120	0.6720	0.7419	0.7518	0.7427	0.7361	0.3297
0.782	1.78	130	0.7397	0.7419	0.7518	0.7427	0.7361	0.3297
0.7728	1.92	140	0.6921	0.7455	0.7551	0.7463	0.7402	0.3297
0.7704	2.05	150	0.6882	0.7419	0.7518	0.7427	0.7361	0.3297
0.7593	2.19	160	0.7163	0.7348	0.7487	0.7355	0.7304	0.3226
0.7995	2.33	170	0.6639	0.7419	0.7518	0.7427	0.7361	0.3297
0.7657	2.47	180	0.6906	0.7885	0.8418	0.7887	0.7720	0.3297
0.7758	2.6	190	0.7577	0.7133	0.7154	0.7146	0.7134	0.3297
0.8269	2.74	200	0.8168	0.5591	0.6324	0.5596	0.5804	0.2151
0.7721	2.88	210	0.6721	0.7706	0.7924	0.7711	0.7615	0.3297
0.7098	3.01	220	0.6917	0.7133	0.7157	0.7145	0.7129	0.3297
0.7683	3.15	230	0.7175	0.7168	0.7192	0.7181	0.7168	0.3297
0.6907	3.29	240	0.7298	0.7491	0.7598	0.7499	0.7434	0.3297
0.7013	3.42	250	0.7363	0.7634	0.7794	0.7641	0.7562	0.3297
0.7852	3.56	260	0.7616	0.7025	0.7181	0.7034	0.7052	0.3082
0.7375	3.7	270	0.7247	0.7849	0.8127	0.7853	0.7753	0.3297
0.7242	3.84	280	0.7199	0.7921	0.8182	0.7925	0.7838	0.3297
0.7135	3.97	290	0.7190	0.7742	0.7907	0.7748	0.7677	0.3297
0.7501	4.11	300	0.7104	0.7921	0.8292	0.7924	0.7805	0.3297
0.663	4.25	310	0.7579	0.7921	0.8643	0.7922	0.7720	0.3297
0.7316	4.38	320	0.7671	0.7312	0.7347	0.7323	0.7301	0.3297
0.7045	4.52	330	0.7673	0.7204	0.7227	0.7217	0.7206	0.3297
0.7316	4.66	340	0.7421	0.7849	0.8096	0.7854	0.7764	0.3297
0.7667	4.79	350	0.7269	0.7527	0.7576	0.7536	0.7512	0.3297
0.7109	4.93	360	0.7305	0.7742	0.7907	0.7748	0.7677	0.3297
0.7677	5.07	370	0.7805	0.7885	0.8226	0.7888	0.7773	0.3297
0.6988	5.21	380	0.7531	0.7921	0.8182	0.7925	0.7838	0.3297
0.7119	5.34	390	0.7396	0.7670	0.7773	0.7677	0.7630	0.3297
0.6535	5.48	400	0.7259	0.7634	0.7714	0.7642	0.7604	0.3297
0.6732	5.62	410	0.7301	0.7921	0.8292	0.7924	0.7805	0.3297
0.7243	5.75	420	0.7094	0.7849	0.8127	0.7853	0.7753	0.3297
0.7367	5.89	430	0.7266	0.7670	0.7759	0.7678	0.7637	0.3297
0.7464	6.03	440	0.7929	0.7957	0.8277	0.7960	0.7860	0.3297
0.6836	6.16	450	0.7844	0.7957	0.8209	0.7961	0.7880	0.3297
0.6901	6.3	460	0.7724	0.7706	0.7837	0.7713	0.7654	0.3297
0.6776	6.44	470	0.7513	0.7670	0.7806	0.7677	0.7613	0.3297
0.6388	6.58	480	0.7491	0.7384	0.7410	0.7395	0.7383	0.3297
0.7258	6.71	490	0.7361	0.7599	0.7695	0.7606	0.7557	0.3297
0.7458	6.85	500	0.7777	0.7993	0.8483	0.7994	0.7856	0.3297
0.7937	6.99	510	0.7797	0.7921	0.8336	0.7923	0.7793	0.3297
0.6984	7.12	520	0.7597	0.7634	0.7714	0.7642	0.7604	0.3297
0.7206	7.26	530	0.7470	0.7384	0.7415	0.7395	0.7378	0.3297

Framework versions

Transformers 4.39.0.dev0
Pytorch 2.1.0+cu121
Datasets 2.17.1
Tokenizers 0.15.2

hcene/distilCamemBERT_nli_on_legal_data

distilCamemBERT_nli_on_legal_data

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

hcene/
distilCamemBERT_nli_on_legal_data