xlm-roberta-toxicity-classification-ptp-all-languages-7

This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.4031
• Qwk: 0.9304
• Mae: 0.1656
• Accuracy: 0.8378
• Arabic Qwk: 0.9486
• Arabic Mae: 0.128
• Arabic Accuracy: 0.872
• Chinese Qwk: 0.9065
• Chinese Mae: 0.224
• Chinese Accuracy: 0.778
• Czech Qwk: 0.9538
• Czech Mae: 0.106
• Czech Accuracy: 0.896
• Dutch Qwk: 0.9489
• Dutch Mae: 0.122
• Dutch Accuracy: 0.88
• English Qwk: 0.9466
• English Mae: 0.13
• English Accuracy: 0.872
• French Qwk: 0.9502
• French Mae: 0.118
• French Accuracy: 0.884
• German Qwk: 0.8524
• German Mae: 0.334
• German Accuracy: 0.684
• Hindi Qwk: 0.9335
• Hindi Mae: 0.156
• Hindi Accuracy: 0.846
• Indonesian Qwk: 0.9476
• Indonesian Mae: 0.13
• Indonesian Accuracy: 0.87
• Italian Qwk: 0.9673
• Italian Mae: 0.078
• Italian Accuracy: 0.922
• Japanese Qwk: 0.9166
• Japanese Mae: 0.202
• Japanese Accuracy: 0.8
• Korean Qwk: 0.9447
• Korean Mae: 0.134
• Korean Accuracy: 0.866
• Polish Qwk: 0.9473
• Polish Mae: 0.128
• Polish Accuracy: 0.872
• Portuguese Qwk: 0.8567
• Portuguese Mae: 0.324
• Portuguese Accuracy: 0.696
• Russian Qwk: 0.8930
• Russian Mae: 0.262
• Russian Accuracy: 0.742
• Spanish Qwk: 0.9375
• Spanish Mae: 0.152
• Spanish Accuracy: 0.85
• Swedish Qwk: 0.9452
• Swedish Mae: 0.13
• Swedish Accuracy: 0.872
• Sanity Check Qwk: nan
• Sanity Check Mae: 0.0
• Sanity Check Accuracy: 1.0

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: 1e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Use 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: 1

Training results

Training Loss	Epoch	Step	Arabic Accuracy	Arabic Mae	Arabic Qwk	Chinese Accuracy	Chinese Mae	Chinese Qwk	Czech Accuracy	Czech Mae	Czech Qwk	Dutch Accuracy	Dutch Mae	Dutch Qwk	English Accuracy	English Mae	English Qwk	French Accuracy	French Mae	French Qwk	German Accuracy	German Mae	German Qwk	Hindi Accuracy	Hindi Mae	Hindi Qwk	Indonesian Accuracy	Indonesian Mae	Indonesian Qwk	Italian Accuracy	Italian Mae	Italian Qwk	Japanese Accuracy	Japanese Mae	Japanese Qwk	Korean Accuracy	Korean Mae	Korean Qwk	Polish Accuracy	Polish Mae	Polish Qwk	Portuguese Accuracy	Portuguese Mae	Portuguese Qwk	Russian Accuracy	Russian Mae	Russian Qwk	Spanish Accuracy	Spanish Mae	Spanish Qwk	Swedish Accuracy	Swedish Mae	Swedish Qwk	Accuracy	Validation Loss	Mae	Qwk	Sanity Check Accuracy	Sanity Check Mae	Sanity Check Qwk
0.6439	0.0268	500	0.68	0.332	0.8688	0.672	0.364	0.8316	0.756	0.266	0.8816	0.736	0.266	0.9029	0.758	0.246	0.8965	0.726	0.274	0.9033	0.572	0.464	0.7697	0.728	0.288	0.8731	0.746	0.258	0.8983	0.742	0.268	0.8843	0.65	0.366	0.8254	0.742	0.266	0.8900	0.75	0.268	0.8854	0.59	0.46	0.7764	0.624	0.418	0.8133	0.714	0.31	0.8587	0.784	0.226	0.9006	0.7079	0.6977	0.3101	0.8660	0.9606	0.0394	0.0
0.7334	0.0536	1000	0.758	0.244	0.9048	0.706	0.302	0.8598	0.792	0.212	0.9045	0.774	0.228	0.9160	0.802	0.202	0.9196	0.782	0.222	0.9155	0.622	0.42	0.8002	0.764	0.246	0.8908	0.77	0.23	0.9114	0.808	0.196	0.9168	0.704	0.308	0.8621	0.776	0.226	0.9068	0.786	0.224	0.9053	0.636	0.406	0.7976	0.658	0.374	0.8189	0.78	0.236	0.8936	0.804	0.2	0.9170	0.7506	0.5961	0.2609	0.8873	0.8976	0.1024	0.0
0.603	0.0804	1500	0.788	0.218	0.8993	0.7	0.31	0.8535	0.758	0.246	0.8777	0.81	0.194	0.9139	0.826	0.178	0.9218	0.822	0.178	0.9194	0.596	0.438	0.7888	0.766	0.24	0.8893	0.796	0.204	0.9140	0.814	0.188	0.9190	0.67	0.358	0.8036	0.754	0.248	0.8914	0.768	0.236	0.8918	0.562	0.484	0.7532	0.664	0.364	0.8140	0.748	0.264	0.8693	0.806	0.2	0.9105	0.7475	0.5850	0.2638	0.8752	0.9843	0.0157	0.0
0.5844	0.1072	2000	0.784	0.216	0.9134	0.692	0.314	0.8568	0.794	0.208	0.9004	0.82	0.186	0.9143	0.808	0.194	0.9198	0.844	0.156	0.9342	0.676	0.36	0.8291	0.794	0.214	0.8992	0.824	0.176	0.9263	0.864	0.138	0.9409	0.724	0.284	0.8736	0.838	0.164	0.9297	0.788	0.212	0.9082	0.66	0.36	0.8224	0.662	0.348	0.8476	0.758	0.246	0.8840	0.854	0.148	0.9372	0.7785	0.5209	0.2278	0.8988	0.9764	0.0236	0.0
0.6695	0.1340	2500	0.772	0.23	0.9084	0.704	0.306	0.8658	0.83	0.172	0.9257	0.824	0.178	0.9303	0.824	0.178	0.9271	0.834	0.166	0.9290	0.656	0.37	0.8270	0.772	0.234	0.8929	0.84	0.16	0.9350	0.872	0.13	0.9453	0.694	0.316	0.8661	0.83	0.172	0.9281	0.822	0.18	0.9260	0.682	0.334	0.8499	0.68	0.334	0.8444	0.796	0.21	0.9079	0.86	0.144	0.9381	0.7849	0.5210	0.2213	0.9051	0.9843	0.0157	0.0
0.7138	0.1609	3000	0.76	0.242	0.8975	0.69	0.318	0.8476	0.808	0.194	0.9140	0.812	0.19	0.9226	0.76	0.242	0.9012	0.834	0.166	0.9302	0.608	0.428	0.8081	0.768	0.238	0.8901	0.832	0.168	0.9283	0.834	0.166	0.9306	0.672	0.336	0.8425	0.788	0.216	0.9095	0.808	0.194	0.9174	0.564	0.494	0.7738	0.632	0.374	0.8189	0.738	0.268	0.8810	0.812	0.192	0.9158	0.7515	0.5684	0.2571	0.8861	0.9685	0.0394	0.0
0.5483	0.1877	3500	0.808	0.194	0.9207	0.736	0.266	0.8886	0.848	0.154	0.9339	0.838	0.168	0.9260	0.844	0.158	0.9351	0.854	0.148	0.9371	0.624	0.41	0.8220	0.796	0.208	0.9071	0.848	0.152	0.9382	0.896	0.106	0.9545	0.766	0.244	0.8885	0.816	0.186	0.9225	0.83	0.174	0.9268	0.668	0.358	0.8396	0.7	0.308	0.8649	0.806	0.198	0.9160	0.858	0.144	0.9397	0.7992	0.4820	0.2073	0.9109	1.0	0.0	nan
0.5865	0.2145	4000	0.804	0.196	0.9186	0.74	0.264	0.8873	0.842	0.16	0.9295	0.824	0.182	0.9231	0.806	0.194	0.9222	0.846	0.154	0.9354	0.654	0.372	0.8351	0.798	0.206	0.9071	0.824	0.178	0.9262	0.84	0.16	0.9336	0.74	0.268	0.8869	0.818	0.184	0.9223	0.832	0.17	0.9297	0.614	0.42	0.8134	0.674	0.332	0.8617	0.76	0.246	0.8996	0.86	0.142	0.9400	0.7838	0.5047	0.2223	0.9055	0.9764	0.0315	0.0
0.5605	0.2413	4500	0.8	0.2	0.9207	0.712	0.298	0.8765	0.816	0.186	0.9211	0.836	0.17	0.9295	0.806	0.194	0.9264	0.852	0.148	0.9377	0.664	0.36	0.8330	0.748	0.256	0.8967	0.84	0.164	0.9317	0.898	0.102	0.9580	0.67	0.342	0.8681	0.81	0.19	0.9243	0.812	0.19	0.9235	0.692	0.332	0.8490	0.672	0.342	0.8585	0.794	0.212	0.9127	0.84	0.164	0.9308	0.7834	0.5189	0.2231	0.9074	1.0	0.0	nan
0.4937	0.2681	5000	0.82	0.18	0.9302	0.766	0.238	0.8976	0.858	0.144	0.9369	0.808	0.198	0.9212	0.844	0.156	0.9389	0.85	0.15	0.9405	0.662	0.362	0.8278	0.824	0.178	0.9274	0.83	0.17	0.9327	0.872	0.128	0.9475	0.774	0.236	0.8992	0.824	0.176	0.9287	0.816	0.186	0.9253	0.668	0.36	0.8339	0.686	0.324	0.8640	0.798	0.208	0.9077	0.864	0.138	0.9423	0.8006	0.4932	0.2049	0.9139	0.9843	0.0157	0.0
0.5286	0.2949	5500	0.826	0.174	0.9251	0.704	0.3	0.8662	0.86	0.142	0.9374	0.848	0.152	0.9348	0.87	0.13	0.9449	0.852	0.148	0.9347	0.64	0.372	0.8239	0.812	0.19	0.9150	0.85	0.15	0.9379	0.896	0.104	0.9558	0.758	0.248	0.8894	0.842	0.158	0.9340	0.812	0.19	0.9204	0.718	0.304	0.8506	0.688	0.316	0.8579	0.81	0.196	0.9045	0.872	0.13	0.9444	0.8058	0.4726	0.1978	0.9128	0.9685	0.0315	0.0
0.4297	0.3217	6000	0.828	0.172	0.9321	0.728	0.278	0.8788	0.836	0.164	0.9302	0.846	0.16	0.9321	0.844	0.16	0.9298	0.86	0.142	0.9387	0.638	0.38	0.8372	0.814	0.188	0.9190	0.848	0.152	0.9383	0.878	0.122	0.9494	0.732	0.278	0.8760	0.8	0.2	0.9173	0.812	0.19	0.9222	0.636	0.388	0.8279	0.686	0.326	0.8561	0.78	0.228	0.8968	0.842	0.16	0.9327	0.7918	0.5110	0.2137	0.9085	1.0	0.0	nan
0.4847	0.3485	6500	0.824	0.176	0.9277	0.746	0.26	0.8891	0.854	0.148	0.9358	0.85	0.152	0.9358	0.854	0.146	0.9414	0.85	0.15	0.9389	0.688	0.332	0.8418	0.834	0.168	0.9275	0.84	0.16	0.9350	0.898	0.104	0.9550	0.772	0.236	0.8996	0.846	0.156	0.9345	0.842	0.16	0.9337	0.712	0.312	0.8490	0.702	0.304	0.8701	0.816	0.19	0.9171	0.866	0.136	0.9430	0.8142	0.4566	0.1907	0.9182	1.0	0.0	nan
0.4536	0.3753	7000	0.812	0.188	0.9268	0.758	0.244	0.8986	0.878	0.126	0.9450	0.822	0.182	0.9247	0.852	0.148	0.9412	0.87	0.13	0.9478	0.672	0.35	0.8434	0.804	0.198	0.9183	0.862	0.138	0.9447	0.904	0.096	0.9598	0.748	0.26	0.8869	0.84	0.16	0.9338	0.812	0.192	0.9217	0.668	0.356	0.8396	0.712	0.292	0.8794	0.798	0.208	0.9061	0.86	0.142	0.9407	0.8070	0.4731	0.1978	0.9169	0.9921	0.0079	0.0
0.454	0.4021	7500	0.808	0.192	0.9256	0.762	0.242	0.8962	0.87	0.132	0.9413	0.852	0.15	0.9372	0.826	0.174	0.9330	0.86	0.14	0.9435	0.66	0.362	0.8428	0.826	0.178	0.9219	0.84	0.162	0.9331	0.902	0.1	0.9564	0.772	0.23	0.8967	0.848	0.154	0.9349	0.85	0.152	0.9363	0.658	0.372	0.8377	0.694	0.314	0.8612	0.822	0.182	0.9240	0.85	0.154	0.9336	0.8086	0.4703	0.1966	0.9164	0.9921	0.0079	0.0
0.4405	0.4290	8000	0.838	0.162	0.9332	0.74	0.264	0.8848	0.864	0.138	0.9389	0.86	0.142	0.9404	0.862	0.14	0.9416	0.88	0.12	0.9483	0.642	0.38	0.8329	0.806	0.196	0.9123	0.868	0.132	0.9461	0.904	0.096	0.9590	0.776	0.23	0.8960	0.846	0.154	0.9353	0.854	0.146	0.9378	0.67	0.358	0.8404	0.722	0.284	0.8747	0.824	0.18	0.9184	0.866	0.136	0.9423	0.8158	0.4390	0.1888	0.9179	1.0	0.0	nan
0.5102	0.4558	8500	0.844	0.156	0.9365	0.748	0.256	0.8908	0.892	0.11	0.9513	0.87	0.13	0.9452	0.864	0.136	0.9449	0.872	0.13	0.9423	0.674	0.344	0.8435	0.816	0.186	0.9210	0.858	0.142	0.9414	0.888	0.112	0.9529	0.788	0.216	0.9051	0.844	0.156	0.9344	0.852	0.15	0.9375	0.672	0.35	0.8416	0.706	0.302	0.8704	0.85	0.152	0.9354	0.882	0.12	0.9486	0.8215	0.4266	0.1825	0.9217	1.0	0.0	nan
0.4028	0.4826	9000	0.848	0.152	0.9377	0.778	0.224	0.9030	0.882	0.12	0.9467	0.872	0.13	0.9434	0.86	0.14	0.9435	0.884	0.116	0.9504	0.666	0.35	0.8430	0.836	0.166	0.9258	0.868	0.134	0.9438	0.894	0.108	0.9527	0.766	0.238	0.8959	0.856	0.144	0.9397	0.856	0.144	0.9407	0.634	0.4	0.8245	0.702	0.302	0.8660	0.838	0.168	0.9235	0.87	0.134	0.9413	0.8209	0.4372	0.1837	0.9201	1.0	0.0	nan
0.4185	0.5094	9500	0.838	0.162	0.9332	0.778	0.224	0.9030	0.892	0.11	0.9522	0.874	0.13	0.9428	0.848	0.154	0.9349	0.88	0.12	0.9503	0.644	0.382	0.8325	0.84	0.162	0.9300	0.862	0.138	0.9427	0.892	0.108	0.9549	0.786	0.216	0.9058	0.854	0.146	0.9397	0.824	0.178	0.9256	0.66	0.37	0.8325	0.71	0.296	0.8761	0.832	0.172	0.9267	0.882	0.118	0.9509	0.8200	0.4321	0.1848	0.9206	0.9921	0.0079	0.0
0.5041	0.5362	10000	0.842	0.158	0.9321	0.758	0.244	0.8954	0.87	0.134	0.9409	0.868	0.134	0.9434	0.866	0.134	0.9460	0.882	0.12	0.9456	0.666	0.36	0.8413	0.838	0.164	0.9275	0.86	0.14	0.9403	0.896	0.106	0.9537	0.798	0.206	0.9063	0.844	0.156	0.9345	0.846	0.154	0.9366	0.7	0.318	0.8535	0.716	0.29	0.8743	0.84	0.164	0.9259	0.876	0.126	0.9462	0.8242	0.4300	0.1801	0.9215	1.0	0.0	nan
0.4305	0.5630	10500	0.848	0.152	0.9386	0.778	0.224	0.9047	0.898	0.104	0.9542	0.864	0.14	0.9411	0.866	0.136	0.9444	0.894	0.106	0.9556	0.668	0.354	0.8446	0.842	0.16	0.9321	0.864	0.136	0.9446	0.908	0.092	0.9615	0.794	0.212	0.9094	0.854	0.146	0.9396	0.842	0.158	0.9363	0.688	0.328	0.8584	0.714	0.29	0.8810	0.846	0.158	0.9324	0.87	0.13	0.9466	0.8283	0.4182	0.1754	0.9262	1.0	0.0	nan
0.3719	0.5898	11000	0.862	0.138	0.9446	0.756	0.248	0.8940	0.87	0.134	0.9412	0.874	0.128	0.9463	0.85	0.152	0.9394	0.88	0.12	0.9510	0.672	0.35	0.8453	0.844	0.158	0.9334	0.858	0.142	0.9430	0.902	0.098	0.9592	0.798	0.206	0.9122	0.86	0.14	0.9421	0.844	0.156	0.9373	0.688	0.332	0.8538	0.712	0.294	0.8766	0.848	0.154	0.9367	0.868	0.132	0.9459	0.8253	0.4201	0.1786	0.9251	1.0	0.0	nan
0.4213	0.6166	11500	0.844	0.156	0.9395	0.766	0.236	0.9006	0.892	0.112	0.9499	0.86	0.144	0.9406	0.86	0.14	0.9450	0.85	0.15	0.9408	0.67	0.35	0.8466	0.848	0.154	0.9347	0.864	0.136	0.9452	0.906	0.094	0.9608	0.742	0.264	0.8926	0.856	0.144	0.9407	0.832	0.168	0.9322	0.692	0.324	0.8588	0.724	0.282	0.8848	0.832	0.172	0.9290	0.868	0.134	0.9439	0.8207	0.4344	0.1831	0.9241	1.0	0.0	nan
0.5293	0.6434	12000	0.856	0.144	0.9430	0.768	0.234	0.8988	0.896	0.106	0.9535	0.854	0.148	0.9395	0.844	0.158	0.9366	0.85	0.15	0.9402	0.686	0.33	0.8494	0.832	0.17	0.9270	0.85	0.15	0.9398	0.906	0.094	0.9606	0.78	0.222	0.9053	0.86	0.14	0.9422	0.85	0.15	0.9377	0.688	0.334	0.8481	0.718	0.286	0.8809	0.834	0.168	0.9309	0.868	0.132	0.9459	0.8226	0.4226	0.1806	0.9242	1.0	0.0	nan
0.3045	0.6702	12500	0.868	0.132	0.9466	0.762	0.24	0.8981	0.884	0.118	0.9492	0.878	0.124	0.9482	0.866	0.136	0.9443	0.888	0.112	0.9533	0.69	0.328	0.8513	0.854	0.148	0.9372	0.864	0.136	0.9440	0.916	0.084	0.9649	0.772	0.232	0.9028	0.866	0.134	0.9448	0.852	0.148	0.9396	0.712	0.304	0.8615	0.732	0.274	0.8845	0.852	0.15	0.9380	0.868	0.134	0.9433	0.8333	0.4033	0.1700	0.9282	1.0	0.0	nan
0.3547	0.6971	13000	0.86	0.14	0.9445	0.778	0.224	0.9044	0.886	0.116	0.9499	0.876	0.126	0.9476	0.862	0.14	0.9432	0.872	0.13	0.9456	0.68	0.338	0.8525	0.846	0.156	0.9325	0.864	0.136	0.9450	0.912	0.088	0.9632	0.794	0.21	0.9110	0.862	0.138	0.9436	0.854	0.146	0.9398	0.668	0.354	0.8482	0.734	0.27	0.8863	0.852	0.15	0.9381	0.876	0.126	0.9469	0.8305	0.4236	0.1732	0.9273	1.0	0.0	nan
0.4719	0.7239	13500	0.856	0.144	0.9430	0.762	0.24	0.8988	0.884	0.12	0.9468	0.872	0.13	0.9462	0.844	0.158	0.9363	0.878	0.122	0.9501	0.676	0.342	0.8480	0.842	0.16	0.9309	0.858	0.142	0.9429	0.9	0.1	0.9582	0.776	0.23	0.9034	0.864	0.136	0.9440	0.84	0.16	0.9355	0.694	0.326	0.8566	0.724	0.28	0.8860	0.846	0.156	0.9360	0.866	0.136	0.9430	0.8251	0.4282	0.1786	0.9253	1.0	0.0	nan
0.3304	0.7507	14000	0.874	0.126	0.9500	0.78	0.222	0.9062	0.908	0.094	0.9585	0.868	0.134	0.9448	0.854	0.146	0.9430	0.892	0.11	0.9530	0.692	0.324	0.8582	0.856	0.146	0.9371	0.874	0.126	0.9493	0.914	0.086	0.9642	0.776	0.226	0.9053	0.854	0.146	0.9404	0.852	0.148	0.9390	0.682	0.338	0.8531	0.718	0.288	0.8796	0.834	0.168	0.9307	0.88	0.122	0.9482	0.8324	0.4153	0.1710	0.9283	1.0	0.0	nan
0.4641	0.7775	14500	0.874	0.126	0.9490	0.788	0.214	0.9092	0.904	0.098	0.9569	0.876	0.126	0.9472	0.878	0.122	0.9513	0.898	0.104	0.9554	0.694	0.322	0.8567	0.85	0.152	0.9346	0.872	0.128	0.9481	0.918	0.082	0.9655	0.782	0.22	0.9090	0.866	0.134	0.9448	0.856	0.144	0.9409	0.708	0.31	0.8619	0.73	0.274	0.8878	0.848	0.154	0.9369	0.88	0.122	0.9482	0.8390	0.4056	0.1641	0.9309	1.0	0.0	nan
0.367	0.8043	15000	0.87	0.13	0.9473	0.778	0.224	0.9056	0.89	0.112	0.9514	0.874	0.128	0.9464	0.87	0.13	0.9484	0.898	0.104	0.9555	0.692	0.324	0.8567	0.844	0.158	0.9333	0.864	0.136	0.9450	0.918	0.082	0.9658	0.792	0.212	0.9121	0.866	0.134	0.9448	0.848	0.152	0.9385	0.7	0.32	0.8586	0.726	0.28	0.8834	0.848	0.154	0.9368	0.872	0.13	0.9450	0.8348	0.4090	0.1687	0.9292	1.0	0.0	nan
0.4378	0.8311	15500	0.874	0.126	0.9489	0.784	0.218	0.9086	0.896	0.106	0.9537	0.884	0.118	0.9503	0.878	0.124	0.9490	0.9	0.102	0.9561	0.69	0.326	0.8541	0.854	0.148	0.9368	0.87	0.13	0.9474	0.914	0.086	0.9640	0.798	0.204	0.9155	0.868	0.132	0.9458	0.852	0.148	0.9398	0.682	0.336	0.8558	0.736	0.268	0.8895	0.852	0.15	0.9384	0.88	0.122	0.9482	0.8384	0.4057	0.1648	0.9308	1.0	0.0	nan
0.4411	0.8579	16000	0.87	0.13	0.9479	0.778	0.224	0.9065	0.896	0.104	0.9562	0.882	0.12	0.9495	0.87	0.132	0.9462	0.894	0.108	0.9536	0.686	0.334	0.8512	0.854	0.148	0.9370	0.87	0.13	0.9477	0.922	0.078	0.9674	0.8	0.202	0.9166	0.862	0.138	0.9431	0.862	0.138	0.9437	0.692	0.326	0.8593	0.73	0.276	0.8863	0.85	0.152	0.9377	0.878	0.126	0.9452	0.8375	0.4046	0.1661	0.9302	1.0	0.0	nan
0.3288	0.8847	16500	0.4008	0.9313	0.1626	0.8407	0.9499	0.124	0.876	0.9055	0.226	0.776	0.9600	0.094	0.906	0.9502	0.118	0.884	0.9479	0.126	0.876	0.9544	0.106	0.896	0.8514	0.334	0.684	0.9371	0.146	0.856	0.9466	0.132	0.868	0.9682	0.076	0.924	0.9203	0.19	0.812	0.9439	0.136	0.864	0.9449	0.134	0.866	0.8572	0.326	0.692	0.8886	0.266	0.74	0.9380	0.15	0.852	0.9481	0.122	0.88	nan	0.0	1.0
0.3776	0.9115	17000	0.4075	0.9301	0.1667	0.8368	0.9463	0.134	0.866	0.9068	0.224	0.778	0.9538	0.106	0.896	0.9496	0.12	0.882	0.9452	0.134	0.868	0.9518	0.114	0.888	0.8512	0.338	0.68	0.9353	0.152	0.85	0.9468	0.132	0.868	0.9658	0.082	0.918	0.9151	0.206	0.796	0.9467	0.13	0.87	0.9427	0.14	0.86	0.8564	0.33	0.69	0.8927	0.26	0.744	0.9374	0.152	0.85	0.9484	0.122	0.88	nan	0.0	1.0
0.2721	0.9383	17500	0.4012	0.9303	0.1658	0.8375	0.9491	0.126	0.874	0.9044	0.228	0.774	0.9585	0.098	0.902	0.9485	0.122	0.88	0.9473	0.128	0.874	0.9537	0.108	0.894	0.8528	0.334	0.684	0.9347	0.152	0.85	0.9468	0.132	0.868	0.9665	0.08	0.92	0.9155	0.204	0.798	0.9457	0.132	0.868	0.9408	0.144	0.856	0.8552	0.334	0.684	0.8919	0.264	0.74	0.9366	0.154	0.848	0.9491	0.12	0.882	nan	0.0	1.0
0.4061	0.9651	18000	0.3980	0.9307	0.1647	0.8385	0.9475	0.13	0.87	0.9051	0.226	0.776	0.9543	0.104	0.898	0.9503	0.118	0.884	0.9464	0.13	0.872	0.9539	0.108	0.894	0.8560	0.326	0.692	0.9338	0.154	0.848	0.9459	0.134	0.866	0.9665	0.08	0.92	0.9185	0.196	0.806	0.9430	0.138	0.862	0.9464	0.13	0.87	0.8587	0.328	0.688	0.8918	0.264	0.74	0.9360	0.156	0.846	0.9490	0.12	0.882	nan	0.0	1.0
0.3829	0.9920	18500	0.4031	0.9304	0.1656	0.8378	0.9486	0.128	0.872	0.9065	0.224	0.778	0.9538	0.106	0.896	0.9489	0.122	0.88	0.9466	0.13	0.872	0.9502	0.118	0.884	0.8524	0.334	0.684	0.9335	0.156	0.846	0.9476	0.13	0.87	0.9673	0.078	0.922	0.9166	0.202	0.8	0.9447	0.134	0.866	0.9473	0.128	0.872	0.8567	0.324	0.696	0.8930	0.262	0.742	0.9375	0.152	0.85	0.9452	0.13	0.872	nan	0.0	1.0

Framework versions

• Transformers 4.50.0
• Pytorch 2.3.0+cu118
• Datasets 2.21.0
• Tokenizers 0.21.1