learning_rate: 0.0001
train_batch_size: 1
eval_batch_size: 1
seed: 42
gradient_accumulation_steps: 64
total_train_batch_size: 64
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: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Precision@0.01	Recall@0.01	F1@0.01	Accuracy@0.01	Precision@0.02	Recall@0.02	F1@0.02	Accuracy@0.02	Precision@0.03	Recall@0.03	F1@0.03	Accuracy@0.03	Precision@0.04	Recall@0.04	F1@0.04	Accuracy@0.04	Precision@0.05	Recall@0.05	F1@0.05	Accuracy@0.05	Precision@0.06	Recall@0.06	F1@0.06	Accuracy@0.06	Precision@0.07	Recall@0.07	F1@0.07	Accuracy@0.07	Precision@0.08	Recall@0.08	F1@0.08	Accuracy@0.08	Precision@0.09	Recall@0.09	F1@0.09	Accuracy@0.09	Precision@0.1	Recall@0.1	F1@0.1	Accuracy@0.1	Precision@0.11	Recall@0.11	F1@0.11	Accuracy@0.11	Precision@0.12	Recall@0.12	F1@0.12	Accuracy@0.12	Precision@0.13	Recall@0.13	F1@0.13	Accuracy@0.13	Precision@0.14	Recall@0.14	F1@0.14	Accuracy@0.14	Precision@0.15	Recall@0.15	F1@0.15	Accuracy@0.15	Precision@0.16	Recall@0.16	F1@0.16	Accuracy@0.16	Precision@0.17	Recall@0.17	F1@0.17	Accuracy@0.17	Precision@0.18	Recall@0.18	F1@0.18	Accuracy@0.18	Precision@0.19	Recall@0.19	F1@0.19	Accuracy@0.19	Precision@0.2	Recall@0.2	F1@0.2	Accuracy@0.2	Precision@0.21	Recall@0.21	F1@0.21	Accuracy@0.21	Precision@0.22	Recall@0.22	F1@0.22	Accuracy@0.22	Precision@0.23	Recall@0.23	F1@0.23	Accuracy@0.23	Precision@0.24	Recall@0.24	F1@0.24	Accuracy@0.24	Precision@0.25	Recall@0.25	F1@0.25	Accuracy@0.25	Precision@0.26	Recall@0.26	F1@0.26	Accuracy@0.26	Precision@0.27	Recall@0.27	F1@0.27	Accuracy@0.27	Precision@0.28	Recall@0.28	F1@0.28	Accuracy@0.28	Precision@0.29	Recall@0.29	F1@0.29	Accuracy@0.29	Precision@0.3	Recall@0.3	F1@0.3	Accuracy@0.3	Precision@0.31	Recall@0.31	F1@0.31	Accuracy@0.31	Precision@0.32	Recall@0.32	F1@0.32	Accuracy@0.32	Precision@0.33	Recall@0.33	F1@0.33	Accuracy@0.33	Precision@0.34	Recall@0.34	F1@0.34	Accuracy@0.34	Precision@0.35	Recall@0.35	F1@0.35	Accuracy@0.35	Precision@0.36	Recall@0.36	F1@0.36	Accuracy@0.36	Precision@0.37	Recall@0.37	F1@0.37	Accuracy@0.37	Precision@0.38	Recall@0.38	F1@0.38	Accuracy@0.38	Precision@0.39	Recall@0.39	F1@0.39	Accuracy@0.39	Precision@0.4	Recall@0.4	F1@0.4	Accuracy@0.4	Precision@0.41	Recall@0.41	F1@0.41	Accuracy@0.41	Precision@0.42	Recall@0.42	F1@0.42	Accuracy@0.42	Precision@0.43	Recall@0.43	F1@0.43	Accuracy@0.43	Precision@0.44	Recall@0.44	F1@0.44	Accuracy@0.44	Precision@0.45	Recall@0.45	F1@0.45	Accuracy@0.45	Precision@0.46	Recall@0.46	F1@0.46	Accuracy@0.46	Precision@0.47	Recall@0.47	F1@0.47	Accuracy@0.47	Precision@0.48	Recall@0.48	F1@0.48	Accuracy@0.48	Precision@0.49	Recall@0.49	F1@0.49	Accuracy@0.49	Precision@0.5	Recall@0.5	F1@0.5	Accuracy@0.5	Precision@0.51	Recall@0.51	F1@0.51	Accuracy@0.51	Precision@0.52	Recall@0.52	F1@0.52	Accuracy@0.52	Precision@0.53	Recall@0.53	F1@0.53	Accuracy@0.53	Precision@0.54	Recall@0.54	F1@0.54	Accuracy@0.54	Precision@0.55	Recall@0.55	F1@0.55	Accuracy@0.55	Precision@0.56	Recall@0.56	F1@0.56	Accuracy@0.56	Precision@0.57	Recall@0.57	F1@0.57	Accuracy@0.57	Precision@0.58	Recall@0.58	F1@0.58	Accuracy@0.58	Precision@0.59	Recall@0.59	F1@0.59	Accuracy@0.59	Precision@0.6	Recall@0.6	F1@0.6	Accuracy@0.6	Precision@0.61	Recall@0.61	F1@0.61	Accuracy@0.61	Precision@0.62	Recall@0.62	F1@0.62	Accuracy@0.62	Precision@0.63	Recall@0.63	F1@0.63	Accuracy@0.63	Precision@0.64	Recall@0.64	F1@0.64	Accuracy@0.64	Precision@0.65	Recall@0.65	F1@0.65	Accuracy@0.65	Precision@0.66	Recall@0.66	F1@0.66	Accuracy@0.66	Precision@0.67	Recall@0.67	F1@0.67	Accuracy@0.67	Precision@0.68	Recall@0.68	F1@0.68	Accuracy@0.68	Precision@0.69	Recall@0.69	F1@0.69	Accuracy@0.69	Precision@0.7	Recall@0.7	F1@0.7	Accuracy@0.7	Precision@0.71	Recall@0.71	F1@0.71	Accuracy@0.71	Precision@0.72	Recall@0.72	F1@0.72	Accuracy@0.72	Precision@0.73	Recall@0.73	F1@0.73	Accuracy@0.73	Precision@0.74	Recall@0.74	F1@0.74	Accuracy@0.74	Precision@0.75	Recall@0.75	F1@0.75	Accuracy@0.75	Precision@0.76	Recall@0.76	F1@0.76	Accuracy@0.76	Precision@0.77	Recall@0.77	F1@0.77	Accuracy@0.77	Precision@0.78	Recall@0.78	F1@0.78	Accuracy@0.78	Precision@0.79	Recall@0.79	F1@0.79	Accuracy@0.79	Precision@0.8	Recall@0.8	F1@0.8	Accuracy@0.8	Precision@0.81	Recall@0.81	F1@0.81	Accuracy@0.81	Precision@0.82	Recall@0.82	F1@0.82	Accuracy@0.82	Precision@0.83	Recall@0.83	F1@0.83	Accuracy@0.83	Precision@0.84	Recall@0.84	F1@0.84	Accuracy@0.84	Precision@0.85	Recall@0.85	F1@0.85	Accuracy@0.85	Precision@0.86	Recall@0.86	F1@0.86	Accuracy@0.86	Precision@0.87	Recall@0.87	F1@0.87	Accuracy@0.87	Precision@0.88	Recall@0.88	F1@0.88	Accuracy@0.88	Precision@0.89	Recall@0.89	F1@0.89	Accuracy@0.89	Precision@0.9	Recall@0.9	F1@0.9	Accuracy@0.9	Precision@0.91	Recall@0.91	F1@0.91	Accuracy@0.91	Precision@0.92	Recall@0.92	F1@0.92	Accuracy@0.92	Precision@0.93	Recall@0.93	F1@0.93	Accuracy@0.93	Precision@0.94	Recall@0.94	F1@0.94	Accuracy@0.94	Precision@0.95	Recall@0.95	F1@0.95	Accuracy@0.95	Precision@0.96	Recall@0.96	F1@0.96	Accuracy@0.96	Precision@0.97	Recall@0.97	F1@0.97	Accuracy@0.97	Precision@0.98	Recall@0.98	F1@0.98	Accuracy@0.98	Precision@0.99	Recall@0.99	F1@0.99	Accuracy@0.99
5.8618	1.0	4160	0.1579	0.6785	0.9929	0.8061	0.8303	0.7597	0.9835	0.8573	0.8836	0.7958	0.9772	0.8772	0.9028	0.8144	0.9734	0.8868	0.9117	0.8279	0.9710	0.8938	0.9180	0.8403	0.9684	0.8998	0.9234	0.8481	0.9660	0.9032	0.9264	0.8546	0.9641	0.9061	0.9290	0.8602	0.9631	0.9088	0.9313	0.8649	0.9617	0.9107	0.9330	0.8691	0.9604	0.9125	0.9345	0.8721	0.9593	0.9136	0.9355	0.8747	0.9579	0.9144	0.9363	0.8774	0.9571	0.9155	0.9372	0.8802	0.9561	0.9166	0.9382	0.8830	0.9556	0.9179	0.9392	0.8854	0.9552	0.9190	0.9402	0.8873	0.9543	0.9196	0.9407	0.8891	0.9537	0.9202	0.9412	0.8909	0.9534	0.9211	0.9419	0.8926	0.9525	0.9216	0.9424	0.8944	0.9521	0.9224	0.9430	0.8955	0.9513	0.9225	0.9432	0.8965	0.9502	0.9225	0.9433	0.8969	0.9496	0.9225	0.9433	0.8982	0.9484	0.9226	0.9435	0.8993	0.9475	0.9228	0.9437	0.9004	0.9469	0.9230	0.9439	0.9019	0.9467	0.9237	0.9444	0.9034	0.9461	0.9243	0.9449	0.9047	0.9454	0.9246	0.9452	0.9056	0.9450	0.9248	0.9454	0.9065	0.9446	0.9252	0.9457	0.9074	0.9440	0.9254	0.9459	0.9085	0.9438	0.9258	0.9462	0.9093	0.9433	0.9260	0.9464	0.9100	0.9428	0.9261	0.9466	0.9109	0.9423	0.9263	0.9467	0.9118	0.9418	0.9266	0.9470	0.9124	0.9417	0.9268	0.9472	0.9130	0.9412	0.9269	0.9472	0.9140	0.9406	0.9271	0.9474	0.9146	0.9398	0.9271	0.9474	0.9158	0.9396	0.9275	0.9478	0.9170	0.9392	0.9279	0.9482	0.9177	0.9387	0.9281	0.9483	0.9185	0.9383	0.9283	0.9485	0.9193	0.9376	0.9284	0.9486	0.9200	0.9370	0.9284	0.9487	0.9206	0.9367	0.9286	0.9488	0.9214	0.9363	0.9288	0.9490	0.9219	0.9356	0.9287	0.9489	0.9224	0.9352	0.9288	0.9490	0.9228	0.9350	0.9289	0.9491	0.9234	0.9350	0.9292	0.9494	0.9241	0.9345	0.9293	0.9494	0.9250	0.9341	0.9295	0.9497	0.9256	0.9339	0.9298	0.9499	0.9269	0.9336	0.9302	0.9502	0.9273	0.9331	0.9302	0.9502	0.9276	0.9323	0.9299	0.9501	0.9284	0.9318	0.9301	0.9502	0.9290	0.9313	0.9301	0.9503	0.9296	0.9309	0.9303	0.9504	0.9306	0.9307	0.9307	0.9507	0.9320	0.9302	0.9311	0.9511	0.9327	0.9293	0.9310	0.9510	0.9333	0.9288	0.9310	0.9511	0.9340	0.9281	0.9310	0.9511	0.9347	0.9273	0.9310	0.9512	0.9354	0.9263	0.9309	0.9511	0.9364	0.9260	0.9311	0.9513	0.9373	0.9250	0.9311	0.9514	0.9383	0.9242	0.9312	0.9515	0.9392	0.9235	0.9313	0.9516	0.9402	0.9230	0.9315	0.9518	0.9410	0.9218	0.9313	0.9517	0.9421	0.9204	0.9311	0.9516	0.9428	0.9198	0.9312	0.9517	0.9439	0.9192	0.9314	0.9519	0.9448	0.9179	0.9312	0.9518	0.9460	0.9164	0.9310	0.9517	0.9479	0.9152	0.9313	0.9520	0.9493	0.9140	0.9313	0.9521	0.9504	0.9125	0.9311	0.9520	0.9517	0.9101	0.9304	0.9516	0.9529	0.9085	0.9302	0.9515	0.9542	0.9062	0.9296	0.9512	0.9555	0.9041	0.9291	0.9510	0.9566	0.9028	0.9289	0.9509	0.9577	0.9005	0.9282	0.9505	0.9600	0.8975	0.9277	0.9503	0.9620	0.8948	0.9272	0.9501	0.9639	0.89	0.9255	0.9491	0.9662	0.8857	0.9242	0.9484	0.9697	0.8790	0.9221	0.9472	0.9744	0.8685	0.9184	0.9452	0.9794	0.8508	0.9106	0.9406	0.9860	0.8112	0.8901	0.9288
3.574	2.0	8320	0.2679	0.9139	0.9550	0.9340	0.9520	0.9191	0.9520	0.9352	0.9531	0.9225	0.9505	0.9363	0.9540	0.9246	0.9493	0.9368	0.9545	0.9251	0.9479	0.9364	0.9542	0.9262	0.9472	0.9365	0.9544	0.9271	0.9468	0.9368	0.9546	0.9282	0.9463	0.9372	0.9549	0.9284	0.9460	0.9371	0.9549	0.9287	0.9453	0.9369	0.9548	0.9293	0.9449	0.9370	0.9549	0.9298	0.9449	0.9373	0.9550	0.9301	0.9445	0.9372	0.9550	0.9302	0.9440	0.9371	0.9549	0.9304	0.9437	0.9370	0.9549	0.9304	0.9435	0.9369	0.9548	0.9308	0.9434	0.9370	0.9549	0.9312	0.9432	0.9371	0.9550	0.9312	0.9431	0.9371	0.9550	0.9314	0.9429	0.9371	0.9550	0.9316	0.9428	0.9372	0.9551	0.9317	0.9428	0.9372	0.9551	0.9317	0.9423	0.9370	0.9549	0.9319	0.9421	0.9370	0.9550	0.9321	0.9421	0.9371	0.9550	0.9321	0.9420	0.9370	0.9550	0.9324	0.9420	0.9372	0.9551	0.9327	0.9419	0.9373	0.9552	0.9328	0.9418	0.9373	0.9552	0.9329	0.9418	0.9374	0.9553	0.9329	0.9417	0.9373	0.9552	0.9330	0.9415	0.9372	0.9552	0.9333	0.9412	0.9372	0.9552	0.9336	0.9409	0.9372	0.9552	0.9336	0.9409	0.9373	0.9552	0.9337	0.9408	0.9373	0.9552	0.9339	0.9408	0.9373	0.9553	0.9340	0.9407	0.9373	0.9553	0.9343	0.9406	0.9375	0.9554	0.9344	0.9405	0.9374	0.9554	0.9345	0.9404	0.9374	0.9554	0.9345	0.9402	0.9373	0.9553	0.9349	0.94	0.9374	0.9554	0.9353	0.9399	0.9376	0.9555	0.9356	0.9399	0.9378	0.9557	0.9356	0.9395	0.9376	0.9555	0.9357	0.9395	0.9376	0.9556	0.9358	0.9390	0.9374	0.9554	0.9360	0.9388	0.9374	0.9554	0.9360	0.9385	0.9373	0.9554	0.9363	0.9384	0.9374	0.9554	0.9364	0.9384	0.9374	0.9555	0.9366	0.9384	0.9375	0.9555	0.9366	0.9384	0.9375	0.9556	0.9368	0.9383	0.9376	0.9556	0.9370	0.9383	0.9377	0.9557	0.9374	0.9383	0.9379	0.9558	0.9374	0.9382	0.9378	0.9558	0.9374	0.9382	0.9378	0.9558	0.9374	0.9382	0.9378	0.9558	0.9375	0.9382	0.9378	0.9558	0.9376	0.9382	0.9379	0.9558	0.9379	0.9380	0.9379	0.9559	0.9381	0.9379	0.9380	0.9559	0.9381	0.9377	0.9379	0.9559	0.9383	0.9375	0.9379	0.9559	0.9386	0.9375	0.9380	0.9560	0.9387	0.9372	0.9379	0.9559	0.9387	0.9372	0.9379	0.9559	0.9388	0.9370	0.9379	0.9559	0.9389	0.9368	0.9378	0.9559	0.9391	0.9368	0.9380	0.9560	0.9393	0.9364	0.9378	0.9559	0.9393	0.9364	0.9378	0.9559	0.9393	0.9363	0.9378	0.9559	0.9396	0.9361	0.9378	0.9559	0.9398	0.9360	0.9379	0.9560	0.9404	0.9360	0.9382	0.9562	0.9407	0.9359	0.9383	0.9562	0.9409	0.9359	0.9384	0.9563	0.9410	0.9358	0.9384	0.9563	0.9411	0.9357	0.9384	0.9563	0.9413	0.9355	0.9384	0.9563	0.9413	0.9354	0.9383	0.9563	0.9420	0.9354	0.9387	0.9566	0.9423	0.9352	0.9388	0.9566	0.9428	0.9352	0.9390	0.9568	0.9432	0.9350	0.9391	0.9569	0.9438	0.9350	0.9393	0.9571	0.9442	0.9347	0.9394	0.9572	0.9449	0.9339	0.9394	0.9572	0.9454	0.9337	0.9395	0.9573	0.9459	0.9330	0.9394	0.9572	0.9463	0.9321	0.9392	0.9571	0.9469	0.9316	0.9392	0.9571	0.9481	0.9308	0.9394	0.9573	0.9491	0.9295	0.9392	0.9573	0.9501	0.9273	0.9386	0.9569	0.9549	0.9229	0.9387	0.9571
1.7597	2.9995	12477	0.3203	0.9384	0.9372	0.9378	0.9558	0.9411	0.9359	0.9385	0.9564	0.9417	0.9350	0.9384	0.9563	0.9421	0.9349	0.9385	0.9564	0.9429	0.9348	0.9388	0.9567	0.9431	0.9348	0.9389	0.9568	0.9432	0.9347	0.9389	0.9568	0.9437	0.9345	0.9391	0.9569	0.9437	0.9344	0.9390	0.9569	0.9438	0.9343	0.9391	0.9569	0.9439	0.9342	0.9390	0.9569	0.9440	0.9339	0.9389	0.9568	0.9442	0.9337	0.9389	0.9568	0.9444	0.9334	0.9389	0.9568	0.9448	0.9334	0.9391	0.9570	0.9449	0.9334	0.9391	0.9570	0.9450	0.9332	0.9391	0.9570	0.9450	0.9332	0.9391	0.9570	0.9452	0.9330	0.9391	0.9570	0.9454	0.9330	0.9392	0.9571	0.9455	0.9329	0.9392	0.9571	0.9455	0.9328	0.9391	0.9570	0.9455	0.9328	0.9391	0.9570	0.9456	0.9328	0.9391	0.9570	0.9456	0.9328	0.9391	0.9570	0.9457	0.9327	0.9391	0.9570	0.9458	0.9326	0.9392	0.9571	0.9459	0.9326	0.9392	0.9571	0.9460	0.9326	0.9392	0.9571	0.9461	0.9325	0.9392	0.9571	0.9462	0.9325	0.9393	0.9572	0.9462	0.9325	0.9393	0.9572	0.9464	0.9325	0.9394	0.9572	0.9465	0.9324	0.9394	0.9572	0.9464	0.9323	0.9393	0.9572	0.9467	0.9323	0.9394	0.9573	0.9467	0.9323	0.9394	0.9573	0.9467	0.9322	0.9394	0.9573	0.9467	0.9322	0.9394	0.9573	0.9467	0.9321	0.9393	0.9572	0.9467	0.9321	0.9393	0.9572	0.9467	0.9321	0.9393	0.9572	0.9468	0.9320	0.9393	0.9572	0.9470	0.9319	0.9394	0.9573	0.9469	0.9317	0.9393	0.9572	0.9469	0.9317	0.9393	0.9572	0.9469	0.9317	0.9393	0.9572	0.9470	0.9317	0.9393	0.9572	0.9471	0.9316	0.9393	0.9572	0.9471	0.9316	0.9393	0.9572	0.9471	0.9316	0.9393	0.9572	0.9472	0.9316	0.9393	0.9572	0.9473	0.9314	0.9393	0.9572	0.9473	0.9314	0.9393	0.9572	0.9474	0.9314	0.9393	0.9572	0.9474	0.9312	0.9392	0.9572	0.9474	0.9312	0.9392	0.9572	0.9473	0.9310	0.9391	0.9571	0.9473	0.9310	0.9391	0.9571	0.9474	0.9310	0.9392	0.9571	0.9474	0.9310	0.9392	0.9571	0.9475	0.9309	0.9391	0.9571	0.9476	0.9307	0.9391	0.9571	0.9476	0.9306	0.9390	0.9571	0.9477	0.9306	0.9390	0.9571	0.9477	0.9306	0.9390	0.9571	0.9477	0.9304	0.9389	0.9570	0.9478	0.9304	0.9390	0.9570	0.9477	0.9302	0.9389	0.9570	0.9479	0.9302	0.9390	0.9570	0.9479	0.9301	0.9389	0.9570	0.9480	0.9299	0.9389	0.9570	0.9481	0.9299	0.9389	0.9570	0.9481	0.9299	0.9389	0.9570	0.9482	0.9299	0.9390	0.9570	0.9482	0.9299	0.9390	0.9570	0.9483	0.9298	0.9389	0.9570	0.9483	0.9297	0.9389	0.9570	0.9484	0.9297	0.9390	0.9571	0.9485	0.9296	0.9390	0.9571	0.9485	0.9294	0.9389	0.9570	0.9485	0.9294	0.9389	0.9570	0.9486	0.9294	0.9389	0.9570	0.9488	0.9294	0.9390	0.9571	0.9491	0.9293	0.9391	0.9572	0.9495	0.9293	0.9393	0.9573	0.9495	0.9292	0.9392	0.9573	0.9496	0.9292	0.9393	0.9573	0.9497	0.9290	0.9392	0.9573	0.9499	0.9289	0.9393	0.9573	0.9501	0.9287	0.9393	0.9573	0.9503	0.9287	0.9394	0.9574	0.9507	0.9285	0.9395	0.9575	0.9509	0.9283	0.9394	0.9575	0.9512	0.9281	0.9395	0.9575	0.9515	0.9277	0.9395	0.9575	0.9520	0.9270	0.9393	0.9575	0.9525	0.9267	0.9394	0.9575	0.9537	0.9246	0.9389	0.9573

Framework versions

Transformers 4.48.0.dev0
Pytorch 2.5.1+cu124
Datasets 3.2.0
Tokenizers 0.21.0

Kyle1668/answerdotai-ModernBERT-large_20250111-224237

answerdotai-ModernBERT-large_20250111-224237

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Kyle1668/
answerdotai-ModernBERT-large_20250111-224237