scenario-NON-KD-PO-COPY-D2_data-AmazonScience_massive_all_1_144

This model is a fine-tuned version of haryoaw/scenario-MDBT-TCR_data-AmazonScience_massive_all_1_1 on the massive dataset. It achieves the following results on the evaluation set:

• Loss: 2.0782
• Accuracy: 0.8544
• F1: 0.8289

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: 5e-05
• train_batch_size: 32
• eval_batch_size: 32
• seed: 44
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
0.5161	0.27	5000	0.6816	0.8291	0.7878
0.3963	0.53	10000	0.6619	0.8406	0.8132
0.3396	0.8	15000	0.6545	0.8447	0.8161
0.2234	1.07	20000	0.7323	0.8483	0.8257
0.229	1.34	25000	0.7528	0.8444	0.8191
0.2172	1.6	30000	0.7349	0.8495	0.8253
0.2086	1.87	35000	0.7411	0.8485	0.8257
0.1495	2.14	40000	0.8672	0.8444	0.8152
0.1561	2.41	45000	0.8479	0.8490	0.8186
0.1557	2.67	50000	0.8802	0.8479	0.8208
0.1543	2.94	55000	0.8013	0.8538	0.8334
0.1052	3.21	60000	0.9790	0.8501	0.8221
0.1104	3.47	65000	0.9687	0.8499	0.8274
0.1245	3.74	70000	0.8966	0.8485	0.8279
0.1056	4.01	75000	1.0067	0.8494	0.8275
0.0875	4.28	80000	1.0929	0.8475	0.8222
0.0881	4.54	85000	1.0596	0.8469	0.8178
0.1	4.81	90000	1.0371	0.8519	0.8269
0.0741	5.08	95000	1.1216	0.8462	0.8180
0.0734	5.34	100000	1.1472	0.8492	0.8228
0.0819	5.61	105000	1.1234	0.8482	0.8226
0.0805	5.88	110000	1.0975	0.8496	0.8237
0.0667	6.15	115000	1.1985	0.8476	0.8189
0.0664	6.41	120000	1.1688	0.8482	0.8236
0.0674	6.68	125000	1.1931	0.8462	0.8206
0.0726	6.95	130000	1.1113	0.8517	0.8268
0.0561	7.22	135000	1.2818	0.8484	0.8214
0.0513	7.48	140000	1.2779	0.8504	0.8239
0.0602	7.75	145000	1.2326	0.8502	0.8238
0.0474	8.02	150000	1.3027	0.8500	0.8253
0.0492	8.28	155000	1.3551	0.8475	0.8224
0.0533	8.55	160000	1.2721	0.8498	0.8258
0.0527	8.82	165000	1.2575	0.8490	0.8239
0.0396	9.09	170000	1.3997	0.8489	0.8231
0.0494	9.35	175000	1.3877	0.8472	0.8219
0.0547	9.62	180000	1.3829	0.8476	0.8221
0.0459	9.89	185000	1.3692	0.8511	0.8257
0.0331	10.15	190000	1.3953	0.8492	0.8267
0.0459	10.42	195000	1.4430	0.8483	0.8276
0.0418	10.69	200000	1.4325	0.8495	0.8288
0.0482	10.96	205000	1.3494	0.8514	0.8253
0.0332	11.22	210000	1.5266	0.8500	0.8251
0.0403	11.49	215000	1.4327	0.8535	0.8302
0.0394	11.76	220000	1.4069	0.8513	0.8282
0.0296	12.03	225000	1.4835	0.8521	0.8310
0.0393	12.29	230000	1.5214	0.8503	0.8271
0.0365	12.56	235000	1.5325	0.8508	0.8267
0.0356	12.83	240000	1.5022	0.8505	0.8273
0.0279	13.09	245000	1.5751	0.8486	0.8260
0.0335	13.36	250000	1.5749	0.8483	0.8248
0.0326	13.63	255000	1.5202	0.8509	0.8265
0.0341	13.9	260000	1.5074	0.8496	0.8266
0.0224	14.16	265000	1.6855	0.8476	0.8180
0.0247	14.43	270000	1.5959	0.8501	0.8231
0.0301	14.7	275000	1.5587	0.8515	0.8281
0.0235	14.96	280000	1.6095	0.8488	0.8270
0.0223	15.23	285000	1.7195	0.8485	0.8234
0.0243	15.5	290000	1.7301	0.8483	0.8214
0.0307	15.77	295000	1.5648	0.8518	0.8251
0.0201	16.03	300000	1.6855	0.8506	0.8252
0.0222	16.3	305000	1.7225	0.8484	0.8227
0.0252	16.57	310000	1.6764	0.8486	0.8241
0.0252	16.84	315000	1.6818	0.8498	0.8241
0.0174	17.1	320000	1.7985	0.8519	0.8315
0.0228	17.37	325000	1.7816	0.8492	0.8232
0.0251	17.64	330000	1.7655	0.8500	0.8248
0.0248	17.9	335000	1.7425	0.8512	0.8260
0.0182	18.17	340000	1.8507	0.8497	0.8229
0.0194	18.44	345000	1.8165	0.8503	0.8273
0.0196	18.71	350000	1.7348	0.8493	0.8244
0.0218	18.97	355000	1.8210	0.8514	0.8266
0.0144	19.24	360000	1.8294	0.8520	0.8289
0.017	19.51	365000	1.8268	0.8489	0.8249
0.02	19.77	370000	1.7976	0.8506	0.8285
0.0137	20.04	375000	1.8765	0.8513	0.8292
0.0143	20.31	380000	1.8971	0.8503	0.8293
0.0143	20.58	385000	1.8773	0.8512	0.8295
0.0136	20.84	390000	1.8520	0.8515	0.8277
0.0143	21.11	395000	1.9031	0.8509	0.8256
0.0124	21.38	400000	1.8758	0.8529	0.8302
0.0125	21.65	405000	2.0046	0.8491	0.8274
0.0141	21.91	410000	1.9489	0.8519	0.8285
0.016	22.18	415000	1.9530	0.8529	0.8283
0.0139	22.45	420000	1.9622	0.8521	0.8278
0.0107	22.71	425000	2.0015	0.8511	0.8250
0.0105	22.98	430000	1.9852	0.8534	0.8289
0.0113	23.25	435000	2.0320	0.8520	0.8265
0.0117	23.52	440000	1.9807	0.8530	0.8279
0.0104	23.78	445000	2.0138	0.8515	0.8279
0.0092	24.05	450000	2.0358	0.8520	0.8285
0.0106	24.32	455000	2.0526	0.8516	0.8281
0.0101	24.58	460000	2.0302	0.8532	0.8293
0.0096	24.85	465000	2.0315	0.8530	0.8286
0.0076	25.12	470000	2.0628	0.8530	0.8289
0.0081	25.39	475000	2.0493	0.8537	0.8287
0.0098	25.65	480000	2.0548	0.8550	0.8309
0.0087	25.92	485000	2.0356	0.8544	0.8313
0.0071	26.19	490000	2.0422	0.8545	0.8304
0.0099	26.46	495000	2.0361	0.8537	0.8276
0.0093	26.72	500000	2.0464	0.8545	0.8296
0.009	26.99	505000	2.0414	0.8531	0.8271
0.0081	27.26	510000	2.0718	0.8530	0.8269
0.0069	27.52	515000	2.0666	0.8543	0.8284
0.0085	27.79	520000	2.0561	0.8536	0.8267
0.0084	28.06	525000	2.0665	0.8540	0.8280
0.0072	28.33	530000	2.0960	0.8536	0.8284
0.0067	28.59	535000	2.0782	0.8535	0.8271
0.0062	28.86	540000	2.0716	0.8537	0.8280
0.008	29.13	545000	2.0839	0.8539	0.8286
0.0072	29.39	550000	2.0825	0.8539	0.8281
0.0059	29.66	555000	2.0781	0.8544	0.8286
0.0072	29.93	560000	2.0782	0.8544	0.8289

Framework versions

• Transformers 4.33.3
• Pytorch 2.1.1+cu121
• Datasets 2.14.5
• Tokenizers 0.13.3