newsdiscourse-model

alex2awesome/newsdiscourse-model

newsdiscourse-model is a machine learning model from alex2awesome. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as mit.

should probably proofread and complete it, then remove this comment. --

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At a glance

Library: transformers
License: mit
Model type: roberta
Access: Public
Created: Jul 7, 2023
Updated: Nov 17, 2023
SHA: ca101360

Model card

From the Hugging Face model README

newsdiscourse-model

This model is a fine-tuned version of roberta-base on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 1.9458
F1: 0.5610

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: 1
eval_batch_size: 1
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10.0

Training results

Training Loss	Epoch	Step	Validation Loss	F1
No log	0.14	100	1.4843	0.2881
No log	0.28	200	1.3307	0.3841
No log	0.43	300	1.2427	0.3991
No log	0.57	400	1.2590	0.4899
1.2399	0.71	500	1.2648	0.4658
1.2399	0.85	600	1.2064	0.4988
1.2399	1.0	700	1.2564	0.4668
1.2399	1.14	800	1.2062	0.4912
1.2399	1.28	900	1.1202	0.4904
0.9315	1.42	1000	1.1924	0.5188
0.9315	1.57	1100	1.1627	0.5034
0.9315	1.71	1200	1.1093	0.5111
0.9315	1.85	1300	1.1332	0.5166
0.9315	1.99	1400	1.1558	0.5285
0.8604	2.14	1500	1.2531	0.5122
0.8604	2.28	1600	1.2830	0.5414
0.8604	2.42	1700	1.2550	0.5335
0.8604	2.56	1800	1.1928	0.5120
0.8604	2.71	1900	1.2441	0.5308
0.7406	2.85	2000	1.2791	0.5400
0.7406	2.99	2100	1.2354	0.5485
0.7406	3.13	2200	1.3047	0.5258
0.7406	3.28	2300	1.3636	0.5640
0.7406	3.42	2400	1.2963	0.5747
0.6355	3.56	2500	1.2897	0.5123
0.6355	3.7	2600	1.3225	0.5481
0.6355	3.85	2700	1.3197	0.5467
0.6355	3.99	2800	1.2346	0.5353
0.6355	4.13	2900	1.3397	0.5629
0.5698	4.27	3000	1.4259	0.5622
0.5698	4.42	3100	1.3702	0.5607
0.5698	4.56	3200	1.4294	0.5584
0.5698	4.7	3300	1.5041	0.5459
0.5698	4.84	3400	1.4156	0.5394
0.5069	4.99	3500	1.4384	0.5527
0.5069	5.13	3600	1.5322	0.5439
0.5069	5.27	3700	1.4899	0.5557
0.5069	5.41	3800	1.4526	0.5391
0.5069	5.56	3900	1.5027	0.5607
0.4127	5.7	4000	1.5458	0.5662
0.4127	5.84	4100	1.5080	0.5537
0.4127	5.98	4200	1.5936	0.5483
0.4127	6.13	4300	1.7079	0.5401
0.4127	6.27	4400	1.5939	0.5521
0.3574	6.41	4500	1.5588	0.5702
0.3574	6.55	4600	1.6363	0.5568
0.3574	6.7	4700	1.6629	0.5535
0.3574	6.84	4800	1.6523	0.5662
0.3574	6.98	4900	1.7245	0.5461
0.3417	7.12	5000	1.6766	0.5629
0.3417	7.26	5100	1.8219	0.5450
0.3417	7.41	5200	1.7422	0.5533
0.3417	7.55	5300	1.8250	0.5564
0.3417	7.69	5400	1.7744	0.5600
0.2852	7.83	5500	1.7919	0.5549
0.2852	7.98	5600	1.7604	0.5639
0.2852	8.12	5700	1.7660	0.5599
0.2852	8.26	5800	1.7323	0.5600
0.2852	8.4	5900	1.9174	0.5529
0.2606	8.55	6000	1.8664	0.5611
0.2606	8.69	6100	1.9191	0.5568
0.2606	8.83	6200	1.8900	0.5565
0.2606	8.97	6300	1.9376	0.5524
0.2606	9.12	6400	1.9220	0.5594
0.2274	9.26	6500	1.9188	0.5585
0.2274	9.4	6600	1.9459	0.5527
0.2274	9.54	6700	1.9439	0.5543
0.2274	9.69	6800	1.9437	0.5596
0.2274	9.83	6900	1.9484	0.5581
0.2258	9.97	7000	1.9458	0.5610

Framework versions

Transformers 4.30.2
Pytorch 2.0.1+cu117
Datasets 2.13.1
Tokenizers 0.13.3

Library: transformers
Type: roberta
License: mit
Created: Jul 7, 2023
Updated: Nov 17, 2023