rut5-base-absum-tech-support-calls

abletobetable/rut5-base-absum-tech-support-calls

rut5-base-absum-tech-support-calls is a machine learning model from abletobetable. 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. --

Downloads · 30 days

3% of all-time downloads

All-time downloads

383

Public

Repo size

7.8 GB

Likes

Public

Hugging Face

Repo makeup

Click a slice to open those files.

.bin977 MB · 100%

At a glance

Library: transformers
License: mit
Model type: t5
Access: Public
Created: May 24, 2023
Updated: May 24, 2023
SHA: b25a0d02

Model card

From the Hugging Face model README

rut5-base-absum-tech-support-calls

This model is a fine-tuned version of cointegrated/rut5-base-absum on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 1.4464
Rouge-1: 0.5076
Rouge-2: 0.3897
Rouge-l: 0.4945
Gen Len: 15.75
Avg Rouge F: 0.4639

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: 2e-05
train_batch_size: 3
eval_batch_size: 1
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 200
num_epochs: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge-1	Rouge-2	Rouge-l	Gen Len	Avg Rouge F
2.6017	2.78	50	2.0030	0.0	0.0	0.0	8.125	0.0
2.1413	5.56	100	1.5154	0.1125	0.0317	0.0958	11.5	0.08
1.6874	8.33	150	1.2364	0.3417	0.2312	0.325	13.25	0.2993
1.2272	11.11	200	1.1259	0.3605	0.2437	0.3291	14.25	0.3111
0.9384	13.89	250	1.0853	0.4505	0.3	0.4211	13.5	0.3905
0.7071	16.67	300	1.0607	0.3559	0.1368	0.3133	14.875	0.2687
0.5871	19.44	350	1.0346	0.5377	0.4194	0.5126	16.0	0.4899
0.4194	22.22	400	1.0672	0.5079	0.3819	0.4829	15.5	0.4576
0.3685	25.0	450	1.1284	0.5029	0.3835	0.4897	14.75	0.4587
0.2884	27.78	500	1.1729	0.5427	0.421	0.5164	15.875	0.4933
0.2368	30.56	550	1.1640	0.5326	0.421	0.5195	15.25	0.491
0.195	33.33	600	1.2053	0.5326	0.421	0.5195	15.25	0.491
0.1667	36.11	650	1.2525	0.4245	0.2717	0.4114	16.125	0.3692
0.1491	38.89	700	1.3346	0.5032	0.3897	0.4901	16.0	0.461
0.1122	41.67	750	1.3354	0.5094	0.4062	0.5094	15.375	0.475
0.1166	44.44	800	1.3685	0.5076	0.3897	0.4945	15.625	0.4639
0.0973	47.22	850	1.4157	0.5076	0.3897	0.4945	15.375	0.4639
0.0944	50.0	900	1.4523	0.5095	0.3897	0.4963	15.125	0.4652
0.0744	52.78	950	1.4221	0.5326	0.421	0.5195	15.25	0.491
0.0745	55.56	1000	1.4464	0.5076	0.3897	0.4945	15.75	0.4639

Framework versions

Transformers 4.29.2
Pytorch 2.0.1+cu118
Tokenizers 0.13.3

Library: transformers
Type: t5
License: mit
Created: May 24, 2023
Updated: May 24, 2023