t5_base-qg-ap-test

tiagoblima/t5_base-qg-ap-test

t5_base-qg-ap-test is a machine learning model from tiagoblima. 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

12% of all-time downloads

All-time downloads

Public

Parameters

223M

24.1 GB on disk

Likes

Public

Hugging Face

Repo makeup

Click a slice to open those files.

.safetensors892 MB · 100%

At a glance

Library: transformers
License: mit
Model type: t5
Access: Public
Created: Jan 17, 2024
Updated: Jan 24, 2024
SHA: 378a488c

Base models

unicamp-dl/ptt5-base-portuguese-vocab

Datasets

tiagoblima/du-qg-squadv1_pt

Model card

From the Hugging Face model README

t5_base-qg-ap-test

This model is a fine-tuned version of unicamp-dl/ptt5-base-portuguese-vocab on the tiagoblima/du-qg-squadv1_pt dataset. It achieves the following results on the evaluation set:

Loss: 0.0163

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: 0.0001
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06
lr_scheduler_type: linear
num_epochs: 100.0

Training results

Training Loss	Epoch	Step	Validation Loss
No log	1.0	1	12.8054
No log	2.0	2	10.7880
No log	3.0	3	8.8731
No log	4.0	4	7.4068
No log	5.0	5	6.4581
No log	6.0	6	5.6475
No log	7.0	7	4.9596
No log	8.0	8	4.5058
No log	9.0	9	4.0768
No log	10.0	10	3.7047
No log	11.0	11	3.4143
No log	12.0	12	3.1360
No log	13.0	13	2.8866
No log	14.0	14	2.6325
No log	15.0	15	2.3889
No log	16.0	16	2.1914
No log	17.0	17	2.0424
No log	18.0	18	1.9111
No log	19.0	19	1.7763
No log	20.0	20	1.6505
No log	21.0	21	1.5257
No log	22.0	22	1.4126
No log	23.0	23	1.3109
No log	24.0	24	1.2189
No log	25.0	25	1.1338
No log	26.0	26	1.0486
No log	27.0	27	0.9640
No log	28.0	28	0.8828
No log	29.0	29	0.8060
No log	30.0	30	0.7329
No log	31.0	31	0.6639
No log	32.0	32	0.6010
No log	33.0	33	0.5439
No log	34.0	34	0.4925
No log	35.0	35	0.4471
No log	36.0	36	0.4066
No log	37.0	37	0.3690
No log	38.0	38	0.3341
No log	39.0	39	0.3023
No log	40.0	40	0.2746
No log	41.0	41	0.2470
No log	42.0	42	0.2205
No log	43.0	43	0.1968
No log	44.0	44	0.1771
No log	45.0	45	0.1593
No log	46.0	46	0.1424
No log	47.0	47	0.1288
No log	48.0	48	0.1170
No log	49.0	49	0.1070
No log	50.0	50	0.0996
No log	51.0	51	0.0939
No log	52.0	52	0.0888
No log	53.0	53	0.0845
No log	54.0	54	0.0818
No log	55.0	55	0.0790
No log	56.0	56	0.0763
No log	57.0	57	0.0732
No log	58.0	58	0.0697
No log	59.0	59	0.0666
No log	60.0	60	0.0642
No log	61.0	61	0.0611
No log	62.0	62	0.0583
No log	63.0	63	0.0560
No log	64.0	64	0.0532
No log	65.0	65	0.0512
No log	66.0	66	0.0487
No log	67.0	67	0.0464
No log	68.0	68	0.0431
No log	69.0	69	0.0399
No log	70.0	70	0.0381
No log	71.0	71	0.0364
No log	72.0	72	0.0348
No log	73.0	73	0.0333
No log	74.0	74	0.0316
No log	75.0	75	0.0299
No log	76.0	76	0.0285
No log	77.0	77	0.0274
No log	78.0	78	0.0264
No log	79.0	79	0.0253
No log	80.0	80	0.0242
No log	81.0	81	0.0236
No log	82.0	82	0.0231
No log	83.0	83	0.0229
No log	84.0	84	0.0226
No log	85.0	85	0.0223
No log	86.0	86	0.0218
No log	87.0	87	0.0212
No log	88.0	88	0.0205
No log	89.0	89	0.0198
No log	90.0	90	0.0192
No log	91.0	91	0.0186
No log	92.0	92	0.0181
No log	93.0	93	0.0177
No log	94.0	94	0.0173
No log	95.0	95	0.0170
No log	96.0	96	0.0168
No log	97.0	97	0.0166
No log	98.0	98	0.0165
No log	99.0	99	0.0164
1.4009	100.0	100	0.0163

Framework versions

Transformers 4.35.2
Pytorch 2.0.0
Datasets 2.15.0
Tokenizers 0.15.0

Library: transformers
Type: t5
License: mit
Created: Jan 17, 2024
Updated: Jan 24, 2024