t5_small_instruction

Ryabaa/t5_small_instruction

t5_small_instruction is a machine learning model from Ryabaa. 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.

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

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Hugging Face

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At a glance

Library: transformers
Model type: t5
Access: Public
Created: Oct 2, 2024
Updated: Oct 5, 2024
SHA: 4192f5d0

Model card

From the Hugging Face model README

my_t5_small_test

This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.0284
Bleu: 94.5112
Gen Len: 28.0025

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: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 50
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Bleu	Gen Len
No log	1.0	25	0.2332	84.2783	26.685
No log	2.0	50	0.1832	87.343	27.29
No log	3.0	75	0.1488	88.2143	27.48
No log	4.0	100	0.1164	89.9573	28.0175
No log	5.0	125	0.0972	89.9952	27.9375
No log	6.0	150	0.0839	90.1522	27.975
No log	7.0	175	0.0708	90.5573	27.9775
No log	8.0	200	0.0634	91.0811	27.975
No log	9.0	225	0.0567	92.014	27.98
No log	10.0	250	0.0506	92.5003	28.0
No log	11.0	275	0.0471	92.6721	28.005
No log	12.0	300	0.0435	93.1332	28.1125
No log	13.0	325	0.0411	93.0801	28.01
No log	14.0	350	0.0391	93.4516	27.9675
No log	15.0	375	0.0375	93.6638	28.005
No log	16.0	400	0.0364	93.5842	28.0175
No log	17.0	425	0.0352	93.9818	28.09
No log	18.0	450	0.0342	94.273	28.1575
No log	19.0	475	0.0336	93.9818	28.08
0.1533	20.0	500	0.0330	93.9818	28.01
0.1533	21.0	525	0.0323	93.9553	28.05
0.1533	22.0	550	0.0320	93.8493	27.955
0.1533	23.0	575	0.0317	93.8758	27.955
0.1533	24.0	600	0.0312	93.8493	27.955
0.1533	25.0	625	0.0311	93.8758	27.8825
0.1533	26.0	650	0.0307	93.9818	27.93
0.1533	27.0	675	0.0305	94.1407	27.9925
0.1533	28.0	700	0.0303	94.1407	27.9475
0.1533	29.0	725	0.0301	94.1407	27.9075
0.1533	30.0	750	0.0299	94.273	27.9525
0.1533	31.0	775	0.0296	94.3524	28.0125
0.1533	32.0	800	0.0296	94.0877	27.895
0.1533	33.0	825	0.0294	94.1671	27.9275
0.1533	34.0	850	0.0292	94.1936	27.945
0.1533	35.0	875	0.0290	94.326	28.0475
0.1533	36.0	900	0.0289	94.3789	28.06
0.1533	37.0	925	0.0288	94.3789	28.0225
0.1533	38.0	950	0.0287	94.3789	28.035
0.1533	39.0	975	0.0286	94.3789	28.06
0.0545	40.0	1000	0.0285	94.4318	28.02
0.0545	41.0	1025	0.0285	94.4318	27.99
0.0545	42.0	1050	0.0285	94.3524	27.9625
0.0545	43.0	1075	0.0285	94.4053	27.975
0.0545	44.0	1100	0.0284	94.4847	27.9875
0.0545	45.0	1125	0.0284	94.5112	27.9875
0.0545	46.0	1150	0.0284	94.4847	27.9875
0.0545	47.0	1175	0.0284	94.4318	27.9825
0.0545	48.0	1200	0.0284	94.564	27.995
0.0545	49.0	1225	0.0284	94.5112	28.0025
0.0545	50.0	1250	0.0284	94.5112	28.0025

Framework versions

Transformers 4.44.2
Pytorch 2.4.1+cu121
Datasets 3.0.1
Tokenizers 0.19.1

Library: transformers
Type: t5
Created: Oct 2, 2024
Updated: Oct 5, 2024