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mt5-small-finetuned-summarization – AI Model by ahmedshark | AlphaNeural AI
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ahmedshark
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mt5-small-finetuned-summarization
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transformers
tensorboard
safetensors
mt5
text2text-generation
summarization
generated_from_trainer
google/mt5-small
finetune
apache-2.0
endpoints_compatible
us
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mt5-small-finetuned-summarization
This model is a fine-tuned version of
google/mt5-small
on an unknown dataset. It achieves the following results on the evaluation set:
Loss: 2.5678
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: 0.0005
train_batch_size: 8
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 90
num_epochs: 1
Training results
Training Loss
Epoch
Step
Validation Loss
3.0615
0.128
100
3.1638
3.461
0.256
200
2.8180
3.2633
0.384
300
2.7739
3.2169
0.512
400
2.6986
3.1099
0.64
500
2.6516
3.1311
0.768
600
2.6042
3.0676
0.896
700
2.5785
Framework versions
Transformers 4.45.1
Pytorch 2.4.0
Datasets 3.0.1
Tokenizers 0.20.0
