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XLM_R_Extractive_QA_Vi_En_Zh – AI Model by FredDYyy | AlphaNeural AI
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FredDYyy
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XLM_R_Extractive_QA_Vi_En_Zh
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transformers
pytorch
tensorboard
xlm-roberta
question-answering
generated_from_trainer
mit
endpoints_compatible
us
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XLM_R_Extractive_QA_Vi_En_Zh
This model is a fine-tuned version of
xlm-roberta-base
on the None dataset. It achieves the following results on the evaluation set:
Loss: 2.4547
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: 4
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 3
Training results
Training Loss
Epoch
Step
Validation Loss
2.0082
1.0
3245
2.3766
1.681
2.0
6491
2.3099
1.4326
3.0
9735
2.4547
Framework versions
Transformers 4.28.1
Pytorch 2.0.0+cu118
Datasets 2.12.0
Tokenizers 0.13.3
