prompts = [
        "Determine if the following sentences are equivalent: Sentence 1: {sentence1} Sentence 2: {sentence2}. Answer: ",
        "Are these sentences saying the same thing? First: {sentence1} Second: {sentence2}. Response: ",
        "Check sentence equivalence: \"{sentence1}\" versus \"{sentence2}\". Result: ",
    ]

Concatenate the prompts, the two sentences and the label as so:

python

input_text = prompt.format(sentence1=sentence1, sentence2=sentence2)    
input_text += " " + responses[label]

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 1
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 4
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10
mixed_precision_training: Native AMP

Training results

Final results: {'loss': 0.0949, 'grad_norm': 5.0146379470825195, 'learning_rate': 6.000000000000001e-07, 'epoch': 10.0}

Average results: {'train_runtime': 363.2148, 'train_samples_per_second': 5.506, 'train_steps_per_second': 1.377, 'train_loss': 0.4939311617612839, 'epoch': 10.0}

Framework versions

Transformers 4.38.1
Pytorch 2.2.0+cu121
Datasets 2.17.0
Tokenizers 0.15.2

alonzogarbanzo/Bloom-1b7-glue-mrpc-IT-baseline

Bloom-1b7-glue-mrpc-IT-baseline

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

alonzogarbanzo/
Bloom-1b7-glue-mrpc-IT-baseline