t5-base-iterater

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T5 (base) fine-tuned on IteraTeR

This model is a fine-tuned version of t5-base on an IteraTeR dataset. It achieves the following results on the evaluation set:

• Loss: 0.2580

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: 5e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 2

Training results

How to use

from transformers import T5ForConditionalGeneration, T5TokenizerFast
MODEL_CKPT = 'mrm8488/t5-base-iterater'

tokenizer = T5TokenizerFast.from_pretrained(MODEL_CKPT)
model = T5ForConditionalGeneration.from_pretrained(MODEL_CKPT)

def predict(intent, text):
   input_text =  f"<{intent}>  {text}"
   features = tokenizer([input_text], return_tensors='pt')
   output = model.generate(input_ids=features['input_ids'], 
               attention_mask=features['attention_mask'], max_length=128, num_beams=8)
   return tokenizer.decode(output[0], skip_special_tokens=True)
   
text = "Delay-based schemes have the potential to resolve this last packet problem by scheduling the link based on the delay for the packet has encountered."
intent = "clarity"

predict(intent, text)
# Delay-based schemes have the potential to resolve this last packet problem by scheduling the link based on the delay the packet has encountered.

Framework versions

• Transformers 4.18.0.dev0
• Pytorch 1.10.0+cu111
• Datasets 2.0.0
• Tokenizers 0.11.6