readme: ---
  license: apache-2.0
  language:
   - en
  datasets:
  - wanyu/IteraTeR_full_sent
  tags:
  - generated_from_trainer
  - IteraTeR
  
  widget:
   - text: "<clarity>  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."
  
  model-index:
  - name: t5-base-iterater
    results: []
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # T5 (base) fine-tuned on IteraTeR
  
  This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an [IteraTeR](https://huggingface.co/datasets/wanyu/IteraTeR_full_sent) 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
  
  | Training Loss | Epoch | Step  | Validation Loss |
  |:-------------:|:-----:|:-----:|:---------------:|
  | 0.3286        | 0.09  | 2000  | 0.3010          |
  | 0.3194        | 0.18  | 4000  | 0.2872          |
  | 0.3208        | 0.27  | 6000  | 0.2792          |
  | 0.3091        | 0.36  | 8000  | 0.2731          |
  | 0.3164        | 0.45  | 10000 | 0.2678          |
  | 0.2941        | 0.54  | 12000 | 0.2682          |
  | 0.2981        | 0.63  | 14000 | 0.2696          |
  | 0.2975        | 0.72  | 16000 | 0.2643          |
  | 0.3109        | 0.81  | 18000 | 0.2624          |
  | 0.2965        | 0.9   | 20000 | 0.2648          |
  | 0.3053        | 0.99  | 22000 | 0.2627          |
  | 0.2779        | 1.08  | 24000 | 0.2632          |
  | 0.2692        | 1.17  | 26000 | 0.2608          |
  | 0.2755        | 1.26  | 28000 | 0.2600          |
  | 0.2771        | 1.35  | 30000 | 0.2584          |
  | 0.2774        | 1.44  | 32000 | 0.2609          |
  | 0.2976        | 1.53  | 34000 | 0.2593          |
  | 0.2646        | 1.62  | 36000 | 0.2616          |
  | 0.2705        | 1.71  | 38000 | 0.2574          |
  | 0.2714        | 1.8   | 40000 | 0.2577          |
  | 0.2857        | 1.9   | 42000 | 0.2576          |
  | 0.2832        | 1.99  | 44000 | 0.2580          |
  
  
  ### How to use
  
  ```py
  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
  