---
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