metadata
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: []
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
| 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
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