|
--- |
|
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 |
|
|
