|
--- |
|
language: |
|
- zh |
|
- en |
|
library_name: transformers |
|
tags: |
|
- LoRA |
|
- rewrite |
|
- question rewrite |
|
- query rewrite |
|
--- |
|
|
|
# Model Card for Model ID |
|
|
|
<!-- Provide a quick summary of what the model is/does. --> |
|
This is a fine-tuned model for question or statements rewrite task focused on Traditional Chinese specifically. |
|
In this version , we have adjusted the way the model calculates loss. |
|
(**The original training process (i.e. SFTTrainer class from trl) calculates CE on whole prompt template.**) |
|
|
|
In order to prevent the model from copying the original sentence, the total loss we use will be counted as three parts : |
|
1. Context Loss (from the beginning to ```<rephrased>```) |
|
2. Answer Loss (from ```<rephrased>``` to ```</rephrased>```) |
|
3. Variety Loss (VTLoss) , it calculates the IOU of orignal tokenized sentence and rewritten tokenized sentence , trying to encourage the model |
|
to generate as diverse text as possible. |
|
|
|
Noted that the answer loss will take a larger weight than context loss since the answer is more important part that we shall take care of. |
|
|
|
## Model Details |
|
the prompt template should be used as follow: |
|
``` |
|
<task> |
|
你是一名熱於助人的AI小幫手，請將敘述語句或者問句變得更加通順與簡潔。 |
|
</task> |
|
|
|
原始句子: |
|
<origin> |
|
{before} |
|
</origin> |
|
|
|
修改後: |
|
<rephrased> |
|
{after} |
|
</rephrased> |
|
``` |
|
|
|
Noted that {before} {after} are the original question/statement and rewritten question/statement respcetively. |
|
Moreover , this model is not the best rewrite tool compared with many open source LLMs , it is a trial version. |
|
But we'll still make improvements on it. |
|
|
|
### Model Description |
|
|
|
<!-- Provide a longer summary of what this model is. --> |
|
|
|
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. |
|
|
|
- **Developed by:** [--] |
|
- **Funded by [optional]:** [--] |
|
- **Shared by [optional]:** [--] |
|
- **Model type:** [--] |
|
- **Language(s) (NLP):** [Traditional Chinese] |
|
- **License:** [--] |
|
- **Finetuned from model [optional]:** [Taiwan LLM base v2.0] |
|
|
|
|
|
## Training Details |
|
|
|
### Training Data |
|
|
|
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> |
|
Generate from GPT4o and artificial human feedback. |
|
Custom Traditional Chinese BenchMark Dataset , with rewritten answers came from Gemini. |
|
Also , the evaluation task is assigned to GPTo with custom rubrics. |
|
|
|
[More Information Needed] |
|
|
|
### Training Procedure |
|
|
|
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> |
|
|
|
|
|
#### Training Hyperparameters |
|
|
|
- **Training regime:** [QLoRA] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> |
|
|
|
|
|
## More Information [optional] |
|
|
|
[--] |
|
|
|
## Model Card Authors [optional] |
|
|
|
[--] |
|
|
|
## Model Card Contact |
|
|
|
[--] |
