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open-calm-7b-summarizer-lora

Text Generation
PEFT
Japanese
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open-calm-7b-summarizer-lora
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---
library_name: peft
language:
- ja
pipeline_tag: text-generation
---

# Fine-tuned OpenCALM-7B Adapters for Meeting Summarization

## Description

These are weights for LoRA adapters fine-tuned on the OpenCALM-7B ([Andonian et al., 2021](https://huggingface.co/cyberagent/open-calm-7b)) model for Japanese meeting summarization.

## Usage

### Load model and tokenizer

Loading the model in the 4-bit quantized format is recommended to get reliable results since these LoRA adapters were trained by using QLoRA ([Dettmers et al., 2023](https://arxiv.org/abs/2305.14314)).


```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
from peft import PeftModel

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.float16
)

tokenizer = AutoTokenizer.from_pretrained("cyberagent/open-calm-7b")
model = AutoModelForCausalLM.from_pretrained(
    "cyberagent/open-calm-7b", 
    quantization_config=bnb_config, 
    device_map="auto"
)

model = PeftModel.from_pretrained(model, "haih2/open-calm-7b-summarizer-lora")
```

### Generate summary

In the prompt provided to the model:
* The first part is the length of the summary to be generated,
* and The second part is the source meeting to be summarized.

```python
prompt = "この段落の要約50字以内生成:次に、私立高校の生徒に対する留学支援についてでございますが、都内の私立高校は、それぞれの学校における教育方針に基づきまして、生徒の留学先として海外の学校と提携するなど、既にさまざまな独自の取り組みを進めております。\\nこうした状況等を踏まえ、私立高校を対象とした留学支援のあり方について、今後検討してまいります。\\n\n"

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
with torch.no_grad():
    tokens = model.generate(
        **inputs,
        max_new_tokens=256,
        do_sample=True,
        temperature=0.7,
        top_k=32,
        top_p=0.9,
        repetition_penalty=1.0,
        no_repeat_ngram_size=0,
        pad_token_id=tokenizer.pad_token_id,
    )
    
output = tokenizer.decode(tokens[0], skip_special_tokens=True)
print(output)
```

## Prompt Format

Any prompt is fine, but it is suggested to have `length` and `source` parts as follows:

```
"この段落を{length}に要約しなさい:{source}\n要約:"
```

or

```
"この段落の要約{length}生成:{source}\n"
```

## Fine-tuning Details

### Dataset

* [Congressional meeting's minutes](https://github.com/kmr-y/NTCIR14-QALab-PoliInfo-FormalRunDataset/tree/master) provided by QA Lab PoliInfo.

### Fine-tuning procedure

The OpenCALM-7B model was fine-tuned on the above dataset using the QLoRA method with prompt `この段落の要約{length}生成:{source}\n`. We outline the following hyperparameters:

|||
|----------------|----------------:|
| **Optimizer** <br> &emsp; beta_1 <br> &emsp; beta_2 <br> &emsp; weight decay | AdamW <br> 0.9 <br> 0.999 <br> 0.01 |
| **Learning rate** <br> &emsp; scheduler type | 2e-5 <br> linear |
| **LoRA** <br> &emsp; target modules <br> &emsp; r <br> &emsp; alpha <br> &emsp; dropout | <br> query_key_value, dense <br> 4 <br> 64 <br> 0.05 |
| **QLoRA** <br> &emsp; compute dtype <br> &emsp; storage dtype <br> &emsp; quantization strategy | <br> float16 <br> nf4 <br> double quantization |
| **Sequence length**             | 1536 |
| **Batch size**                  | 4    |
| **Gradient accumulation steps** | 2    |
| **Epochs**                      | 10   |
| **Warmup steps**                | 200  |

## Evaluation

### Testing data & Metric

We evaluated the model on two sets: one for *multi-topic* summarization and the other for *single-topic* summarization. ROUGE-L (F1-score-based) with the [Japanese Mecab tokenizer](https://pypi.org/project/mecab-python3/) was used as the evaluation metric.

### Results

| Solution/Model | ROUGE-L <br> (multi-topic) | ROUGE-L <br> (single-topic) |
|----------------|:--------------------------:|:---------------------------:|
|1st place solution*  |34.12      |**34.44**|
|2nd place solution*  |32.79      |33.65    |
|*OpenCALM-7B (QLoRA)*|***36.75***|*33.31*  |

*\* These scores are extracted from this [leaderboard](https://github.com/PoliInfo/PoliInfo.github.io/blob/master/FormalRunResult.md) for the summarization task.*