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---
license: cc-by-nc-sa-4.0
datasets:
- NorGLM/NO-CNN-DailyMail
language:
- 'no'
pipeline_tag: summarization
---
# Model Card
NorGPT-3B-summarization-peft is trained on top of [NorGPT-3B](https://huggingface.co/NorGLM/NorGPT-3B) model using RLHF strategy on [NO-CNN-DailyMail](https://huggingface.co/datasets/NorGLM/NO-CNN-DailyMail) dataset.
Different from step 2 in the original RLHF, we trained the reward model by estimating the semantic similarity between the candidate generated text and the human annotated summary (golden summary) using the [NorBERT](https://huggingface.co/ltg/norbert) model. Generated summaries with higher cosine similarity to the golden summary will be ranked higher in the training of the reward model.
Prompt format:
```
Summarise the article:\\n{article} |||\\n{positive_sample}
```
Inference prompt:
```
Summarise the article:\\n{article} |||\\n
```
## Training Split
We split data to train on step 1-step 3 for RLHF:
| | #samples |
|-------|---------------------|
| step 1 | 61181 |
| step 2 | 16798 |
| step 3 | 9758 |
## Run the Model
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_id = "NorGLM/NorGPT-3B-rfhl-summarization"
tokenizer = AutoTokenizer.from_pretrained(model_id)
tokenizer.pad_token = tokenizer.eos_token
model = AutoModelForCausalLM.from_pretrained(
model_id,
device_map='auto',
torch_dtype=torch.bfloat16
)
```
## Inference on test set
Load the model to evaluate on the test set of NO-CNN-DailyMail dataset:
```python
def generate_texts(model, tokenizer, prompts, max_seq_length=200, do_sample=True, top_p=0.95, top_k=10):
# prompts are a list of news articles
results = []
cnt = 0
for prompt in prompts:
cnt += 1
pro_len = len(prompt.split())
if pro_len>1024:
results.append('')
continue
prompt = 'Summarise the article:\\n' + prompt + ' |||\\n'
model_inputs = tokenizer(prompt, return_tensors='pt').to(torch_device)
output = model.generate(**model_inputs, do_sample=False, max_new_tokens=max_seq_length)
result = tokenizer.decode(output[0], skip_special_tokens=True)
result = result.split("|||\\n")[-1]
results.append(result)
return results
print("--LOADING EVAL DATAS---")
eval_data = load_dataset("NorGLM/NO-CNN-DailyMail", data_files="test.csv")
prompts = eval_data['train']['article']
positive_samples = eval_data['train']['positive_sample']
print("--MAKING PREDICTIONS---")
model.eval()
output_file = <output file name>
with torch.no_grad():
results = generate_texts(model, tokenizer, prompts)
df = pd.DataFrame({'article':prompts, 'generated_text':results, 'positive_sample':positive_samples})
print("Save results to csv file...")
df.to_csv(output_file)
```
## Note
More training details will be released soon! |