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---
license: cc-by-nc-sa-4.0
datasets:
- NorGLM/NO-CNN-DailyMail
language:
- 'no'
pipeline_tag: summarization
---
# Model Card
NorLlama-3B-summarization-peft is trained on top of [NorLlama-3B](https://huggingface.co/NorGLM/NorLlama-3B) model on [NO-CNN-DailyMail](https://huggingface.co/datasets/NorGLM/NO-CNN-DailyMail) dataset.
Prompt format:
```
Summarise the article:\\n{article} |||\\n{positive_sample}
```
Inference prompt:
```
Summarise the article:\\n{article} |||\\n
```
## Run the Model
```python
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
source_model_id = "NorGLM/NorLlama-3B"
peft_model_id = "NorGLM/NorLlama-3B-summarization-peft"
config = PeftConfig.from_pretrained(peft_model_id)
model = AutoModelForCausalLM.from_pretrained(source_model_id, device_map='balanced')
tokenizer_max_len = 2048
tokenizer_config = {'pretrained_model_name_or_path': source_model_id,
'max_len': tokenizer_max_len}
tokenizer = tokenizer = AutoTokenizer.from_pretrained(**tokenizer_config)
tokenizer.pad_token = tokenizer.eos_token
model = PeftModel.from_pretrained(model, peft_model_id)
```
## 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! |