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