Update README.md

README.md

@@ -1,3 +1,93 @@
 ---
 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!