Update README.md

README.md

@@ -1,3 +1,93 @@
 ---
 license: cc-by-nc-sa-4.0
+datasets:
+- NorGLM/NO-ConvAI2
+language:
+- 'no'
+pipeline_tag: text-generation
 ---
+
+# Model Card
+
+NorGPT-369M-conversation-peft is trained on top of [NorGPT-369M](https://huggingface.co/NorGLM/NorGPT-369M) model on [NO-ConvAI2](https://huggingface.co/datasets/NorGLM/NO-ConvAI2) dataset.
+
+Prompt format:
+```
+Human: {prompt} Robot: |||\n {answer}
+```
+
+Inference prompt:
+```
+Human: {prompt} Robot: |||\n
+```
+
+## Run the Model
+```python
+from peft import PeftModel, PeftConfig
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+from tqdm.auto import tqdm
+
+source_model_id = "NorGLM/NorGPT-369M"
+peft_model_id = "NorGLM/NorGPT-369M-conversation-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 Example
+Load the model to evaluate on the test set of NO-CNN/DailyMail dataset:
+```python
+def load_and_prepare_data_last_prompt(df):
+    """ Load and spearates last prompt from prompt """
+     # id, turn_id, prompt, answer
+    last_prompt = ["Human: " + df['prompt']
+                   [i].split("Human:")[-1] for i in range(len(df))]
+    df['last_prompt'] = last_prompt
+    return df
+
+def generate_text(text, max_length=200):
+    # generate with greedy search
+    model_inputs = tokenizer(text, return_attention_mask=True, return_tensors="pt",
+              padding=True, truncation=True,  max_length=tokenizer_max_len)
+
+    with torch.no_grad():
+        output_tokens = model.generate(
+                  **model_inputs, max_new_tokens=50, pad_token_id=tokenizer.eos_token_id)
+
+    text_outputs = [tokenizer.decode(
+                x, skip_special_tokens=True) for x in output_tokens]
+
+    return text_outputs
+
+print("--LOADING EVAL DATAS---")
+eval_data = load_dataset("NorGLM/NO-ConvAI2", data_files="test_PersonaChat_prompt.json")
+prompts = eval_data['train']['prompt']
+positive_samples = eval_data['train']['answer']
+
+print("--MAKING PREDICTIONS---")
+model.eval()
+
+output_file = <output file name>
+generated_text = []
+
+for prompt in tqdm(prompts):
+    generated_text.append(generate_text(prompt, max_length=tokenizer_max_len))
+
+df = pd.DataFrame({'prompts':prompts, 'generated_text':generated_text, 'positive_sample':positive_samples})
+
+print("Save results to csv file...")
+df.to_csv(output_file)
+
+```
+
+## Note
+More training details will be released soon!