Update README.md

README.md

@@ -160,8 +160,6 @@ results = []
 loop = 0
 for data in tqdm(tasks):
   task_id = data["task_id"]
-  if task_id != 66 and task_id != 72:
-     continue
   input = data["input"]
   # in context learning用のプロンプト
   icl_prompt = create_icl_prompt(input, sample_tasks, task_id)
@@ -170,7 +168,6 @@ for data in tqdm(tasks):
 {input}
 ### 回答
 """  
-  # 1回目
   tokenized_input = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt").to(model.device)
   with torch.no_grad():
       outputs = model.generate(
@@ -201,11 +198,21 @@ for data in tqdm(tasks):
   
   print(f"task_id: {data['task_id']}, prompt: {prompt}, output: {output}")
   
-  #break
+```
+
+### 5. Dump results
+```python
+import re
+model_name = re.sub(".*/", "", model_name)
+with open(f"./{model_name}-outputs.jsonl", 'w', encoding='utf-8') as f:
+    for result in results:
+        json.dump(result, f, ensure_ascii=False)  # ensure_ascii=False for handling non-ASCII characters
+        f.write('\n')
 ```
 
 # Uploaded  model
 
-- **Developed by:** oxygen65
+- **Developed by:** oxygen65  
+
 This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
 [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)