Improve language tag (#1)

- Improve language tag (68d1f3e6f04c6c977c8965444a4b339c4a35e419)


Co-authored-by: Loïck BOURDOIS <[email protected]>

README.md

@@ -1,35 +1,47 @@
----
-license: apache-2.0
-datasets:
-- AI-MO/NuminaMath-TIR
-language:
-- en
-metrics:
-- accuracy
-base_model:
-- Qwen/Qwen2.5-0.5B-Instruct
----
-# NeuroCoder Qwen2.5-0.5B-Instruct-MemoryR
-
-## Overview
-
-This is the Hugging Face checkpoint of **Qwen2.5-0.5B-Instruct-MemoryR**, a memory-augmented RL-tuned model based on Qwen2.5. 
-
-The model is introduced and analyzed in our paper: https://arxiv.org/abs/2504.02273
-
-## Usage
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-# Load tokenizer and model
-tokenizer = AutoTokenizer.from_pretrained("neurocoder/Qwen2.5-0.5B-Instruct-MemoryR")
-model = AutoModelForCausalLM.from_pretrained("neurocoder/Qwen2.5-0.5B-Instruct-MemoryR")
-
-# Example input
-prompt = "What is the capital of France?"
-inputs = tokenizer(prompt, return_tensors="pt")
-
-# Generate output
-outputs = model.generate(**inputs, max_new_tokens=50)
-print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+---
+license: apache-2.0
+datasets:
+- AI-MO/NuminaMath-TIR
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+metrics:
+- accuracy
+base_model:
+- Qwen/Qwen2.5-0.5B-Instruct
+---
+# NeuroCoder Qwen2.5-0.5B-Instruct-MemoryR
+
+## Overview
+
+This is the Hugging Face checkpoint of **Qwen2.5-0.5B-Instruct-MemoryR**, a memory-augmented RL-tuned model based on Qwen2.5. 
+
+The model is introduced and analyzed in our paper: https://arxiv.org/abs/2504.02273
+
+## Usage
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained("neurocoder/Qwen2.5-0.5B-Instruct-MemoryR")
+model = AutoModelForCausalLM.from_pretrained("neurocoder/Qwen2.5-0.5B-Instruct-MemoryR")
+
+# Example input
+prompt = "What is the capital of France?"
+inputs = tokenizer(prompt, return_tensors="pt")
+
+# Generate output
+outputs = model.generate(**inputs, max_new_tokens=50)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
 ```