Improve language tag

README.md

@@ -1,96 +1,107 @@
----
-library_name: transformers
-tags:
-- text-generation-inference
-- code
-- fact
-- math
-- short-context-reasoning
-license: apache-2.0
-language:
-- en
-- zh
-base_model:
-- Qwen/Qwen2.5-0.5B-Instruct
-pipeline_tag: text-generation
----
-![5.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/UWb3145w-rFhJLCytbsBV.png)
-
-# **TESS-QwenRe-Fact-0.5B**
-
-> **TESS-QwenRe-Fact-0.5B** is a **compact fact-checking and short reasoning model** built upon **Qwen2.5 0.5B**. Designed for rapid response, real-world fact verification, and concise logical reasoning, this lightweight model is ideal for digital assistants, quick-response tools, and misinformation detection systems in **English** and **Chinese**.
-
-## **Key Features**
-
-1. **Fact Verification & Correction**  
-   Trained to analyze factual accuracy in statements and offer corrected or clarified responses, making it ideal for real-time verification tasks and misinformation mitigation.
-
-2. **Concise Reasoning**  
-   Specializes in **short-form reasoning**, capable of analyzing and explaining claims, decisions, or statements in just a few logical steps — perfect for Q&A bots and assistant systems.
-
-3. **Multilingual Support (EN + ZH)**  
-   Supports fact-checking tasks in both **English** and **Simplified Chinese**, enhancing accessibility for bilingual or regional use cases.
-
-4. **Built on Qwen2.5 0.5B**  
-   Combines the latest architectural improvements from **Qwen2.5** with a small parameter footprint (0.5B), optimized for **speed**, **efficiency**, and **edge-device compatibility**.
-
-5. **Prompt-Friendly Output**  
-   Responds well to well-structured queries, returning clean, interpretable answers — especially for true/false classification, source-based fact validation, and yes/no reasoning.
-
-## **Quickstart with Transformers**
-
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-model_name = "prithivMLmods/TESS-QwenRe-Fact-0.5B"
-
-model = AutoModelForCausalLM.from_pretrained(
-    model_name,
-    torch_dtype="auto",
-    device_map="auto"
-)
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-
-prompt = "Is the capital of Australia Sydney? Explain briefly."
-messages = [
-    {"role": "system", "content": "You are a concise and accurate fact-checking assistant."},
-    {"role": "user", "content": prompt}
-]
-text = tokenizer.apply_chat_template(
-    messages,
-    tokenize=False,
-    add_generation_prompt=True
-)
-model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
-
-generated_ids = model.generate(
-    **model_inputs,
-    max_new_tokens=256
-)
-generated_ids = [
-    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
-]
-
-response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
-```
-
-## **Intended Use**
-
-- **Fact-Checking Assistants**: Quickly verify factual claims in conversation or content.
-- **Digital Truth Detectors**: Misinformation and rumor detection in social feeds or news summaries.
-- **Micro-Reasoning Bots**: Smart agents for short-form logic and rationale generation.
-- **Multilingual Knowledge Tools**: Fact reasoning in **EN/ZH**, ideal for diverse platforms.
-
-## **Limitations**
-
-1. **Limited Depth**  
-   Focused on **short-form reasoning** — may not perform well on multi-step or abstract logic tasks.
-
-2. **Compact Model Scale**  
-   At 0.5B parameters, it prioritizes **efficiency over complexity** — best for straightforward fact-based tasks.
-
-3. **Language & Topic Bias**  
-   Inherits limitations and biases from its base model Qwen2.5 0.5B. Use carefully in sensitive contexts.
-
-4. **Prompt Clarity Required**  
+---
+library_name: transformers
+tags:
+- text-generation-inference
+- code
+- fact
+- math
+- short-context-reasoning
+license: apache-2.0
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+base_model:
+- Qwen/Qwen2.5-0.5B-Instruct
+pipeline_tag: text-generation
+---
+![5.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/UWb3145w-rFhJLCytbsBV.png)
+
+# **TESS-QwenRe-Fact-0.5B**
+
+> **TESS-QwenRe-Fact-0.5B** is a **compact fact-checking and short reasoning model** built upon **Qwen2.5 0.5B**. Designed for rapid response, real-world fact verification, and concise logical reasoning, this lightweight model is ideal for digital assistants, quick-response tools, and misinformation detection systems in **English** and **Chinese**.
+
+## **Key Features**
+
+1. **Fact Verification & Correction**  
+   Trained to analyze factual accuracy in statements and offer corrected or clarified responses, making it ideal for real-time verification tasks and misinformation mitigation.
+
+2. **Concise Reasoning**  
+   Specializes in **short-form reasoning**, capable of analyzing and explaining claims, decisions, or statements in just a few logical steps — perfect for Q&A bots and assistant systems.
+
+3. **Multilingual Support (EN + ZH)**  
+   Supports fact-checking tasks in both **English** and **Simplified Chinese**, enhancing accessibility for bilingual or regional use cases.
+
+4. **Built on Qwen2.5 0.5B**  
+   Combines the latest architectural improvements from **Qwen2.5** with a small parameter footprint (0.5B), optimized for **speed**, **efficiency**, and **edge-device compatibility**.
+
+5. **Prompt-Friendly Output**  
+   Responds well to well-structured queries, returning clean, interpretable answers — especially for true/false classification, source-based fact validation, and yes/no reasoning.
+
+## **Quickstart with Transformers**
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "prithivMLmods/TESS-QwenRe-Fact-0.5B"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "Is the capital of Australia Sydney? Explain briefly."
+messages = [
+    {"role": "system", "content": "You are a concise and accurate fact-checking assistant."},
+    {"role": "user", "content": prompt}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=256
+)
+generated_ids = [
+    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+]
+
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+```
+
+## **Intended Use**
+
+- **Fact-Checking Assistants**: Quickly verify factual claims in conversation or content.
+- **Digital Truth Detectors**: Misinformation and rumor detection in social feeds or news summaries.
+- **Micro-Reasoning Bots**: Smart agents for short-form logic and rationale generation.
+- **Multilingual Knowledge Tools**: Fact reasoning in **EN/ZH**, ideal for diverse platforms.
+
+## **Limitations**
+
+1. **Limited Depth**  
+   Focused on **short-form reasoning** — may not perform well on multi-step or abstract logic tasks.
+
+2. **Compact Model Scale**  
+   At 0.5B parameters, it prioritizes **efficiency over complexity** — best for straightforward fact-based tasks.
+
+3. **Language & Topic Bias**  
+   Inherits limitations and biases from its base model Qwen2.5 0.5B. Use carefully in sensitive contexts.
+
+4. **Prompt Clarity Required**  
    Structured prompts result in higher factual accuracy and shorter response latency.