Update README.md

README.md

@@ -1,63 +1,75 @@
-# Qwen2.5-1.5B-Sign
-
-## Introduction
-
-Qwen2.5-Sign is a text-to-chinese-sign model base on Qwen2.5
-
-## Finetune Details
-
-- Finetune dataset: [alpaca-zh-text2sign](https://huggingface.co/datasets/thundax/alpaca-zh-text2sign)
-- Finetune parameter
-
-| Parameter                   | Value  |
-|-----------------------------|--------|
-| learning_rate               | 5e-05  |
-| train_batch_size            | 4      |
-| eval_batch_size             | 4      |
-| gradient_accumulation_steps | 8      |
-| total_train_batch_size      | 32     |
-| lr_scheduler_type           | cosine |
-| lr_scheduler_warmup_steps   | 100    |
-| num_epochs                  | 4      |
-
-## Quickstart
-
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-device = "cuda"  # the device to load the model onto
-
-model = AutoModelForCausalLM.from_pretrained(
-    "thundax/Qwen2.5-1.5B-Sign",
-    torch_dtype="auto",
-    device_map="auto"
-)
-tokenizer = AutoTokenizer.from_pretrained("thundax/Qwen2.5-1.5B-Sign")
-
-text = "站一个制高点看上海，上海的弄堂是壮观的景象。它是这城市背景一样的东西。"
-input_text = f'Translate sentence into labels\n{text}\n'
-model_inputs = tokenizer([input_text], return_tensors="pt").to(device)
-
-generated_ids = model.generate(
-    model_inputs.input_ids,
-    max_new_tokens=512
-)
-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]
-```
-
-## Citation
-
-If you find our work helpful, feel free to give us a cite.
-
-```
-@software{qwen2-sign,
-  author = {thundax},
-  title = {qwen2-sign: A Tool for Text to Sign},
-  year = {2025},
-  url = {https://github.com/thundax-lyp},
-}
+---
+license: mit
+datasets:
+- thundax/alpaca-zh-text2sign
+language:
+- zh
+metrics:
+- accuracy
+base_model:
+- Qwen/Qwen2.5-1.5B
+pipeline_tag: text-generation
+---
+# Qwen2.5-1.5B-Sign
+
+## Introduction
+
+Qwen2.5-Sign is a text-to-chinese-sign model base on Qwen2.5
+
+## Finetune Details
+
+- Finetune dataset: [alpaca-zh-text2sign](https://huggingface.co/datasets/thundax/alpaca-zh-text2sign)
+- Finetune parameter
+
+| Parameter                   | Value  |
+|-----------------------------|--------|
+| learning_rate               | 5e-05  |
+| train_batch_size            | 4      |
+| eval_batch_size             | 4      |
+| gradient_accumulation_steps | 8      |
+| total_train_batch_size      | 32     |
+| lr_scheduler_type           | cosine |
+| lr_scheduler_warmup_steps   | 100    |
+| num_epochs                  | 4      |
+
+## Quickstart
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+device = "cuda"  # the device to load the model onto
+
+model = AutoModelForCausalLM.from_pretrained(
+    "thundax/Qwen2.5-1.5B-Sign",
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained("thundax/Qwen2.5-1.5B-Sign")
+
+text = "站一个制高点看上海，上海的弄堂是壮观的景象。它是这城市背景一样的东西。"
+input_text = f'Translate sentence into labels\n{text}\n'
+model_inputs = tokenizer([input_text], return_tensors="pt").to(device)
+
+generated_ids = model.generate(
+    model_inputs.input_ids,
+    max_new_tokens=512
+)
+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]
+```
+
+## Citation
+
+If you find our work helpful, feel free to give us a cite.
+
+```
+@software{qwen2-sign,
+  author = {thundax},
+  title = {qwen2-sign: A Tool for Text to Sign},
+  year = {2025},
+  url = {https://github.com/thundax-lyp},
+}
 ```