Update README.md

README.md

@@ -1,3 +1,55 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+language:
+- zh
+metrics:
+- accuracy
+- precision
+base_model:
+- Qwen/Qwen2.5-0.5B
+---
+The model is an intermediate product of the [EPCD (Easy-Data-Clean-Pipeline)](https://github.com/ytzfhqs/EDCP) project, primarily used to distinguish between the main content and non-content (such as book introductions, publisher information, writing standards, revision notes) of **medical textbooks** after performing OCR using [MinerU](https://github.com/opendatalab/MinerU). The base model uses [Qwen2.5-0.5B](https://huggingface.co/Qwen/Qwen2.5-0.5B), avoiding the length limitation of the Bert Tokenizer while providing higher accuracy.
+
+# Data Composition
+
+- The data consists of scanned PDF copies of textbooks, converted into `Markdown` files through `OCR` using [MinerU](https://github.com/opendatalab/MinerU). After a simple regex-based cleaning, the samples were split using `\n`, and a `Bloom` probabilistic filter was used for precise deduplication, resulting in 50,000 samples. Due to certain legal considerations, we may not plan to make the dataset publicly available.
+- Due to the nature of textbooks, most samples are main content. According to statistics, in our dataset, 79.89% (40,000) are main content samples, while 20.13% (10,000) are non-content samples. Considering data imbalance, we evaluate the model's performance on both Precision and Accuracy metrics on the test set.
+- To ensure consistency in the data distribution between the test set and the training set, we used stratified sampling to select 10% of the data as the test set.
+
+# Training Techniques
+
+- To maximize model accuracy, we used Bayesian optimization (TPE algorithm) and Hyperband pruning (HyperbandPruner) to accelerate hyperparameter tuning.
+
+# Model Performance
+
+| Dataset | Accuracy | Precision |
+|---------|----------|-----------|
+| Train | 0.9894 | 0.9673 |
+| Test | 0.9788 | 0.9548 |
+
+# Usage
+
+```python
+import torch
+from transformers import AutoModelForSequenceClassification
+from transformers import AutoTokenizer
+
+ID2LABEL = {0: "正文", 1: "非正文"}
+
+model_name = 'ytzfhqs/Qwen2.5-med-book-main-classification'
+model = AutoModelForSequenceClassification.from_pretrained(
+ model_name,
+ torch_dtype="auto",
+ device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+text = '下列为修订说明'
+encoding = tokenizer(text, return_tensors='pt')
+encoding = {k: v.to(model.device) for k, v in encoding.items()}
+outputs = model(**encoding)
+logits = outputs.logits
+id = torch.argmax(logits, dim=-1).item()
+response = ID2LABEL[id]
+print(response)
+```