---
tags:
- PretrainModel
- TCM
- transformer
- herberta
- text-embedding
license: apache-2.0
language:
- zh
- en
metrics:
- accuracy
base_model:
- hfl/chinese-roberta-wwm-ext-large
new_version: XiaoEnn/herberta_seq_512_V2
inference: true
library_name: transformers
---

# Herberta: A Pretrained Model for TCM Herbal Medicine and Downstream Tasks

## Introduction

Herberta is a pre-trained model developed by the Angelpro Team, aimed at advancing the representation learning and modeling capabilities in Traditional Chinese Medicine (TCM). Built upon the **chinese-roberta-wwm-ext-large** model, Herberta leverages MLM (Masked Language Modeling) tasks to pre-train on datasets comprising **700 ancient books (538.95M)** and **48 modern Chinese medicine textbooks (54M)**, resulting in a robust model for embedding generation and TCM-specific downstream tasks.

We named the model "Herberta" by combining "Herb" and "Roberta" to signify its purpose in herbal medicine research. Herberta is ideal for applications such as:

- **Encoder for Herbal Formulas**: Generating meaningful embeddings for TCM formulations.
- **Domain-Specific Word Embedding**: Serving the Chinese medicine text domain.
- **Support for TCM Downstream Tasks**: Including classification, labeling, and more.

---

## Pretraining Experiments

### Dataset

| Data Type              | Quantity    | Data Size        |
|------------------------|-------------|------------------|
| **Ancient TCM Books**  | 700 books   | ~538.95M         |
| **Modern TCM Textbooks** | 48 books   | ~54M             |
| **Mixed-Type Dataset** | Combined dataset | ~637.8M          |

### Pretrain result：


| Model                 | eval_accuracy | Loss/epoch_valid | Perplexity_valid |
|-----------------------|---------------|------------------|------------------|
| **herberta_seq_512_v2** | 0.9841        | 0.04367          | 1.083           |
| **herberta_seq_128_v2** | 0.9406        | 0.2877           | 1.333           |
| **herberta_seq_512_V3** |  0.755         |1.100         | 3.010           |

#### Metrics Comparison

![Accuracy](https://cdn-uploads.huggingface.co/production/uploads/6564baaa393bae9c194fc32e/RDgI-0Ro2kMiwV853Wkgx.png)
![Loss](https://cdn-uploads.huggingface.co/production/uploads/6564baaa393bae9c194fc32e/BJ7enbRg13IYAZuxwraPP.png)
![Perplexity](https://cdn-uploads.huggingface.co/production/uploads/6564baaa393bae9c194fc32e/lOohRMIctPJZKM5yEEcQ2.png)


### Pretraining Configuration

#### Ancient Books
- Pretraining Strategy: BERT-style MASK (15% tokens masked)
- Sequence Length: 512
- Batch Size: 32
- Learning Rate: `1e-5` with an epoch-based decay (`epoch * 0.1`)
- Tokenization: Sentence-based tokenization with padding for sequences <512 tokens.

---

## Downstream Task: TCM Pattern Classification

### Task Definition
Using **321 pattern descriptions** extracted from TCM internal medicine textbooks, we evaluated the classification performance on four models:

1. **Herberta_seq_512_v2**: Pretrained on 700 ancient TCM books.
2. **Herberta_seq_512_v3**: Pretrained on 48 modern TCM textbooks.
3. **Herberta_seq_128_v2**: Pretrained on 700 ancient TCM books (128-length sequences).
4. **Roberta**: Baseline model without TCM-specific pretraining.

### Training Configuration
- Max Sequence Length: 512
- Batch Size: 16
- Epochs: 30

### Results

| Model Name              | Eval Accuracy | Eval F1   | Eval Precision | Eval Recall |
|--------------------------|---------------|-----------|----------------|-------------|
| **Herberta_seq_512_v2** | **0.9454**    | **0.9293** | **0.9221**     | **0.9454**  |
| **Herberta_seq_512_v3** | 0.8989        | 0.8704    | 0.8583         | 0.8989      |
| **Herberta_seq_128_v2** | 0.8716        | 0.8443    | 0.8351         | 0.8716      |
| **Roberta**             | 0.8743        | 0.8425    | 0.8311         | 0.8743      |

![image/png](https://cdn-uploads.huggingface.co/production/uploads/6564baaa393bae9c194fc32e/1yG96YdzXuxQlTfjOmXqg.png)


#### Summary
The **Herberta_seq_512_v2** model, pretrained on 700 ancient TCM books, exhibited superior performance across all evaluation metrics. This highlights the significance of domain-specific pretraining on larger and historically richer datasets for TCM applications.

---

## Quickstart

### Use Hugging Face

```python
from transformers import AutoTokenizer, AutoModel

model_name = "XiaoEnn/herberta"

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)

# Input text
text = "中医理论是我国传统文化的瑰宝。"

# Tokenize and prepare input
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding="max_length", max_length=128)

# Get the model's outputs
with torch.no_grad():
    outputs = model(**inputs)

# Get the embedding (sentence-level average pooling)
sentence_embedding = outputs.last_hidden_state.mean(dim=1)

print("Embedding shape:", sentence_embedding.shape)
print("Embedding vector:", sentence_embedding)

```

if you find our work helpful, feel free to give us a cite

@misc{herberta-embedding,
  title = {Herberta: A Pretrained Model for TCM Herbal Medicine and Downstream Tasks as Text Embedding Generation},
  url = {https://github.com/15392778677/herberta},
  author = {Yehan Yang, Xinhan Zheng},
  month = {December},
  year = {2024}
}

@article{herberta-technical-report,
  title={Herberta: A Pretrained Model for TCM Herbal Medicine and Downstream Tasks as Text Embedding Generation},
  author={Yehan Yang, Xinhan Zheng},
  institution={Beijing Angelpro Technology Co., Ltd.},
  year={2024},
  note={Presented at the 2024 Machine Learning Applications Conference (MLAC)}
}