transformers safetensors distilbert text-classification multimodal vqa text audio chat text-generation en zh dataset:zeroMN/nlp_corpus_zh dataset:zeroMN/hanlp_date-zh dataset:Johnson8187/Chinese_Multi-Emotion_Dialogue_Dataset dataset:clapAI/MultiLingualSentiment dataset:shareAI/ShareGPT-Chinese-English-90k dataset:wikimedia/wikipedia dataset:google/code_x_glue_tt_text_to_text dataset:silk-road/ChatHaruhi-54K-Role-Playing-Dialogue dataset:yentinglin/TaiwanChat dataset:liswei/rm-static-zhTW dataset:yys/OpenOrca-Chinese dataset:Fumika/Wikinews-multilingual dataset:aqweteddy/Taiwan-Curlture-MCQ dataset:Nexdata/Chinese_Mandarin_Multi-emotional_Synthesis_Corpus dataset:Nexdata/Chinese_Mandarin_Entertainment_anchor_Style_Multi-emotional_Synthesis_Corpus dataset:voices365/102_Hours_High_Quality_Chinese_Audio_Dataset_For_Speech_Synthesis_Female_Samples dataset:voices365/Chinese_Female_001VoiceArtist_40Hours_High_Quality_Voice_Dataset dataset:Nexdata/Mandarin_Spontaneous_Speech_Data dataset:speechbrain/common_language dataset:hello2mao/Chinese_Audio_Resource dataset:a686d380/h-corpus-2023 dataset:synthetic-dataset dataset:zeroMN/AVEdate base_model:zeroMN/SHMT base_model:finetune:zeroMN/SHMT license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
base_model:
- zeroMN/SHMT
- zeroMN/auto
datasets:
- zeroMN/nlp_corpus_zh
- zeroMN/hanlp_date-zh
- Johnson8187/Chinese_Multi-Emotion_Dialogue_Dataset
- clapAI/MultiLingualSentiment
- shareAI/ShareGPT-Chinese-English-90k
- wikimedia/wikipedia
- google/code_x_glue_tt_text_to_text
- silk-road/ChatHaruhi-54K-Role-Playing-Dialogue
- yentinglin/TaiwanChat
- liswei/rm-static-zhTW
- yys/OpenOrca-Chinese
- Fumika/Wikinews-multilingual
- aqweteddy/Taiwan-Curlture-MCQ
- Nexdata/Chinese_Mandarin_Multi-emotional_Synthesis_Corpus
- Nexdata/Chinese_Mandarin_Entertainment_anchor_Style_Multi-emotional_Synthesis_Corpus
- voices365/102_Hours_High_Quality_Chinese_Audio_Dataset_For_Speech_Synthesis_Female_Samples
- voices365/Chinese_Female_001VoiceArtist_40Hours_High_Quality_Voice_Dataset
- Nexdata/Mandarin_Spontaneous_Speech_Data
- speechbrain/common_language
- hello2mao/Chinese_Audio_Resource
- a686d380/h-corpus-2023
- synthetic-dataset
- zeroMN/AVEdate
language:
- en
- zh
library_name: transformers
license: apache-2.0
metrics:
- accuracy
- bleu
- wer
pipeline_tag: text-generation
tags:
- multimodal
- vqa
- text
- audio
- chat
task: extractive-qa
project_name: autotrain-bert-ex-qa1
log: tensorboard
backend: local
data:
  path: lhoestq/squad
  train_split: train
  valid_split: validation
  column_mapping:
    text_column: context
    question_column: question
    answer_column: answers
params:
  max_seq_length: 512
  max_doc_stride: 128
  epochs: 3
  batch_size: 4
  lr: 2e-5
  optimizer: adamw_torch
  scheduler: linear
  gradient_accumulation: 1
  mixed_precision: fp16
hub:
  username: ${HF_USERNAME}
  token: ${HF_TOKEN}
  push_to_hub: true
model-index:
- name: AutoModel
  results:
  - task:
      type: vqa
      name: Visual Question Answering
    dataset:
      name: Synthetic Multimodal Dataset
      type: synthetic-dataset
      split: test
    metrics:
    - type: accuracy
      value: 85
---
# Model Card for AutoModel
-AutoModel 是一个多模态模型，支持图像、文本和语音输入...
---

### **3. 提供可下载文件**
- **模型权重文件**（如 `AutoModel.pth`）。
- **配置文件**（如 `config.json`）。
- **依赖文件**（如 `requirements.txt`）。
- **运行脚本**（如 `run_model.py`）。

用户可以直接下载这些文件并运行模型。
```python
1. import torch
   from model import AutoModel, Config

2. config = Config(config_file="path/to/config.json")
   model = AutoModel(config)
   model.load_state_dict(torch.load("path/to/AutoModel.pth"))
   model.eval()
```

### **4. 自动运行模型的限制**
Hugging Face Hub 本身不能自动运行上传的模型，但通过 `Spaces` 提供的接口可以解决这一问题。`Spaces` 能够运行托管的推理服务，让用户无需本地配置即可测试模型。

---

### **推荐方法**
- **快速测试**：使用 Hugging Face `Spaces` 创建在线演示。
- **高级使用**：在模型卡中提供完整的运行说明，允许用户本地运行模型。

##通过这些方式，您可以让模型仓库既支持在线运行，也便于用户离线部署。
-
```python

```
### Model Description
--
AutoModel is a multimodal deep learning model designed to process and fuse data from three different modalities: images, text, and audio. It supports a variety of downstream tasks, including:
Visual Question Answering (VQA)
Captioning
Information Retrieval
Automatic Speech Recognition (ASR)
Real-time ASR
--
--
The model employs separate encoders for each modality (image, text, audio) and combines their outputs through a fusion layer. It is built with PyTorch and leverages a modular architecture for flexible fine-tuning and deployment.
--
**Developed by:** Independent researcher
**Funded by :** Self-funded
**Shared by :** Independent researcher
**Model type:** Multimodal
**Language(s) (NLP):** English zh
**License:** Apache-2.0
**Finetuned from model :** None

### Model Sources

**Repository:** [GitHub Repository Placeholder](https://github.com/user/repository) *(Add link to code repository)*
**Paper [optional]:** 
**Demo [optional]:** 


## How to use the Model
--
1. Clone the repository:
   ```bash
   git clone https://huggingface.co/zeroMN/AutoModel

```python
2. pip install torch transformers

3. import torch
   from model import AutoModel, Config

4. config = Config(config_file="path/to/config.json")
   model = AutoModel(config)
   model.load_state_dict(torch.load("path/to/AutoModel.pth"))
   model.eval()

5. image = torch.randn(1, 3, 224, 224)
   text = torch.randn(1, 512, 768)
   audio = torch.randn(1, 16000)

   outputs = model(image, text, audio)
   print(outputs)
```

### Direct Use
--
AutoModel is intended for research and application development in multimodal tasks. It can process and integrate data from multiple input types (images, text, audio) for tasks like VQA, captioning, and ASR.
--
### Downstream Use [optional]
--
AutoModel can be fine-tuned on specific datasets to optimize its performance for custom tasks in various domains, such as medical image-text analysis, video-audio subtitling, and real-time speech-to-text systems.
--
### Out-of-Scope Use
--
- Tasks outside its multimodal capabilities (e.g., pure text processing without fusion).
- Non-English language tasks (unless retrained with a multilingual tokenizer and data).
--
## Bias, Risks, and Limitations
--
### Recommendations
--
Users should be aware of potential biases in pre-trained encoders and datasets, such as demographic biases in images, text, or speech. Before deployment, it is recommended to evaluate the model's fairness and robustness in real-world settings.
--
## How to train the Model
--

```python
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from torch.utils.data import Dataset, DataLoader
import datasets

# 加载数据集
dataset_names = [
    "Johnson8187/Chinese_Multi-Emotion_Dialogue_Dataset",
    "clapAI/MultiLingualSentiment",
    "shareAI/ShareGPT-Chinese-English-90k",
    "wikimedia/wikipedia",
    "google/code_x_glue_tt_text_to_text",
    "silk-road/ChatHaruhi-54K-Role-Playing-Dialogue",
    "yentinglin/TaiwanChat",
    "liswei/rm-static-zhTW",
    "yys/OpenOrca-Chinese",
    "Fumika/Wikinews-multilingual",
    "aqweteddy/Taiwan-Curlture-MCQ",
    "Nexdata/Chinese_Mandarin_Multi-emotional_Synthesis_Corpus",
    "Nexdata/Chinese_Mandarin_Entertainment_anchor_Style_Multi-emotional_Synthesis_Corpus",
    "voices365/102_Hours_High_Quality_Chinese_Audio_Dataset_For_Speech_Synthesis_Female_Samples",
    "voices365/Chinese_Female_001VoiceArtist_40Hours_High_Quality_Voice_Dataset",
    "Nexdata/Mandarin_Spontaneous_Speech_Data",
    "speechbrain/common_language",
    "hello2mao/Chinese_Audio_Resource"
]

# 加载模型
model_name = "zeroMN/zeroSG"
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# 加载数据
 datasets_list = []
for dataset_name in dataset_names:
    dataset = datasets.load_dataset(dataset_name)
    datasets_list.append(dataset)

# 创建数据加载器
batch_size = 32
data_loader = DataLoader(datasets_list, batch_size=batch_size, shuffle=True)

# 定义训练参数
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=1e-5)

# 训练模型
for epoch in range(5):
    model.train()
    total_loss = 0
    for batch in data_loader:
        input_ids = batch["input_ids"].to(device)
        attention_mask = batch["attention_mask"].to(device)
        labels = batch["labels"].to(device)

        optimizer.zero_grad()

        outputs = model(input_ids, attention_mask=attention_mask, labels=labels)
        loss = criterion(outputs, labels)

        loss.backward()
        optimizer.step()

        total_loss += loss.item()

    print(f"Epoch {epoch+1}, Loss: {total_loss / len(data_loader)}")
```