Pred_genre open-source model - Accurately predict the chronological backdrop of a story based on a 200-word plot summary!

Pred Genre

Developed by Herais

This model predicts the historical era background of a story based on approximately 200-character Chinese plot summaries, trained on film and TV drama datasets.

Text Classification

Transformers

ChineseOpen Source License:Apache-2.0 #Genre Classification #Chinese Text Analysis #Film Era Prediction

Downloads 57

Release Time : 3/2/2022

Model Overview

A fine-tuned classification model based on hfl/chinese-bert-wwm-ext, designed to predict the era background of film and TV works from Chinese plot summaries.

Model Features

Chinese Plot Analysis

Optimized specifically for Chinese plot summaries, accurately identifying era background features in plots.

Multi-class Classification

Supports 13 different era background classifications, including crime, urban, revolutionary, and other types.

BERT Fine-tuning Based

Uses hfl/chinese-bert-wwm-ext as the base model, excelling in Chinese language understanding.

Model Capabilities

Chinese Text Classification

Plot Era Background Recognition

Short Text Analysis

Use Cases

Film and TV Content Analysis

Film and TV Classification

Automatically tags film and TV works with era background labels for easier content management and recommendation.

Accurately identifies various plot types like crime, urban, revolutionary, etc.

Content Review

Assists review systems in verifying if plot content meets platform classification standards.

Academic Research

Film Trend Analysis

Studies the development trends of film and TV works across different era backgrounds.

🚀 Prediction Model for Time Period Based on Synopsis

This model is designed to predict the time period from a synopsis of approximately 200 Chinese characters. It is trained on TV and movie datasets and accepts simplified Chinese as input. The model is fine - tuned from the "hfl/chinese-bert-wwm-ext" checkpoint.

🚀 Quick Start

This model predicts the time period given a synopsis of about 200 Chinese characters. It is trained on TV and Movie datasets and takes simplified Chinese as input. The model is trained starting from the "hfl/chinese-bert-wwm-ext" checkpoint.

💻 Usage Examples

Basic Usage

from transformers import BertTokenizer, BertForSequenceClassification

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
checkpoint = "Herais/pred_genre"
tokenizer = BertTokenizer.from_pretrained(checkpoint, 
                                          problem_type="single_label_classification")
model = BertForSequenceClassification.from_pretrained(checkpoint).to(device)

label2id_genre = {'涉案': 7, '都市': 10, '革命': 12, '农村': 4, '传奇': 0, 
                  '其它': 2, '传记': 1, '青少': 11, '军旅': 3, '武打': 6, 
                  '科幻': 9, '神话': 8, '宫廷': 5}

id2label_genre = {7: '涉案', 10: '都市', 12: '革命', 4: '农村', 0: '传奇', 
                  2: '其它', 1: '传记', 11: '青少', 3: '军旅', 6: '武打', 
                  9: '科幻', 8: '神话', 5: '宫廷'}

synopsis = """加油吧！检察官。鲤州市安平区检察院检察官助理蔡晓与徐美津是两个刚入职场的“菜鸟”。\
他们在老检察官冯昆的指导与鼓励下，凭借着自己的一腔热血与对检察事业的执著追求，克服工作上的种种困难，\
成功办理电竞赌博、虚假诉讼、水产市场涉黑等一系列复杂案件，惩治了犯罪分子，维护了人民群众的合法权益，\
为社会主义法治建设贡献了自己的一份力量。在这个过程中，蔡晓与徐美津不仅得到了业务能力上的提升，\
也领悟了人生的真谛，学会真诚地面对家人与朋友，收获了亲情与友谊，成长为合格的员额检察官，\
继续为检察事业贡献自己的青春。 """

inputs = tokenizer(synopsis, truncation=True, max_length=512, return_tensors='pt')
model.eval()
outputs = model(**input)

label_ids_pred = torch.argmax(outputs.logits, dim=1).to('cpu').numpy()
labels_pred = [id2label_timeperiod[label] for label in labels_pred]

print(labels_pred)
# ['涉案']

📄 License

This project is licensed under the Apache - 2.0 license.

Property	Details
Model Type	Classification
Training Data	Custom
Evaluation Metrics	Rouge

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