scfengv/TVL_GeneralLayerClassifier

Model Card for Model ID

Model Details

Model Description

• Developed by: scfengv
• Model type: BERT Multi-label Text Classification
• Language: Chinese (Zh)
• Finetuned from model: google-bert/bert-base-chinese

Model Sources

• Repository: scfengv/NLP_DL-Topic-Modeling-for-TVL-livestream-comments

Model Inference Examples

To run the text classification inference, use the following command:

python inference_example_1.py
python inference_example_2.py
python inference_example_3.py

How to Get Started with the Model

import torch
from transformers import BertForSequenceClassification, BertTokenizer

# Load model and tokenizer
model = BertForSequenceClassification.from_pretrained("scfengv/TVL_GeneralLayerClassifier")
tokenizer = BertTokenizer.from_pretrained("scfengv/TVL_GeneralLayerClassifier")

# Prepare your text
text = "Your text here"
inputs = tokenizer(text, return_tensors = "pt", padding = True, truncation = True, max_length = 512)

# Make prediction
with torch.no_grad():
    outputs = model(**inputs)
    predictions = torch.sigmoid(outputs.logits)

# Print predictions
print(predictions)

Training Details

Training Data

Training Procedure

Preprocessing

Training Hyperparameters

The model was trained using the following hyperparameters:

Learning rate: 1e-05
Batch size: 32
Number of epochs: 10
Optimizer: Adam

Evaluation

Results

• Accuracy: 0.9592504607823059
• F1 Score (Micro): 0.9740588950133884
• F1 Score (Macro): 0.9757074189160264

Technical Specifications

Model Architecture and Objective

[More Information Needed]

Compute Infrastructure

Hardware

• NVIDIA Quadro RTX8000

Software

• PyTorch
• HuggingFace