Reddit Tc Bert

🚀 bert-uncased-base

This is a fine - tuned version of bert-base-uncased on a Reddit - dialogue dataset, which can be used for text classification to determine the relationship between two sentences.

🚀 Quick Start

This model is a fine - tuned version of bert-base-uncased on an Reddit - dialogue dataset. It can be employed for Text Classification, specifically to check if two given sentences are related. On the evaluation set, it attains the following results:

• Loss: 0.2297
• Accuracy: 0.9267

✨ Features

• Fine - tuned on Reddit - dialogue dataset.
• Capable of text classification to determine sentence relationships.

📦 Installation

No specific installation steps are provided in the original document, so this section is skipped.

💻 Usage Examples

Basic Usage

import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer

# label_list
label_list = ['matched', 'unmatched']

# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained("Fan-s/reddit-tc-bert", use_fast=True)
model = AutoModelForSequenceClassification.from_pretrained("Fan-s/reddit-tc-bert") 

# Set the input
post = "don't make gravy with asbestos."
response = "i'd expect someone with a culinary background to know that. since we're talking about school dinner ladies, they need to learn this pronto."

# Predict whether the two sentences are matched 
def predict(post, response, max_seq_length=128):
    with torch.no_grad():
        args =  (post, response)
        input = tokenizer(*args, padding="max_length", max_length=max_seq_length, truncation=True, return_tensors="pt")
        output = model(**input)
        logits = output.logits
        item = torch.argmax(logits, dim=1)
        predict_label = label_list[item]
        return predict_label, logits
         
predict_label, logits = predict(post, response)
# Matched
print("predict_label:", predict_label)

Advanced Usage

No advanced usage examples are provided in the original document, so this part is not added.

📚 Documentation

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e - 05
• train_batch_size: 320
• eval_batch_size: 80
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon = 1e - 08
• lr_scheduler_type: linear
• num_epochs: 5.0

Framework versions

• Transformers 4.16.0.dev0
• Pytorch 1.10.1+cu102
• Datasets 1.17.0
• Tokenizers 0.11.0

📄 License

This model is licensed under the Apache - 2.0 license.