metadata

license: apache-2.0
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: bert-uncased-base

bert-uncased-base

This model is a fine-tuned version of bert-base-uncased on an Reddit-dialogue dataset. This model can be used for Text Classification: Given two sentences, see if they are related. It achieves the following results on the evaluation set:

Loss: 0.2297
Accuracy: 0.9267

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

Training results

Framework versions

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

Usage (HuggingFace Transformers)

You can use the model like this:

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)