Bert Base Uncased Finetuned Surveyclassification

🚀 bert-base-uncased-finetuned-surveyclassification

This model is a fine - tuned version of bert - base - uncased on a custom survey dataset, achieving high accuracy and F1 scores in evaluation.

🚀 Quick Start

You can use this model with Transformers pipeline for Text Classification.

from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
tokenizer = AutoTokenizer.from_pretrained("Jorgeutd/bert-base-uncased-finetuned-surveyclassification")
model = AutoModelForSequenceClassification.from_pretrained("Jorgeutd/bert-base-uncased-finetuned-surveyclassification")
text_classifier = pipeline("text-classification", model=model,tokenizer=tokenizer, device=0)
example = "The agent on the phone was very helpful and nice to me."
results = text_classifier(example)
print(results)

✨ Features

This model is a fine - tuned version of [bert - base - uncased](https://huggingface.co/bert - base - uncased) on a custom survey dataset. It achieves the following results on the evaluation set:

• Loss: 0.2818
• Accuracy: 0.9097
• F1: 0.9097

📚 Documentation

Model description

More information needed

Limitations and bias

This model is limited by its training dataset of survey results for a particular customer service domain. This may not generalize well for all use cases in different domains.

📦 Installation

No specific installation steps provided in the original document.

💻 Usage Examples

Basic Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
tokenizer = AutoTokenizer.from_pretrained("Jorgeutd/bert-base-uncased-finetuned-surveyclassification")
model = AutoModelForSequenceClassification.from_pretrained("Jorgeutd/bert-base-uncased-finetuned-surveyclassification")
text_classifier = pipeline("text-classification", model=model,tokenizer=tokenizer, device=0)
example = "The agent on the phone was very helpful and nice to me."
results = text_classifier(example)
print(results)

🔧 Technical Details

Training and evaluation data

Custom survey dataset.

Training procedure

SageMaker notebook instance.

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 3e - 05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e - 08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 100
• num_epochs: 10
• mixed_precision_training: Native AMP

Training results

Framework versions

• Transformers 4.16.2
• Pytorch 1.8.1+cu111
• Datasets 1.18.3
• Tokenizers 0.11.0

📄 License

This model is licensed under the Apache 2.0 license.