Distilbert Base Uncased Finetuned Sst 2 English

Table of Contents

Model Overview

Meet the DistilBERT base uncased finetuned SST-2 model, developed by Hugging Face. This model is a fine-tuned version of the popular DistilBERT model, specifically designed for text classification tasks. It’s trained on the Stanford Sentiment Treebank (SST-2) dataset and reaches an impressive accuracy of 91.3 on the dev set.

Tasks and rank

Text Classification 17th

Sentiment Analysis 7th

Learn More About the Ranking System

Capabilities

This model is perfect for topic classification tasks. You can use it to classify text into different categories, such as positive or negative sentiment. It’s also a great starting point for fine-tuning on your own dataset.

What can it do?

This model can be used for topic classification. Imagine you have a bunch of text data and you want to know what it’s about. This model can help you figure that out.

How does it work?

The model uses a technique called fine-tuning, which means it’s been trained on a specific dataset (in this case, the Stanford Sentiment Treebank) to learn how to classify text into different categories.

What makes it special?

This model is a fine-tune checkpoint of the popular DistilBERT model, which means it’s been trained on a specific task (sentiment analysis) to achieve high accuracy. In fact, it reaches an accuracy of 91.3 on the dev set, which is pretty impressive!

What are its strengths?

• High accuracy: As mentioned earlier, this model achieves high accuracy on sentiment analysis tasks.
• Efficient: The model is relatively small compared to other models, making it more efficient to use.

What are its weaknesses?

• Biased predictions: The model may produce biased predictions, especially when it comes to underrepresented populations.
• Limited scope: The model is not intended to be used for factual or true representations of people or events.

Alternatives

All MiniLM L6 V2 2389 Sentence Embeddings

Bert Base Uncased 1831 Uncased BERT model

How to get started

To use this model, you’ll need to have some basic knowledge of Python and the Transformers library. Here’s an example code snippet to get you started:

import torch
from transformers import DistilBertTokenizer, DistilBertForSequenceClassification

tokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
model = DistilBertForSequenceClassification.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")

inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
with torch.no_grad():
    logits = model(**inputs).logits
    predicted_class_id = logits.argmax().item()
    model.config.id2label[predicted_class_id]

Examples

Classify the sentiment of the sentence 'I loved the new movie!' Positive

Is the sentiment of the sentence 'The service was terrible' positive or negative? Negative

What is the sentiment classification of the sentence 'The hotel room was clean and comfortable'? Positive

Important notes

• This model should not be used to create hostile or alienating environments for people.
• It’s not designed to generate factual or true representations of people or events.
• Be aware of potential biases in the model’s predictions, especially when dealing with underrepresented populations.

Training details

The model was trained on the SST-2 dataset using the following hyperparameters:

Hyperparameter	Value
Learning rate	1e-5
Batch size	32
Warmup	600
Max sequence length	128
Number of train epochs	3.0

Performance

This model is a powerful tool for text classification tasks. But how does it perform? Let’s dive into its speed, accuracy, and efficiency.

Speed

How fast is this model? While we don’t have exact numbers on its processing speed, we can look at its architecture. As a fine-tune checkpoint of DistilBERT-base-uncased, it’s designed to be more efficient than its parent model, ==Bert bert-base-uncased==. This means it can process text faster while maintaining high accuracy.

Accuracy

So, how accurate is this model? On the dev set, it reaches an impressive accuracy of 91.3. To put this into perspective, ==Bert bert-base-uncased==, a more complex model, reaches an accuracy of 92.7. This shows that this model is a strong contender in text classification tasks.

Efficiency

But what about its efficiency? This model is designed to be more efficient than its parent model. It uses fewer parameters and is fine-tuned on a specific task, making it more focused and efficient. This means it can handle large-scale datasets with ease.

Limitations

While this model is a powerful tool for text classification, it’s not perfect. Let’s take a closer look at some of its limitations.

Biased Predictions

We’ve noticed that this model can produce biased predictions, especially when it comes to underrepresented populations. For example, when classifying sentences about films, the model gives very different probabilities for the positive label depending on the country mentioned. This is not okay, and we strongly advise users to test for these biases in their own use cases.

Limited Context

This model is trained on a specific dataset (Stanford Sentiment Treebank) and might not perform well on other types of text or tasks. It’s also limited to English language only.

Not Suitable for Factual Content

This model is not designed to generate factual or true representations of people or events. Using it for such purposes is out of its scope.

Intentional Misuse

Please don’t use this model to create hostile or alienating environments for people. That’s not what it’s meant for.

Comparison to Other Models

While this model reaches an accuracy of 91.3 on the dev set, ==Bert bert-base-uncased version== reaches an accuracy of 92.7. This shows that there’s still room for improvement.

What Can You Do?

To get the most out of this model, we recommend:

1. Test for biases: Use datasets like WinoBias, WinoGender, and Stereoset to evaluate the risks of biased predictions.
2. Fine-tune for your task: This model is mostly intended to be fine-tuned on a downstream task. Check the model hub for fine-tuned versions on a task that interests you.
3. Use it responsibly: Be mindful of the potential limitations and biases of this model, and use it in a way that promotes inclusivity and respect.

By being aware of these limitations, you can use this model in a way that’s both effective and responsible.