---
license: apache-2.0
datasets:
- sst2
language:
- en
metrics:
- accuracy
library_name: transformers
pipeline_tag: text-classification
widget:
- text: "this film 's relationship to actual tension is the same as what christmas-tree flocking in a spray can is to actual snow : a poor -- if durable -- imitation ."
  example_title: "negative"
- text: "director rob marshall went out gunning to make a great one ."
  example_title: "positive"
---

# bert-base-uncased-finetuned-sst2-v2

BERT (`"bert-base-uncased"`) finetuned on SST-2 (Stanford Sentiment Treebank Binary).

This model pertains to the "Try it out!" exercise in section 4 of chapter 3 of the Hugging Face "NLP Course" (https://huggingface.co/learn/nlp-course/chapter3/4).

It was trained using a custom PyTorch loop without Hugging Face Accelerate.

Code: https://github.com/sambitmukherjee/hf-nlp-course-exercises/blob/main/chapter3/section4.ipynb

Experiment tracking: https://wandb.ai/sadhaklal/bert-base-uncased-finetuned-sst2-v2

## Usage

```
from transformers import pipeline

classifier = pipeline("text-classification", model="sadhaklal/bert-base-uncased-finetuned-sst2-v2")
print(classifier("uneasy mishmash of styles and genres ."))
print(classifier("by the end of no such thing the audience , like beatrice , has a watchful affection for the monster ."))
```

## Dataset

From the dataset page:

> The Stanford Sentiment Treebank is a corpus with fully labeled parse trees that allows for a complete analysis of the compositional effects of sentiment in language...

> Binary classification experiments on full sentences (negative or somewhat negative vs somewhat positive or positive with neutral sentences discarded) refer to the dataset as SST-2 or SST binary.

Examples: https://huggingface.co/datasets/sst2/viewer

## Metric

Accuracy on the `'validation'` split of SST-2: 0.9278