--- 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