metadata
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 ."))
Metric
Accuracy on the 'validation'
split of SST-2: 0.9278