| 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 ." | |
| - text: "director rob marshall went out gunning to make a great one ." | |
| # bert-base-uncased-finetuned-sst2-v2 | |
| "bert-base-uncased" finetuned on SST-2. | |
| 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 | |