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--- |
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license: apache-2.0 |
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datasets: |
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- sst2 |
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language: |
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- en |
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metrics: |
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- accuracy |
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library_name: transformers |
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pipeline_tag: text-classification |
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widget: |
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- 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 ." |
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example_title: "negative" |
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- text: "director rob marshall went out gunning to make a great one ." |
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example_title: "positive" |
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--- |
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# bert-base-uncased-finetuned-sst2-v2 |
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BERT (`"bert-base-uncased"`) finetuned on SST-2 (Stanford Sentiment Treebank Binary). |
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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). |
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It was trained using a custom PyTorch loop without Hugging Face Accelerate. |
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Code: https://github.com/sambitmukherjee/hf-nlp-course-exercises/blob/main/chapter3/section4.ipynb |
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Experiment tracking: https://wandb.ai/sadhaklal/bert-base-uncased-finetuned-sst2-v2 |
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## Usage |
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``` |
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from transformers import pipeline |
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classifier = pipeline("text-classification", model="sadhaklal/bert-base-uncased-finetuned-sst2-v2") |
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print(classifier("uneasy mishmash of styles and genres .")) |
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print(classifier("by the end of no such thing the audience , like beatrice , has a watchful affection for the monster .")) |
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``` |
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## Dataset |
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From the dataset page: |
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> 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... |
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> 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. |
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Examples: https://huggingface.co/datasets/sst2/viewer |
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## Metric |
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Accuracy on the `'validation'` split of SST-2: 0.9278 |
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