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A dataset of 50,000 highly polarized movie reviews for sentiment analysis. Each review is labeled as positive or negative.
| Review Text | Sentiment |
|---|---|
| This movie was absolutely brilliant. The storyline, acting, and cinematography were all top-notch... | Positive |
| One of the worst films I've ever seen. The plot made no sense and the acting was terrible... | Negative |
| A masterpiece! Every scene was perfectly crafted and the emotional depth was incredible... | Positive |
Movie review sentences with fine-grained sentiment labels. Part of the GLUE benchmark for evaluating language understanding.
| Sentence | Label |
|---|---|
| A stirring, funny and finally transporting re-imagining of beauty and the beast. | Positive |
| Unflinchingly bleak and desperate. | Negative |
| Offers that rare combination of entertainment and education. | Positive |
A reading comprehension dataset with questions posed on Wikipedia articles. Answers are spans of text from the passage.
| Question | Context Snippet | Answer |
|---|---|---|
| When did Beyonce start becoming popular? | ...Beyoncé Giselle Knowles-Carter rose to fame in the late 1990s with Destiny's Child... | late 1990s |
| What is the capital of France? | ...Paris is the capital and most populous city of France... | Paris |
Annotated corpus for Named Entity Recognition with entities like persons, organizations, locations, and miscellaneous.
| Token | Entity Label |
|---|---|
| Barack | B-PER |
| Obama | I-PER |
| visited | O |
| B-ORG | |
| in | O |
| California | B-LOC |
News articles categorized into four classes: World, Sports, Business, and Sci/Tech.
| Title | Description | Category |
|---|---|---|
| Wall St. Bears Claw Back | After the worst week for stocks... | Business |
| AI Breakthrough | Scientists develop new machine learning algorithm... | Sci/Tech |
| Champions League Final | The most anticipated match of the season... | Sports |
Image descriptions in English, German, French, and Czech. Used for machine translation and multimodal tasks.
| English | German |
|---|---|
| A man in an orange hat starring at something. | Ein Mann mit einem orangen Hut starrt auf etwas. |
| A Boston Terrier is running on lush green grass. | Ein Boston Terrier läuft über saftig-grünes Gras. |
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