embed and flat are special keywords. embed spreads the points based on similarities in the text embedding. flat, as the name suggests, prevents distribution along an axis. These only take effect when both start and end values of an axis are set to the same special keyword.Dataset | Type | Content | X | Y | Z |
|---|---|---|---|---|---|
| Sentiment Classification (Zero-shot) | Prompt | Classify the text into neutral, negative or positive. Text: I love spending time with my family. Sentiment: | 1.00 | 0.42 | 0.07 |
| Sentiment Classification (Zero-shot) | Output | Positive | 1.00 | -0.98 | 0.91 |
| Sentiment Classification (Zero-shot) | Prompt | Classify the text into neutral, negative or positive. Text: The food at the restaurant was terrible. Sentiment: | -1.00 | 0.33 | -0.00 |
| Sentiment Classification (Zero-shot) | Output | Negative | -1.00 | -0.57 | -0.41 |
| Sentiment Classification (Zero-shot) | Prompt | Classify the text into neutral, negative or positive. Text: The movie was pretty average. Sentiment: | 0.00 | 0.35 | 0.03 |
| Sentiment Classification (Zero-shot) | Output | Neutral | 0.00 | 0.99 | -0.97 |
| Sentiment Classification (Zero-shot) | Prompt | Classify the text into neutral, negative or positive. Text: I had a fantastic day at the beach. Sentiment: | 1.00 | 0.33 | 0.02 |
| Sentiment Classification (Zero-shot) | Output | Positive | 1.00 | -0.97 | 0.91 |
| Sentiment Classification (Zero-shot) | Prompt | Classify the text into neutral, negative or positive. Text: The service was really bad. Sentiment: | -1.00 | 0.38 | 0.08 |
| Sentiment Classification (Zero-shot) | Output | Negative | -1.00 | -0.66 | -0.43 |
| Sentiment Classification (Zero-shot) | Prompt | Classify the text into neutral, negative or positive. Text: The weather today is fine. Sentiment: | 0.00 | 0.43 | -0.07 |
| Sentiment Classification (Zero-shot) | Output | Positive | 1.00 | -0.98 | 0.91 |
| Sentiment Classification (Zero-shot) | Prompt | Classify the text into neutral, negative or positive. Text: I am extremely happy with my new job. Sentiment: | 1.00 | 0.44 | 0.01 |
| Sentiment Classification (Zero-shot) | Output | Positive | 1.00 | -0.95 | 0.91 |
| Sentiment Classification (Zero-shot) | Prompt | Classify the text into neutral, negative or positive. Text: The meeting was a waste of time. Sentiment: | -1.00 | 0.44 | 0.02 |
| Sentiment Classification (Zero-shot) | Output | Negative | -1.00 | -0.59 | -0.39 |
| Sentiment Classification (Zero-shot) | Prompt | Classify the text into neutral, negative or positive. Text: The presentation was okay. Sentiment: | 0.00 | 0.44 | 0.04 |
| Sentiment Classification (Zero-shot) | Output | Neutral | 0.00 | 0.95 | -0.95 |
| Sentiment Classification (Zero-shot) | Prompt | Classify the text into neutral, negative or positive. Text: I enjoyed the concert last night. Sentiment: | 1.00 | 0.45 | 0.04 |
| Sentiment Classification (Zero-shot) | Output | Positive | 1.00 | -0.92 | 0.94 |
| Sentiment Classification (Zero-shot) | Prompt | Classify the text into neutral, negative or positive. Text: I hate waking up early. Sentiment: | -1.00 | 0.42 | -0.00 |
| Sentiment Classification (Zero-shot) | Output | Negative | -1.00 | -0.58 | -0.40 |
| Sentiment Classification (Zero-shot) | Prompt | Classify the text into neutral, negative or positive. Text: The project is going well. Sentiment: | 1.00 | 0.39 | 0.07 |
| Sentiment Classification (Zero-shot) | Output | Positive | 1.00 | -0.94 | 0.92 |
| Sentiment Classification (Zero-shot) | Prompt | Classify the text into neutral, negative or positive. Text: My vacation was wonderful. Sentiment: | 1.00 | 0.39 | -0.02 |
| Sentiment Classification (Zero-shot) | Output | Positive | 1.00 | -0.92 | 0.95 |
| Sentiment Classification (Zero-shot) | Prompt | Classify the text into neutral, negative or positive. Text: I am disappointed with the product. Sentiment: | -1.00 | 0.39 | -0.06 |
| Sentiment Classification (Zero-shot) | Output | Negative | -1.00 | -0.56 | -0.37 |
| Sentiment Classification (Zero-shot) | Prompt | Classify the text into neutral, negative or positive. Text: The event was neither good nor bad. Sentiment: | 0.00 | 0.37 | 0.10 |
| Sentiment Classification (Zero-shot) | Output | Neutral | 0.00 | 0.96 | -0.97 |
| Sentiment Classification (Zero-shot) | Prompt | Classify the text into neutral, negative or positive. Text: The trip was amazing. Sentiment: | 1.00 | 0.44 | 0.09 |
| Sentiment Classification (Zero-shot) | Output | Positive | 1.00 | -0.95 | 0.93 |
| Sentiment Classification (Zero-shot) | Prompt | Classify the text into neutral, negative or positive. Text: I am not satisfied with the service. Sentiment: | -1.00 | 0.38 | 0.11 |
| Sentiment Classification (Zero-shot) | Output | Negative | -1.00 | -0.67 | -0.42 |
| Sentiment Classification (Zero-shot) | Prompt | Classify the text into neutral, negative or positive. Text: The movie was fine. Sentiment: | 0.00 | 0.35 | 0.13 |
| Sentiment Classification (Zero-shot) | Output | Neutral | 0.00 | 0.95 | -0.96 |
| Sentiment Classification (Zero-shot) | Prompt | Classify the text into neutral, negative or positive. Text: I am thrilled with the results. Sentiment: | 1.00 | 0.48 | 0.05 |
| Sentiment Classification (Zero-shot) | Output | Positive | 1.00 | -0.95 | 1.00 |
| Sentiment Classification (Zero-shot) | Prompt | Classify the text into neutral, negative or positive. Text: The food was disappointing. Sentiment: | -1.00 | 0.44 | 0.10 |
| Sentiment Classification (Zero-shot) | Output | Negative | -1.00 | -0.65 | -0.43 |
| Sentiment Classification (Zero-shot) | Prompt | Classify the text into neutral, negative or positive. Text: The weather is neither here nor there. Sentiment: | 0.00 | 0.40 | 0.03 |
| Sentiment Classification (Zero-shot) | Output | Neutral | 0.00 | 0.96 | -0.99 |
| Sentiment Classification (Zero-shot) | Prompt | Classify the text into neutral, negative or positive. Text: I am overjoyed with my promotion. Sentiment: | 1.00 | 0.48 | 0.01 |
| Sentiment Classification (Zero-shot) | Output | Positive | 1.00 | -0.94 | 1.00 |
| Sentiment Classification (Zero-shot) | Prompt | Classify the text into neutral, negative or positive. Text: The service at the cafe was poor. Sentiment: | -1.00 | 0.36 | 0.08 |
| Sentiment Classification (Zero-shot) | Output | Negative | -1.00 | -0.65 | -0.43 |
| Sentiment Classification (Zero-shot) | Prompt | Classify the text into neutral, negative or positive. Text: The book was okay. Sentiment: | 0.00 | 0.33 | -0.03 |
| Sentiment Classification (Zero-shot) | Output | Neutral | 0.00 | 0.95 | -0.95 |
| Sentiment Classification (Zero-shot) | Prompt | Classify the text into neutral, negative or positive. Text: I had a great time at the party. Sentiment: | 1.00 | 0.33 | 0.09 |
| Sentiment Classification (Zero-shot) | Output | Positive | 1.00 | -0.94 | 0.99 |
| Sentiment Classification (Zero-shot) | Prompt | Classify the text into neutral, negative or positive. Text: I am unhappy with my purchase. Sentiment: | -1.00 | 0.46 | 0.02 |
| Sentiment Classification (Zero-shot) | Output | Negative | -1.00 | -0.55 | -0.38 |
| Sentiment Classification (Zero-shot) | Prompt | Classify the text into neutral, negative or positive. Text: The movie was just average. Sentiment: | 0.00 | 0.39 | 0.02 |
| Sentiment Classification (Zero-shot) | Output | Neutral | 0.00 | 1.00 | -0.99 |
| Sentiment Classification (Zero-shot) | Prompt | Classify the text into neutral, negative or positive. Text: The experience was delightful. Sentiment: | 1.00 | 0.41 | -0.04 |
| Sentiment Classification (Zero-shot) | Output | Positive | 1.00 | -0.93 | 0.97 |
| Sentiment Classification (Zero-shot) | Prompt | Classify the text into neutral, negative or positive. Text: I regret buying this item. Sentiment: | -1.00 | 0.40 | 0.10 |
| Sentiment Classification (Zero-shot) | Output | Negative | -1.00 | -0.61 | -0.41 |
| Sentiment Classification (Zero-shot) | Prompt | Classify the text into neutral, negative or positive. Text: The performance was so-so. Sentiment: | 0.00 | 0.46 | -0.05 |
| Sentiment Classification (Zero-shot) | Output | Neutral | 0.00 | 0.99 | -1.00 |
| Sentiment Classification (Zero-shot) | Prompt | Classify the text into neutral, negative or positive. Text: I am excited about the new opportunities. Sentiment: | 1.00 | 0.36 | 0.04 |
| Sentiment Classification (Zero-shot) | Output | Positive | 1.00 | -0.91 | 0.94 |
| Sentiment Classification (Zero-shot) | Prompt | Classify the text into neutral, negative or positive. Text: The customer service was awful. Sentiment: | -1.00 | 0.47 | 0.07 |
| Sentiment Classification (Zero-shot) | Output | Negative | -1.00 | -0.61 | -0.42 |
| Sentiment Classification (Zero-shot) | Prompt | Classify the text into neutral, negative or positive. Text: The weather is quite nice today. Sentiment: | 0.00 | 0.43 | -0.04 |
| Sentiment Classification (Zero-shot) | Output | Positive | 0.00 | -1.00 | 0.89 |
| Sentiment Classification (Zero-shot) | Prompt | Classify the text into neutral, negative or positive. Text: I love my new car. Sentiment: | 1.00 | 0.35 | -0.01 |
| Sentiment Classification (Zero-shot) | Output | Positive | 1.00 | -0.93 | 1.00 |
| Sentiment Classification (Zero-shot) | Prompt | Classify the text into neutral, negative or positive. Text: The concert was terrible. Sentiment: | -1.00 | 0.32 | 0.06 |
| Sentiment Classification (Zero-shot) | Output | Negative | -1.00 | -0.58 | -0.41 |
| Sentiment Classification (Zero-shot) | Prompt | Classify the text into neutral, negative or positive. Text: The show was okay. Sentiment: | 0.00 | 0.45 | -0.06 |
| Sentiment Classification (Zero-shot) | Output | Neutral | 0.00 | 0.97 | -1.00 |
| Sentiment Classification (Zero-shot) | Prompt | Classify the text into neutral, negative or positive. Text: I am very pleased with the outcome. Sentiment: | 1.00 | 0.34 | 0.05 |
| Sentiment Classification (Zero-shot) | Output | Positive | 1.00 | -0.98 | 0.90 |
| Sentiment Classification (Zero-shot) | Prompt | Classify the text into neutral, negative or positive. Text: I am upset about the delay. Sentiment: | -1.00 | 0.31 | -0.02 |
| Sentiment Classification (Zero-shot) | Output | Negative | -1.00 | -0.56 | -0.37 |
| Sentiment Classification (Zero-shot) | Prompt | Classify the text into neutral, negative or positive. Text: The presentation was mediocre. Sentiment: | -1.00 | 0.34 | -0.04 |
| Sentiment Classification (Zero-shot) | Output | Negative | -1.00 | -0.56 | -0.39 |
| Sentiment Classification (Zero-shot) | Prompt | Classify the text into neutral, negative or positive. Text: I think the vacation is okay. Sentiment: | -1.00 | 0.38 | -0.01 |
| Sentiment Classification (Zero-shot) | Output | Neutral | -1.00 | 0.95 | -0.96 |