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--- |
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license: mit |
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language: |
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- en |
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pipeline_tag: text-classification |
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tags: |
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- code |
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- emotion |
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- emotions |
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widget: |
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- text: I'm so angry right now. I can't believe he did that to me. |
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example_title: anger |
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- text: I'm feeling disgusted by the smell of this food. |
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example_title: disgust |
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- text: I'm feeling very afraid of what might happen next. |
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example_title: fear |
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- text: I'm so joyful right now! This is the best day of my life. |
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example_title: joy |
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- text: >- |
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I'm feeling neutral about this situation. I don't really care one way or |
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another. |
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example_title: neutral |
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- text: I'm feeling really sad today after my dog passed away." |
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example_title: sadness |
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- text: I'm so surprised by what just happened! I never saw that coming. |
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example_title: surprise |
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- text: I'm feeling cheeky today. I'm going to play a little prank on my friend. |
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example_title: cheeky |
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- text: I'm feeling confused about what to do next. I need some guidance. |
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example_title: confuse |
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- text: I'm feeling curious about the world around me. There's so much to learn! |
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example_title: curious |
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- text: I'm feeling empathetic towards my friend who is going through a tough time. |
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example_title: empathetic |
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- text: I'm feeling grumpy today. Everything is annoying me! |
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example_title: grumpy |
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- text: I'm feeling guilty about what I did. I wish I could take it back. |
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example_title: guilty |
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- text: I'm feeling very energetic today. I'm ready to take on the world! |
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example_title: energetic |
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- text: I'm feeling impatient waiting for this movie to start. |
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example_title: impatient |
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- text: >- |
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I'm feeling so much love for my family right now. They mean everything to |
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me. |
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example_title: love |
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- text: I'm thinking about my future and what I want to achieve. |
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example_title: think |
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- text: >- |
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I'm feeling serious about this issue. It's important and needs to be |
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addressed. |
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example_title: serious |
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- text: >- |
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I'm feeling suspicious of what he's telling me. I think he's hiding |
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something. |
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example_title: suspicious |
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- text: I'm feeling whiny today. Everything is bothering me! |
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example_title: whiny |
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- text: I love football so much |
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example_title: love 2 |
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- text: I'm reflecting on my experiences to gain insights |
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example_title: think 2 |
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- text: >- |
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I borrowed money from a friend and haven't paid it back yet. Now I feel |
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ashamed. |
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example_title: guilty 2 |
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- text: I'm starting to think that he's up to something. |
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example_title: suspicious 2 |
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- text: We need to approach this matter with a sense of purpose |
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example_title: serious 2 |
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--- |
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# Emotion classification from 20 classes |
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## 20 Emotion labels |
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| id | label | |
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| --- | ---------- | |
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| 0 | anger | |
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| 1 | cheeky | |
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| 2 | confuse | |
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| 3 | curious | |
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| 4 | disgust | |
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| 5 | empathetic | |
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| 6 | energetic | |
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| 7 | fear | |
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| 8 | grumpy | |
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| 9 | guilty | |
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| 10 | impatient | |
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| 11 | joy | |
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| 12 | love | |
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| 13 | neutral | |
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| 14 | sadness | |
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| 15 | serious | |
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| 16 | surprise | |
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| 17 | suspicious | |
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| 18 | think | |
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| 19 | whiny | |
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## How to use |
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Here is how to use this model to get the emotion label of a given text: |
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```python |
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from transformers import AutoModelForSequenceClassification, pipeline |
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model_name = 'jitesh/emotion-english' |
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model = AutoModelForSequenceClassification.from_pretrained(model_name) |
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classifier = pipeline("text-classification", model=model, tokenizer=model_name) |
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text = "I can't wait any longer " |
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prediction = classifier(text) |
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print(prediction[0], text) |
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``` |
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The above code outputs the following line. |
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```bash |
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{'label': 'impatient', 'score': 0.924211859703064} I can't wait any longer |
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``` |
