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@@ -54,22 +54,15 @@ inference:
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  temperature: 1
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  ---
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- "Chinese (中文)", "Spanish (Español)", "Hindi (हिन्दी)", "Arabic (العربية)",
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- "Bengali (বাংলা)", "Portuguese (Português)", "Russian (Русский)", "Japanese (日本語)",
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- "German (Deutsch)", "Malay (Bahasa Melayu)", "Telugu (తెలుగు)",
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- "Vietnamese (Tiếng Việt)", "Korean (한국어)", "French (Français)", "Turkish (Türkçe)",
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- "Italian (Italiano)", "Polish (Polski)", "Ukrainian (Українська)",
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- "Tagalog", "Dutch (Nederlands)", "Swiss German (Schweizerdeutsch)"
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  # 🚀 distilbert-based Multilingual Sentiment Classification Model
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- TRY IT HERE: https://huggingface.co/spaces/vdmbrsv/sentiment-analysis-english-five-classes
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  # NEWS!
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  - 2024/12: We are excited to introduce a multilingual sentiment model! Now you can analyze sentiment across multiple languages, enhancing your global reach.
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- ```
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  ## Model Details
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  - `Model Name:` tabularisai/multilingual-sentiment-analysis
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  Below is a Python example on how to use the multilingual sentiment model:
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- ```
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  from transformers import AutoTokenizer, AutoModelForSequenceClassification
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  import torch
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  print(f"Sentiment: {sentiment}\n")
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  ```
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- ## Model Performance
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-
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- Example predictions:
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- $$$
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- 1. "I absolutely loved this movie! The acting was superb and the plot was engaging."
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- Predicted Sentiment: Very Positive (English)
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- 2. "我讨厌这种无休止的争吵。"
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- Predicted Sentiment: Very Negative (Chinese)
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- 3. "El producto funciona como se espera. Nada especial, pero cumple con su función."
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- Predicted Sentiment: Neutral (Spanish)
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- 4. "هذا الكتاب غير حياتي! لقد تعلمت الكثير منه."
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- Predicted Sentiment: Very Positive (Arabic)
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- 5. "Я разочарован покупкой, это не так хорошо, как я надеялся."
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- Predicted Sentiment: Negative (Russian)
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- $$$
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  ## Training Procedure
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  ## Contact
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- For inquiries or private APIs, contact info@tabularis.ai
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  tabularis.ai
 
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  temperature: 1
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  ---
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  # 🚀 distilbert-based Multilingual Sentiment Classification Model
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+ TRY IT HERE: `coming soon`
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  # NEWS!
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  - 2024/12: We are excited to introduce a multilingual sentiment model! Now you can analyze sentiment across multiple languages, enhancing your global reach.
 
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  ## Model Details
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  - `Model Name:` tabularisai/multilingual-sentiment-analysis
 
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  Below is a Python example on how to use the multilingual sentiment model:
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+ ```python
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  from transformers import AutoTokenizer, AutoModelForSequenceClassification
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  import torch
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  print(f"Sentiment: {sentiment}\n")
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  ```
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  ## Training Procedure
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  ## Contact
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+ For inquiries, private APIs, better models, contact info@tabularis.ai
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  tabularis.ai