Text Classification
Transformers
Safetensors
xlm-roberta
hinglish
sentiment
text-embeddings-inference
Instructions to use Sumedhzz/Sentiment-Analyzer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sumedhzz/Sentiment-Analyzer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Sumedhzz/Sentiment-Analyzer")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Sumedhzz/Sentiment-Analyzer") model = AutoModelForSequenceClassification.from_pretrained("Sumedhzz/Sentiment-Analyzer") - Notebooks
- Google Colab
- Kaggle
Sumedh satish gajbhiye commited on
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README.md
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license: apache-2.0
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base_model: xlm-roberta-base
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pipeline_tag: text-classification
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library_name: transformers
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tags:
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- hinglish
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- hindi
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- english
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- sentiment
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widget:
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- text: "
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- text: "This was a complete waste of time."
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example_title: "English Negative"
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---
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license: apache-2.0
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base_model: xlm-roberta-base
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pipeline_tag: text-classification
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library_name: transformers
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tags:
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- hinglish
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- sentiment
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widget:
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- text: "this was great"
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---
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