Text Classification
Transformers
Safetensors
English
distilbert
sentiment-analysis
text-embeddings-inference
Instructions to use Drocho/distilbert-sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Drocho/distilbert-sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Drocho/distilbert-sentiment")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Drocho/distilbert-sentiment") model = AutoModelForSequenceClassification.from_pretrained("Drocho/distilbert-sentiment") - Notebooks
- Google Colab
- Kaggle
Create README.md
Browse files
README.md
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---
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language: en
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tags:
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- text-classification
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- sentiment-analysis
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pipeline_tag: text-classification
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---
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# DistilBERT Sentiment Analysis
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Modelo fine-tuneado sobre `distilbert-base-uncased` para clasificación binaria de sentimientos (positivo/negativo) en textos multidominio.
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