Instructions to use shahp7575/electricidad-base-muchocine-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shahp7575/electricidad-base-muchocine-finetuned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="shahp7575/electricidad-base-muchocine-finetuned")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("shahp7575/electricidad-base-muchocine-finetuned") model = AutoModelForSequenceClassification.from_pretrained("shahp7575/electricidad-base-muchocine-finetuned") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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@@ -34,8 +34,8 @@ model = AutoModelWithLMHead.from_pretrained("shahp7575/gpt2-horoscopes")
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from transformers import pipeline
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clf = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer)
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clf('
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>>> [{'label': '5', 'score': 0.
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clf("La historia y el casting fueron geniales.")
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>>> [{'label': '4', 'score': 0.6666394472122192}]
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from transformers import pipeline
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clf = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer)
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clf('Esta película es una joya. Todo fue perfecto: historia, casting, dirección. Me encantó el clímax.')
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>>> [{'label': '5', 'score': 0.9658033847808838}]
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clf("La historia y el casting fueron geniales.")
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>>> [{'label': '4', 'score': 0.6666394472122192}]
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