Instructions to use Cesar42/bert-base-uncased-emotion_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Cesar42/bert-base-uncased-emotion_v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Cesar42/bert-base-uncased-emotion_v2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Cesar42/bert-base-uncased-emotion_v2") model = AutoModelForSequenceClassification.from_pretrained("Cesar42/bert-base-uncased-emotion_v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Resultados de evaluación: 'test_loss': 0.14830373227596283, 'test_accuracy': 0.9415, 'test_f1': 0.9411005763302622, 'test_runtime': 8.372, 'test_samples_per_second': 238.892, 'test_steps_per_second': 3.822124a88c -
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Resultados de evaluación: 'test_loss': 0.14830373227596283 'test_accuracy': 0.9415 'test_f1': 0.9411005763302622 'test_runtime': 8.372 'test_samples_per_second': 238.892 'test_steps_per_second': 3.8222a2844a -
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