Instructions to use eternaut/bert-base-multilingual-uncased-sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eternaut/bert-base-multilingual-uncased-sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="eternaut/bert-base-multilingual-uncased-sentiment")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("eternaut/bert-base-multilingual-uncased-sentiment") model = AutoModelForSequenceClassification.from_pretrained("eternaut/bert-base-multilingual-uncased-sentiment", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 50858b3773c77248c0b2b800740928f3c9c7505e070f235dd91465c7fe5dbc8f
- Size of remote file:
- 3.64 kB
- SHA256:
- 380ea486c31b52e67854cc23388d1960886e743b24c3f02e72534fde337cd34c
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