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:
- 9425d2856057104e9da8f219042bf7a0a3aa48c86a809236678c94585f443d50
- Size of remote file:
- 670 MB
- SHA256:
- 17dad027603624c3f0d7316b4d2d04cc70d374b8b7b81f81d315ca4a926015c9
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