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
Adding `safetensors` variant of this model
#1
by SFconvertbot - opened
- model.safetensors +3 -0
model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:85ce2baa0714fc67e626932ac163de1858752fc985340dcde0923eb6bfe5ac9a
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size 669462628
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