Text Classification
Transformers
Safetensors
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use albertmartinez/sdg-bert-base-multilingual-cased-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use albertmartinez/sdg-bert-base-multilingual-cased-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="albertmartinez/sdg-bert-base-multilingual-cased-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("albertmartinez/sdg-bert-base-multilingual-cased-classification") model = AutoModelForSequenceClassification.from_pretrained("albertmartinez/sdg-bert-base-multilingual-cased-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b49e7e28c2df6b230d45c348639f0fe5a9bb399f605c1e67a9a4cc47d13936df
- Size of remote file:
- 5.43 kB
- SHA256:
- d1da60ead5c147d70a15126d9f7e50fd4aa57270b2bde46938d5c5336b866985
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