Text Classification
Transformers
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use Kimata/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kimata/results with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Kimata/results")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Kimata/results") model = AutoModelForSequenceClassification.from_pretrained("Kimata/results") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8f088b7a5cf6ba69b30d7350871b2878e096ab33d61d0c4d5a3fec949c191fb6
- Size of remote file:
- 5.3 kB
- SHA256:
- 33902044fa38cdde41de8098659106296c2e4ce04b62b087a93d6c5496f89057
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