Text Classification
Transformers
Safetensors
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use tsc-data-science/outputs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tsc-data-science/outputs with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tsc-data-science/outputs")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tsc-data-science/outputs") model = AutoModelForSequenceClassification.from_pretrained("tsc-data-science/outputs", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8dfd1436272d78a3aac5834167a549e8b4de5d22e94b4f7d6b37047bf8996f6d
- Size of remote file:
- 17.1 MB
- SHA256:
- bc5c1151948923156f20bcafd54fd796705d693f8d7b56c83aec49d651f6d602
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