Text Classification
Transformers
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use mpreda/test_dir_model3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mpreda/test_dir_model3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mpreda/test_dir_model3")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mpreda/test_dir_model3") model = AutoModelForSequenceClassification.from_pretrained("mpreda/test_dir_model3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 78ea975b72b7f3b8ce9c4316dd16a4a1d3623d7486e2115ff73d11cf72f52887
- Size of remote file:
- 5.84 kB
- SHA256:
- ef181450ebc5e84370d37c28e2e2e476b5ccf62c8fa62ebb574f29733ebbfa93
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