Text Classification
Transformers
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use NarmathaV/my_test_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NarmathaV/my_test_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="NarmathaV/my_test_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("NarmathaV/my_test_model") model = AutoModelForSequenceClassification.from_pretrained("NarmathaV/my_test_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 60d7e033c9675d4606863e22b9a3380444c7c352cbdb3f3e50aaee4a1ca7966e
- Size of remote file:
- 1.42 GB
- SHA256:
- abf321d444403fd5da2f7328f9a2a0ee90dfd617e79ce297eba39f4d106ad121
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