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:
- c975e7b745b12c70fdaa682a6a6dd348b70900dda34863aba829cffef8c959b8
- Size of remote file:
- 5.71 kB
- SHA256:
- 0cdf1d7da2dd335bac7922a0e0dcea8a759f90e4ba281b2a6bc3c1f42c6681ec
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