Text Classification
Transformers
TensorBoard
Safetensors
roberta
Trained with AutoTrain
text-embeddings-inference
Instructions to use roncc13/trainCMDBERT-sample with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use roncc13/trainCMDBERT-sample with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="roncc13/trainCMDBERT-sample")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("roncc13/trainCMDBERT-sample") model = AutoModelForSequenceClassification.from_pretrained("roncc13/trainCMDBERT-sample", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 91914cbc43ca866bb999fcf3260b194d49055e0f0fc1c3ed759a4993165f9773
- Size of remote file:
- 5.37 kB
- SHA256:
- 7faf0fa4cb37d6877095efc7e3e4603e8e7d983ba8ebd45c0b43a0f8b30a8903
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