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:
- df71180ca54ca4877831b2c5efa2621a6eef371d8d4fc674a3057c49a12b11b4
- Size of remote file:
- 1.06 kB
- SHA256:
- d1acf7518eaa83a30405413555139e93a2eb8b9085597afc9a51f69cd1ccf771
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