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:
- 698b249c161cda2a1fa2945a6531347fa96ee2a320e01837092f8189e02caaf6
- Size of remote file:
- 873 MB
- SHA256:
- c70b6ea10ea9a5ae8e3b8dddbcaedf8a0eb40842f72a4b2d73641b68502fd8eb
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