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:
- a58695feb0f6aeaf00c6979038e6d9616bcef88a07c28e033c52e6bf3e8f1b6f
- Size of remote file:
- 436 MB
- SHA256:
- a0a85b3cce30965c3f41d199a33f5166870d4156203cc55a282e84ec7547b283
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