Text Classification
Transformers
TensorBoard
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use Ahmed235/roberta-base-topic_classification_simple2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ahmed235/roberta-base-topic_classification_simple2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Ahmed235/roberta-base-topic_classification_simple2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Ahmed235/roberta-base-topic_classification_simple2") model = AutoModelForSequenceClassification.from_pretrained("Ahmed235/roberta-base-topic_classification_simple2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 60fa4050b3add3582d4cc37eee3b254a5c9d9dcf34c824d28259e793875ffbd5
- Size of remote file:
- 725 MB
- SHA256:
- cfb3b6355e0cfc7944d43a8bc6ce6f2adb100259ea0686785b97aa1b584d1f27
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