Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use practice-ac/topic_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use practice-ac/topic_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="practice-ac/topic_classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("practice-ac/topic_classification") model = AutoModelForSequenceClassification.from_pretrained("practice-ac/topic_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0fd061a735a3bcab46482f875f04bb366b3c64b7742199643af04eaf9128b4ab
- Size of remote file:
- 268 MB
- SHA256:
- 7acfaf6b8a791d21f2219e213dbd00c6658f9f7a030b36c725c875a19c486023
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