Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use lehendo/bruv with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lehendo/bruv with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="lehendo/bruv")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("lehendo/bruv") model = AutoModelForSequenceClassification.from_pretrained("lehendo/bruv", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b5e80f8830c80b180f7bd3908308f01ddd6fa3bbfd1a7edaa30a2def7a776021
- Size of remote file:
- 5.37 kB
- SHA256:
- d5ddb1a02feb7cc4ea69dc6a6cb0fb58fba720b5fe81c65d16f818f252cabb0c
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