Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use lordvader31/model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lordvader31/model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="lordvader31/model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("lordvader31/model") model = AutoModelForSequenceClassification.from_pretrained("lordvader31/model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- aedfbc4150dfbe797c25be8b069380fc3b043e1ec07977eda55d91670d977895
- Size of remote file:
- 4.86 kB
- SHA256:
- 5cf964c16de6d5aff8702530c8358f2c6861c04aec3a67fd8be0a05e65d1042e
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