Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use Noola/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Noola/results with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Noola/results")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Noola/results") model = AutoModelForSequenceClassification.from_pretrained("Noola/results", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 307 Bytes
7c7c54d | 1 2 3 4 5 6 7 8 9 10 | class CustomProvider:
def __init__(self, model, tokenizer):
self.model = model
self.tokenizer = tokenizer
def __call__(self, text):
inputs = self.tokenizer(text, return_tensors="pt")
outputs = self.model(**inputs)
return outputs.logits.argmax(dim=-1).item()
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