Text Classification
Transformers
Safetensors
modernbert
feature-extraction
intent
routing
chatbot
agents
moderation
prompt-injection
off-topic
text-embeddings-inference
Instructions to use allsmart/Intent with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use allsmart/Intent with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="allsmart/Intent")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("allsmart/Intent") model = AutoModel.from_pretrained("allsmart/Intent", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Eine Version: ALLSmartIntent
Browse files- beispiel.py +2 -2
beispiel.py
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"""ALLSmartIntent
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pip install torch transformers safetensors huggingface_hub
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python beispiel.py "und morgen?"
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from torch import nn
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from transformers import AutoModel, AutoTokenizer
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MODELL = "allsmart/Intent
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MAX_VERLAUF_ZEICHEN = 600 # per earlier message; longer ones keep start and end (the question is usually at the end)
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GERAET = "cuda" if torch.cuda.is_available() else "cpu"
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"""ALLSmartIntent: what a chat message wants and what a good reply needs, in one pass.
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pip install torch transformers safetensors huggingface_hub
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python beispiel.py "und morgen?"
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from torch import nn
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from transformers import AutoModel, AutoTokenizer
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MODELL = "allsmart/Intent"
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MAX_VERLAUF_ZEICHEN = 600 # per earlier message; longer ones keep start and end (the question is usually at the end)
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GERAET = "cuda" if torch.cuda.is_available() else "cpu"
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