Text Classification
Transformers
Safetensors
distilbert
prompt-routing
llm-router
nadirclaw
text-embeddings-inference
Instructions to use nadirclaw/nadirclaw-distilbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nadirclaw/nadirclaw-distilbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nadirclaw/nadirclaw-distilbert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nadirclaw/nadirclaw-distilbert") model = AutoModelForSequenceClassification.from_pretrained("nadirclaw/nadirclaw-distilbert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload README.md with huggingface_hub
Browse files
README.md
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from transformers import DistilBertTokenizer, DistilBertForSequenceClassification
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import torch
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repo = "
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tok = DistilBertTokenizer.from_pretrained(repo)
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model = DistilBertForSequenceClassification.from_pretrained(repo)
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model.eval()
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from transformers import DistilBertTokenizer, DistilBertForSequenceClassification
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import torch
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repo = "nadirclaw/nadirclaw-distilbert"
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tok = DistilBertTokenizer.from_pretrained(repo)
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model = DistilBertForSequenceClassification.from_pretrained(repo)
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model.eval()
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