Instructions to use 1024m/SMM4H-Task5-BartL-2A with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use 1024m/SMM4H-Task5-BartL-2A with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="1024m/SMM4H-Task5-BartL-2A")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("1024m/SMM4H-Task5-BartL-2A") model = AutoModelForSequenceClassification.from_pretrained("1024m/SMM4H-Task5-BartL-2A", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from 1024m/SMM4H-Task5-BartL-2A: direct link, hf CLI and curl.
- Browser
- Download file 1.36 MB
-
https://huggingface.co/1024m/SMM4H-Task5-BartL-2A/resolve/main/tokenizer.json
- Command line
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hf download hf://1024m/SMM4H-Task5-BartL-2A/tokenizer.json
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curl -L -o tokenizer.json https://huggingface.co/1024m/SMM4H-Task5-BartL-2A/resolve/main/tokenizer.json
1.36 MB
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