Instructions to use 1024m/SMM4H-Task5-BartL-1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use 1024m/SMM4H-Task5-BartL-1B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="1024m/SMM4H-Task5-BartL-1B")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("1024m/SMM4H-Task5-BartL-1B") model = AutoModelForSequenceClassification.from_pretrained("1024m/SMM4H-Task5-BartL-1B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from 1024m/SMM4H-Task5-BartL-1B: direct link, hf CLI and curl.
- Browser
- Download file 3.26 GB
-
https://huggingface.co/1024m/SMM4H-Task5-BartL-1B/resolve/main/optimizer.pt
- Command line
-
hf download hf://1024m/SMM4H-Task5-BartL-1B/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/1024m/SMM4H-Task5-BartL-1B/resolve/main/optimizer.pt
3.26 GB
- Xet hash:
- 50a4d0abfdc3f3c6e05479984315ef792e2c3f84d7b8f613e918a60f8f067996
- Size of remote file:
- 3.26 GB
- SHA256:
- 85d4e92201a3f6a0ccc7ac152dcf5666f224a02bc881fa3af5b316246dabd49c
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