Instructions to use EN3S/NLP_assignment_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EN3S/NLP_assignment_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="EN3S/NLP_assignment_2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("EN3S/NLP_assignment_2") model = AutoModelForSequenceClassification.from_pretrained("EN3S/NLP_assignment_2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download run-0/checkpoint-156/optimizer.pt from EN3S/NLP_assignment_2: direct link, hf CLI and curl.
- Browser
- Download file 876 MB
-
https://huggingface.co/EN3S/NLP_assignment_2/resolve/main/run-0/checkpoint-156/optimizer.pt
- Command line
-
hf download hf://EN3S/NLP_assignment_2/run-0/checkpoint-156/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/EN3S/NLP_assignment_2/resolve/main/run-0/checkpoint-156/optimizer.pt
876 MB
- Xet hash:
- 9da7568ad93930a3e8bcee64f33a661ebc6899f61c508746a148423915209788
- Size of remote file:
- 876 MB
- SHA256:
- 0dbe2ee4a6c940354ab98038be65bd5e28a470d52963a8ac58d946eea229ef37
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