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-39/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-39/optimizer.pt
- Command line
-
hf download hf://EN3S/NLP_assignment_2/run-0/checkpoint-39/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/EN3S/NLP_assignment_2/resolve/main/run-0/checkpoint-39/optimizer.pt
876 MB
- Xet hash:
- 55b062b35f5fc2d0af5b45270748bf4ab28b147d45068326d3b74c7c9f2635c8
- Size of remote file:
- 876 MB
- SHA256:
- 00aaa233a5ab792d448bf134427c353ee79cad1e66b0f69aaec190e7f1ef98af
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