Instructions to use chrommium/two-step-finetuning-sbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chrommium/two-step-finetuning-sbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="chrommium/two-step-finetuning-sbert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("chrommium/two-step-finetuning-sbert") model = AutoModelForSequenceClassification.from_pretrained("chrommium/two-step-finetuning-sbert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from chrommium/two-step-finetuning-sbert: direct link, hf CLI and curl.
- Browser
- Download file 1.71 GB
-
https://huggingface.co/chrommium/two-step-finetuning-sbert/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://chrommium/two-step-finetuning-sbert/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/chrommium/two-step-finetuning-sbert/resolve/main/pytorch_model.bin
1.71 GB
- Xet hash:
- 4dabe0b68dee8b50e67b677e9fdeeff60ee8f190fd42a282e1b2034877badca2
- Size of remote file:
- 1.71 GB
- SHA256:
- a2ecc243fbfede7c74c46b2aa8298221def933e16b38e7699f98d9aa3ff74499
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