Instructions to use tedad09/DuplicateCrossEncoder-FirstTrain-10Epochs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tedad09/DuplicateCrossEncoder-FirstTrain-10Epochs with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tedad09/DuplicateCrossEncoder-FirstTrain-10Epochs")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tedad09/DuplicateCrossEncoder-FirstTrain-10Epochs") model = AutoModelForSequenceClassification.from_pretrained("tedad09/DuplicateCrossEncoder-FirstTrain-10Epochs", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from tedad09/DuplicateCrossEncoder-FirstTrain-10Epochs: direct link, hf CLI and curl.
- Browser
- Download file 133 MB
-
https://huggingface.co/tedad09/DuplicateCrossEncoder-FirstTrain-10Epochs/resolve/main/model.safetensors
- Command line
-
hf download hf://tedad09/DuplicateCrossEncoder-FirstTrain-10Epochs/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/tedad09/DuplicateCrossEncoder-FirstTrain-10Epochs/resolve/main/model.safetensors
133 MB
- Xet hash:
- f0eca79135e2a92d99c9804ed2a8555af78b665809e97fcf7f62907fdd1e3b5e
- Size of remote file:
- 133 MB
- SHA256:
- 93ef39197799f780e7f7ab11f916cda90069d06c7bdcada1de0aa664f06cec37
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