Instructions to use eawang/distilbert_num_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eawang/distilbert_num_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="eawang/distilbert_num_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("eawang/distilbert_num_model") model = AutoModelForSequenceClassification.from_pretrained("eawang/distilbert_num_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from eawang/distilbert_num_model: direct link, hf CLI and curl.
- Browser
- Download file 3.58 kB
-
https://huggingface.co/eawang/distilbert_num_model/resolve/main/training_args.bin
- Command line
-
hf download hf://eawang/distilbert_num_model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/eawang/distilbert_num_model/resolve/main/training_args.bin
3.58 kB
- Xet hash:
- 1f87ee1cbb2652e48d4d56c4e5de90895835b50fa7e2b33c22e2832dfd214107
- Size of remote file:
- 3.58 kB
- SHA256:
- 214908a476a63212ca1777c7b877cdb142b5c227cc1b6ea94dec303fcd0065f2
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