Instructions to use eclec/patentClassfication3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eclec/patentClassfication3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="eclec/patentClassfication3")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("eclec/patentClassfication3") model = AutoModelForSequenceClassification.from_pretrained("eclec/patentClassfication3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from eclec/patentClassfication3: direct link, hf CLI and curl.
- Browser
- Download file 3.96 kB
-
https://huggingface.co/eclec/patentClassfication3/resolve/refs%2Fpr%2F1/training_args.bin
- Command line
-
hf download hf://eclec/patentClassfication3@refs/pr/1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/eclec/patentClassfication3/resolve/refs%2Fpr%2F1/training_args.bin
3.96 kB
- Xet hash:
- 0b857c484bd32eb2e4817fe52f79fbf0776a32b794ef7402a6e48dba49fb5e5f
- Size of remote file:
- 3.96 kB
- SHA256:
- e4631d240a15ad5fa0bcd7e62886e0d38613c687d5d73f3e35caa90d0c35f039
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.