Instructions to use Jillian/bert_results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jillian/bert_results with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jillian/bert_results")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jillian/bert_results") model = AutoModelForSequenceClassification.from_pretrained("Jillian/bert_results", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Jillian/bert_results: direct link, hf CLI and curl.
- Browser
- Download file 5.37 kB
-
https://huggingface.co/Jillian/bert_results/resolve/main/training_args.bin
- Command line
-
hf download hf://Jillian/bert_results/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Jillian/bert_results/resolve/main/training_args.bin
5.37 kB
- Xet hash:
- 5df05f1fb69e226eeb588c81100df90388c20c24a32345e9243ee178a407b8b7
- Size of remote file:
- 5.37 kB
- SHA256:
- eef635cd353710f76cd6ea07b6c83259ac6e23095d022128665f00dd1e870986
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.