Instructions to use Gflorent/BERT_baseline with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Gflorent/BERT_baseline with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Gflorent/BERT_baseline")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Gflorent/BERT_baseline") model = AutoModelForSequenceClassification.from_pretrained("Gflorent/BERT_baseline", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Gflorent/BERT_baseline: direct link, hf CLI and curl.
- Browser
- Download file 4.73 kB
-
https://huggingface.co/Gflorent/BERT_baseline/resolve/main/training_args.bin
- Command line
-
hf download hf://Gflorent/BERT_baseline/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Gflorent/BERT_baseline/resolve/main/training_args.bin
4.73 kB
- Xet hash:
- 6867403683cf35af82e30a94546c115a4a722c332f750eb303c4d557ee10ce2f
- Size of remote file:
- 4.73 kB
- SHA256:
- 8ad3e066977938c8b4d10198e95d7ca3b73230fd24d433572a542f974096b032
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.