Instructions to use payelb/ctrate_bert_small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use payelb/ctrate_bert_small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="payelb/ctrate_bert_small")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("payelb/ctrate_bert_small") model = AutoModelForSequenceClassification.from_pretrained("payelb/ctrate_bert_small", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from payelb/ctrate_bert_small: direct link, hf CLI and curl.
- Browser
- Download file 5.37 kB
-
https://huggingface.co/payelb/ctrate_bert_small/resolve/main/training_args.bin
- Command line
-
hf download hf://payelb/ctrate_bert_small/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/payelb/ctrate_bert_small/resolve/main/training_args.bin
5.37 kB
- Xet hash:
- 45e9facc26aa3b577cbfc57feadd6d58367bcf8342e827074cdf79484931c08b
- Size of remote file:
- 5.37 kB
- SHA256:
- 2dd160191fa97e67d2420f8268f60e186efc826132d3c9b9d2bc8635146dccdd
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.