Instructions to use rorschach-40/home-batch_9_2000_-text-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rorschach-40/home-batch_9_2000_-text-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rorschach-40/home-batch_9_2000_-text-classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rorschach-40/home-batch_9_2000_-text-classification") model = AutoModelForSequenceClassification.from_pretrained("rorschach-40/home-batch_9_2000_-text-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from rorschach-40/home-batch_9_2000_-text-classification: direct link, hf CLI and curl.
- Browser
- Download file 4.79 kB
-
https://huggingface.co/rorschach-40/home-batch_9_2000_-text-classification/resolve/main/training_args.bin
- Command line
-
hf download hf://rorschach-40/home-batch_9_2000_-text-classification/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/rorschach-40/home-batch_9_2000_-text-classification/resolve/main/training_args.bin
4.79 kB
- Xet hash:
- 8f13e8abf83bfee828aeb4ac519b0c678ecb14957927b4fb320b17109c2fb483
- Size of remote file:
- 4.79 kB
- SHA256:
- bd6013db1ec750b5c76fb112e0b9ec30da8426c95a47c14af7f27e6a74475bbc
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.