Text Classification
Transformers
TensorBoard
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use BigTMiami/amazon_domain_to_classification_test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BigTMiami/amazon_domain_to_classification_test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="BigTMiami/amazon_domain_to_classification_test")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("BigTMiami/amazon_domain_to_classification_test") model = AutoModelForSequenceClassification.from_pretrained("BigTMiami/amazon_domain_to_classification_test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from BigTMiami/amazon_domain_to_classification_test: direct link, hf CLI and curl.
- Browser
- Download file 4.92 kB
-
https://huggingface.co/BigTMiami/amazon_domain_to_classification_test/resolve/main/training_args.bin
- Command line
-
hf download hf://BigTMiami/amazon_domain_to_classification_test/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/BigTMiami/amazon_domain_to_classification_test/resolve/main/training_args.bin
4.92 kB
- Xet hash:
- 6734396962a32db3cb4eeeccac5540fc4136a5a63b5191b22e7f3b2ce21efad4
- Size of remote file:
- 4.92 kB
- SHA256:
- 6e749385f6e28f4073046d82a820b734a81c38bcbd4d3737fca523c1f14abf19
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.