Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use KanWasTaken/ATSC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KanWasTaken/ATSC with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KanWasTaken/ATSC")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("KanWasTaken/ATSC") model = AutoModelForSequenceClassification.from_pretrained("KanWasTaken/ATSC", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from KanWasTaken/ATSC: direct link, hf CLI and curl.
- Browser
- Download file 5.3 kB
-
https://huggingface.co/KanWasTaken/ATSC/resolve/main/training_args.bin
- Command line
-
hf download hf://KanWasTaken/ATSC/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/KanWasTaken/ATSC/resolve/main/training_args.bin
5.3 kB
- Xet hash:
- 0b18c8409003e385517a85390d9eddcdeccbe76437d155a3fc2c788e54843e59
- Size of remote file:
- 5.3 kB
- SHA256:
- d515acef61544d9106d563b63d29b828c6b1ba2e7141aee4c48ae5dd0f996943
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