Instructions to use Abhi964/L3_Cube_Task_0_Trained with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Abhi964/L3_Cube_Task_0_Trained with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Abhi964/L3_Cube_Task_0_Trained")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Abhi964/L3_Cube_Task_0_Trained") model = AutoModelForSequenceClassification.from_pretrained("Abhi964/L3_Cube_Task_0_Trained", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Abhi964/L3_Cube_Task_0_Trained: direct link, hf CLI and curl.
- Browser
- Download file 5.11 kB
-
https://huggingface.co/Abhi964/L3_Cube_Task_0_Trained/resolve/main/training_args.bin
- Command line
-
hf download hf://Abhi964/L3_Cube_Task_0_Trained/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Abhi964/L3_Cube_Task_0_Trained/resolve/main/training_args.bin
5.11 kB
- Xet hash:
- 8129756fd4c6bb442cbc587ed204c4628c977e0034b34aa69435434805d2f7bf
- Size of remote file:
- 5.11 kB
- SHA256:
- 63b1c04d2076b0364203cb5efa2744529bb729dc1682a0362931943b7cd92043
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