Instructions to use Carick/albert-base-v2-wordnet_dataset_two-fine-tuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Carick/albert-base-v2-wordnet_dataset_two-fine-tuned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Carick/albert-base-v2-wordnet_dataset_two-fine-tuned")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Carick/albert-base-v2-wordnet_dataset_two-fine-tuned") model = AutoModelForSequenceClassification.from_pretrained("Carick/albert-base-v2-wordnet_dataset_two-fine-tuned", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Carick/albert-base-v2-wordnet_dataset_two-fine-tuned: direct link, hf CLI and curl.
- Browser
- Download file 5.37 kB
-
https://huggingface.co/Carick/albert-base-v2-wordnet_dataset_two-fine-tuned/resolve/main/training_args.bin
- Command line
-
hf download hf://Carick/albert-base-v2-wordnet_dataset_two-fine-tuned/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Carick/albert-base-v2-wordnet_dataset_two-fine-tuned/resolve/main/training_args.bin
5.37 kB
- Xet hash:
- d452884a09215270516783c602317733359979ebcd5983a1fc9f83aeb99d882b
- Size of remote file:
- 5.37 kB
- SHA256:
- cf20b9aad2856df37472c90c6a0a7440d85e464a71ec9e29513a2efdb14d7a4d
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