Instructions to use Carick/albert-base-v2-wordnet_dataset_three-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_three-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_three-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_three-fine-tuned") model = AutoModelForSequenceClassification.from_pretrained("Carick/albert-base-v2-wordnet_dataset_three-fine-tuned", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Carick/albert-base-v2-wordnet_dataset_three-fine-tuned: direct link, hf CLI and curl.
- Browser
- Download file 5.37 kB
-
https://huggingface.co/Carick/albert-base-v2-wordnet_dataset_three-fine-tuned/resolve/main/training_args.bin
- Command line
-
hf download hf://Carick/albert-base-v2-wordnet_dataset_three-fine-tuned/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Carick/albert-base-v2-wordnet_dataset_three-fine-tuned/resolve/main/training_args.bin
5.37 kB
- Xet hash:
- fe2136d99b30593c33699bee9dd634e6d8a03afd097387819a8b9c774729e626
- Size of remote file:
- 5.37 kB
- SHA256:
- fc78170a7a7a7a4b08a9db971f9364d84e15f6ba1174856c035508ffaaa996f2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.