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 model.safetensors from Carick/albert-base-v2-wordnet_dataset_three-fine-tuned: direct link, hf CLI and curl.
- Browser
- Download file 46.8 MB
-
https://huggingface.co/Carick/albert-base-v2-wordnet_dataset_three-fine-tuned/resolve/main/model.safetensors
- Command line
-
hf download hf://Carick/albert-base-v2-wordnet_dataset_three-fine-tuned/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/Carick/albert-base-v2-wordnet_dataset_three-fine-tuned/resolve/main/model.safetensors
46.8 MB
- Xet hash:
- 2552bead4d8c5844774787041c8cec0ae228db579af59604398f7823c398fc40
- Size of remote file:
- 46.8 MB
- SHA256:
- 206d9cd1114dd54c55a4d9c7a11f7aa29b6ad542efa74e39a0a94746a6ba7a15
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.