Instructions to use linktimecloud/my_awesome_model_classification_w_adapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use linktimecloud/my_awesome_model_classification_w_adapter with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="linktimecloud/my_awesome_model_classification_w_adapter")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("linktimecloud/my_awesome_model_classification_w_adapter") model = AutoModelForSequenceClassification.from_pretrained("linktimecloud/my_awesome_model_classification_w_adapter", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from linktimecloud/my_awesome_model_classification_w_adapter: direct link, hf CLI and curl.
- Browser
- Download file 3.52 kB
-
https://huggingface.co/linktimecloud/my_awesome_model_classification_w_adapter/resolve/main/training_args.bin
- Command line
-
hf download hf://linktimecloud/my_awesome_model_classification_w_adapter/training_args.bin
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curl -L -o training_args.bin https://huggingface.co/linktimecloud/my_awesome_model_classification_w_adapter/resolve/main/training_args.bin
3.52 kB
- Xet hash:
- e6e70be0e16cee3ff9c709207d9050a20ab64eb057cc86a101f97a44d1aad65b
- Size of remote file:
- 3.52 kB
- SHA256:
- bebbe840fc3fe1775928246c8ba11e6f9f7f5694d9a77c362877bdfb67c8f78a
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