Instructions to use parameterlab/apricot_binary_coqa_deberta-v3-base_for_gpt-3.5-turbo-0125 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use parameterlab/apricot_binary_coqa_deberta-v3-base_for_gpt-3.5-turbo-0125 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="parameterlab/apricot_binary_coqa_deberta-v3-base_for_gpt-3.5-turbo-0125")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("parameterlab/apricot_binary_coqa_deberta-v3-base_for_gpt-3.5-turbo-0125") model = AutoModelForSequenceClassification.from_pretrained("parameterlab/apricot_binary_coqa_deberta-v3-base_for_gpt-3.5-turbo-0125", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from parameterlab/apricot_binary_coqa_deberta-v3-base_for_gpt-3.5-turbo-0125: direct link, hf CLI and curl.
- Browser
- Download file 4.22 kB
-
https://huggingface.co/parameterlab/apricot_binary_coqa_deberta-v3-base_for_gpt-3.5-turbo-0125/resolve/main/training_args.bin
- Command line
-
hf download hf://parameterlab/apricot_binary_coqa_deberta-v3-base_for_gpt-3.5-turbo-0125/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/parameterlab/apricot_binary_coqa_deberta-v3-base_for_gpt-3.5-turbo-0125/resolve/main/training_args.bin
4.22 kB
- Xet hash:
- ab1f8e86a7f2ecc24f3798cd688370791fb224f3e8c8ee9f932eb2bacb2a756d
- Size of remote file:
- 4.22 kB
- SHA256:
- 092ecad811d068d64ee0f0100dc1a8634351b56d59880cef7d4a99e29117645f
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