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")# 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
- Xet hash:
- 7aa30ccebdbb8c4758519cfd7bacd5fb0c90618ef2f53dcd6e5481ade3b57aa1
- Size of remote file:
- 738 MB
- SHA256:
- a684b15a0981720c097cf72bacdf123f84309919b348638f6bd3856200aa19e6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.