Instructions to use am5uc/ServiceNow_Table_Question_Answering with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use am5uc/ServiceNow_Table_Question_Answering with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("table-question-answering", model="am5uc/ServiceNow_Table_Question_Answering")# Load model directly from transformers import AutoTokenizer, AutoModelForTableQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("am5uc/ServiceNow_Table_Question_Answering") model = AutoModelForTableQuestionAnswering.from_pretrained("am5uc/ServiceNow_Table_Question_Answering", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 31b754f5df972f8a41a07455c452996bd803f8f269e8a839a0daf6d764e7f11c
- Size of remote file:
- 443 MB
- SHA256:
- 60a64b14912a3a7255b82c2f9d958c927917f0edcb0bc2b30bd096633e5f1ae4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.