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", device_map="auto")# 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
Update README.md
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README.md
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@@ -51,7 +51,7 @@ I had three benchmarks, the WikiTableQuestions dataset, the TabFact dataset, and
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| google/tapas-base-finetuned-wtq (before Fine-tuning) | 0.2933 | 0.3405 | 0.5005 | 0.2512 |
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| google/tapas-base-finetuned-wtq (Fine-tuned) | 0.4667 | 0.3405 | 0.5005 | 0.2525 |
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| mistralai/Mistral-7B-Instruct-v0.3 | 0 | Exact Match: 0.0346 / Fuzzy Match: 0.4744| 0.4995 |
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| meta-llama/Llama-3.2-1B | 0.0133 | Exact Match: 0.0593 / Fuzzy Match: 0.2769| 0.4995 | 0.0238 |
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## Usage and Intended Uses
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| google/tapas-base-finetuned-wtq (before Fine-tuning) | 0.2933 | 0.3405 | 0.5005 | 0.2512 |
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| google/tapas-base-finetuned-wtq (Fine-tuned) | 0.4667 | 0.3405 | 0.5005 | 0.2525 |
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| mistralai/Mistral-7B-Instruct-v0.3 | 0 | Exact Match: 0.0346 / Fuzzy Match: 0.4744| 0.4995 | 0.0296 |
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| meta-llama/Llama-3.2-1B | 0.0133 | Exact Match: 0.0593 / Fuzzy Match: 0.2769| 0.4995 | 0.0238 |
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## Usage and Intended Uses
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