Update model card metadata: pipeline tag, license, and add Github link

README.md

@@ -1,10 +1,8 @@
-
 ---
-
-pipeline_tag: text-generation
 base_model: TableLLM-13b
 library_name: transformers
-
+pipeline_tag: table-question-answering
+license: llama2
 ---
 
 [![QuantFactory Banner](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)](https://hf.co/QuantFactory)
@@ -17,7 +15,6 @@ This is quantized version of [RUCKBReasoning/TableLLM-13b](https://huggingface.c
 
 ---
 
-license: llama2
 datasets:
 - RUCKBReasoning/TableLLM-SFT
 language:
@@ -29,7 +26,7 @@ tags:
 
 ---
 
-[![QuantFactory Banner](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)](https://hf.co/QuantFactory)
+[![QuantFactory Banner](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44UcQKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)](https://hf.co/QuantFactory)
 
 
 # QuantFactory/TableLLM-13b-GGUF
@@ -52,9 +49,9 @@ We evaluate the code solution generation ability of TableLLM on three benchmarks
 | Model                | WikiTQ | TAT-QA | FeTaQA |  OTTQA  | WikiSQL | Spider | Self-created | Average |
 | :------------------- | :----: | :----: | :----: | :-----: | :-----: | :----: | :----------: | :-----: |
 | TaPEX                |  38.5  |    –   |    –   |    –    |   83.9  |  15.0  |       /      |   45.8  |
-| TaPas                |  31.5  |    –   |    –   |    –    |   74.2  |  23.1  |       /      |   42.92 |
+| TaPas                |  31.5  |    –   |    –    |   74.2  |  23.1  |       /      |   42.92 |
 | TableLlama           |  24.0  |  22.2  |  20.5  |   6.4   |   43.7  |   9.0  |       /      |   20.7  |
-| GPT3.5               |  58.5  |<ins>72.1</ins>|  71.2  |  60.8   |   81.7   |  67.4  | 77.1 |   69.8  |
+| GPT3.5               |  58.5  | 72.1  |  71.2  |  60.8   |   81.7   |  67.4  | 77.1 |   69.8  |
 | GPT4                 |**74.1**|**77.1**|**78.4**|**69.5** |   84.0  |  69.5  |     77.8     | **75.8**|
 | Llama2-Chat (13B)    |  48.8  |  49.6  |  67.7  |  61.5   |    –    |    –   |       –      |   56.9  |
 | CodeLlama (13B)      |  43.4  |  47.2  |  57.2  |  49.7   |   38.3  |  21.9  |     47.6     |   43.6  |
@@ -62,8 +59,8 @@ We evaluate the code solution generation ability of TableLLM on three benchmarks
 | StructGPT (GPT3.5)   |  52.5  |  27.5  |  11.8  |  14.0   |   67.8  |**84.8**|       /      |   48.9  |
 | Binder (GPT3.5)      |  61.6  |  12.8  |   6.8  |   5.1   |   78.6  |  52.6  |       /      |   42.5  |
 | DATER (GPT3.5)       |  53.4  |  28.4  |  18.3  |  13.0   |   58.2  |  26.5  |       /      |   37.0  |
-| TableLLM-7B (Ours)   |  58.8  |  66.9  |  72.6  |<ins>63.1</ins>|<ins>86.6</ins>|  82.6  |<ins>78.8</ins>|   72.8  |
-| TableLLM-13B (Ours)  |<ins>62.4</ins>|  68.2  |<ins>74.5</ins>|  62.5   | **90.7**|<ins>83.4</ins>|   **80.8**   |<ins>74.7</ins>|
+| TableLLM-7B (Ours)   |  58.8  |  66.9  |  72.6  | 63.1 | 86.6|  82.6  | 78.8|   72.8  |
+| TableLLM-13B (Ours)  | 62.4|  68.2  | 74.5|  62.5   | **90.7**| 83.4|   **80.8**   | 74.7|
 
 ## Prompt Template
 The prompts we used for generating code solutions and text answers are introduced below.
@@ -120,5 +117,4 @@ The prompt template for direct text answer generation on short tables.
 ### [Solution][INST/]
 ````
 
-For more details about how to use TableLLM, please refer to our GitHub page: <https://github.com/TableLLM/TableLLM>
-
+For more details about how to use TableLLM, please refer to our GitHub page: <https://github.com/TableLLM/TableLLM>