Update README.md

README.md

@@ -17,34 +17,8 @@ TAPEX is based on the BART architecture, the transformer encoder-encoder (seq2se
 
 ## Intended Uses
 
-You can use the raw model for simulating neural SQL execution, i.e., employ TAPEX to execute a SQL query on a given table. However, the model is mostly meant to be fine-tuned on a supervised dataset. Currently TAPEX can be fine-tuned to tackle table question answering tasks and table fact verification tasks. See the [model hub](https://huggingface.co/models?search=tapex) to look for fine-tuned versions on a task that interests you.
-
-### How to Use
-
-Here is how to use this model in transformers:
-
-```python
-from transformers import TapexTokenizer, BartForConditionalGeneration
-import pandas as pd
-
-tokenizer = TapexTokenizer.from_pretrained("microsoft/tapex-large")
-model = BartForConditionalGeneration.from_pretrained("microsoft/tapex-large")
-
-data = {
-    "year": [1896, 1900, 1904, 2004, 2008, 2012],
-    "city": ["athens", "paris", "st. louis", "athens", "beijing", "london"]
-}
-table = pd.DataFrame.from_dict(data)
-
-# tapex accepts uncased input since it is pre-trained on the uncased corpus
-query = "select year where city = beijing"
-encoding = tokenizer(table=table, query=query, return_tensors="pt")
-
-outputs = model.generate(**encoding)
-
-print(tokenizer.batch_decode(outputs, skip_special_tokens=True))
-# ['2008']
-```
+⚠️ This model checkpoint is **ONLY** used for fine-tuining on downstream tasks, and you **CANNOT** use this model for simulating neural SQL execution, i.e., employ TAPEX to execute a SQL query on a given table. The one that can neurally execute SQL queries is at [here](https://huggingface.co/microsoft/tapex-large-sql-execution).
+> This separation of two models for two kinds of intention is because of a known issue in BART large, and we recommend readers to see [this comment](https://github.com/huggingface/transformers/issues/15559#issuecomment-1062880564) for more details.
 
 ### How to Fine-tuning