[Arrow writer, Trivia_qa] Could not convert TagMe with type str: converting to null type
Good point, I think the schema should be infered at the writing stage where we have a `writer_batch_size` number of examples (typically 10k) so it's even less likely to run into this scenario.
Running the following code 

```
import nlp
ds = nlp.load_dataset("trivia_qa", "rc", split="validation[:1%]")  # this might take 2.3 min to download but it's cached afterwards...
ds.map(lambda x: x, load_from_cache_file=False)
```

triggers a `ArrowInvalid: Could not convert TagMe with type str: converting to null type` error.

On the other hand if we remove a certain column of `trivia_qa` which seems responsible for the bug, it works:

```
import nlp
ds = nlp.load_dataset("trivia_qa", "rc", split="validation[:1%]")  # this might take 2.3 min to download but it's cached afterwards...
ds.map(lambda x: x, remove_columns=["entity_pages"], load_from_cache_file=False)
```

. Seems quite hard to debug what's going on here... @lhoestq @thomwolf - do you have a good first guess what the problem could be?

**Note** BTW: I think this could be a good test to check that the datasets work correctly: Take a tiny portion of the dataset and check that it can be written correctly.
[Arrow writer, Trivia_qa] Could not convert TagMe with type str: converting to null type 
 Running the following code 

```
import nlp
ds = nlp.load_dataset("trivia_qa", "rc", split="validation[:1%]")  # this might take 2.3 min to download but it's cached afterwards...
ds.map(lambda x: x, load_from_cache_file=False)
```

triggers a `ArrowInvalid: Could not convert TagMe with type str: converting to null type` error.

On the other hand if we remove a certain column of `trivia_qa` which seems responsible for the bug, it works:

```
import nlp
ds = nlp.load_dataset("trivia_qa", "rc", split="validation[:1%]")  # this might take 2.3 min to download but it's cached afterwards...
ds.map(lambda x: x, remove_columns=["entity_pages"], load_from_cache_file=False)
```

. Seems quite hard to debug what's going on here... @lhoestq @thomwolf - do you have a good first guess what the problem could be?

**Note** BTW: I think this could be a good test to check that the datasets work correctly: Take a tiny portion of the dataset and check that it can be written correctly. 
 Good point, I think the schema should be infered at the writing stage where we have a `writer_batch_size` number of examples (typically 10k) so it's even less likely to run into this scenario.