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Minor fixes in pandas docs #1567

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merged 2 commits into from
Jan 9, 2025
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Minor fixes in pandas docs #1567

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26ce10d
minor fixes
lhoestq Jan 9, 2025
2e2520a
again
lhoestq Jan 9, 2025
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8 changes: 5 additions & 3 deletions docs/hub/datasets-pandas.md
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Expand Up @@ -90,7 +90,7 @@ for image_path in (folder_path + df["file_name"]):
...
```

Since the dataset is in a supported structure (a `metadata.csv` file with a `file_name` field), you can save this dataset to Hugging Face and the Dataset Viewer shows both the metadata and images on Hugging Face.
Since the dataset is in a [supported structure](https://huggingface.co/docs/hub/en/datasets-image#additional-columns) (a `metadata.csv` or `.jsonl` file with a `file_name` field), you can save this dataset to Hugging Face and the Dataset Viewer shows both the metadata and images on Hugging Face.

```python
from huggingface_hub import HfApi
Expand Down Expand Up @@ -148,7 +148,7 @@ for audio_path in (folder_path + df["file_name"]):
...
```

Since the dataset is in a supported structure (a `metadata.csv` file with a `file_name` field), you can save it to Hugging Face, and the Hub Dataset Viewer shows both the metadata and audio.
Since the dataset is in a [supported structure](https://huggingface.co/docs/hub/en/datasets-audio#additional-columns) (a `metadata.csv` or `.jsonl` file with a `file_name` field), you can save it to Hugging Face, and the Hub Dataset Viewer shows both the metadata and audio.

```python
from huggingface_hub import HfApi
Expand Down Expand Up @@ -213,7 +213,9 @@ df[["label", "score"]] = [(y["label"], y["score"]) for y in pipe(x for x in tqdm
from transformers import pipeline
from tqdm import tqdm

p = pipeline("text-generation", model="Qwen/Qwen2.5-1.5B-Instruct")
pipe = pipeline("text-generation", model="Qwen/Qwen2.5-1.5B-Instruct")

# Generate chat response
prompt = "What is the main topic of this sentence ? REPLY IN LESS THAN 3 WORDS. Sentence: '{}'"
df["output"] = [y["generated_text"][1]["content"] for y in pipe([{"role": "user", "content": prompt.format(x)}] for x in tqdm(df["text"]))]
```
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