# gFun - RAI

## Setup:
```
git clone https://gitea-s2i2s.isti.cnr.it/andrea.pedrotti/gfun_multimodal.git
cd gfun_multimodal
mkdir models
mkdir resources
# optional
mkdir models/category_mappers
```
In `models`, scaricare i modelli pre-trained condivisi. La directory `models` contiene 4 subdir `metaclassifier, vgfs, vectorizer, category_mappers`.
In `resources` estrarre i muse-embeddings.
In `models/category_mappers` estrarre il file csv che contiene il mapping da category label a category id (opzionale).

## Inference:
Per eseguire la classificazione dei documenti:

```python
python infer.py --datapth <path/to/the/csv_file.csv>
```

I risultati saranno salvati di default nella cartella `results/inference-preds`, in un file csv denominato a seconda input file specificato in `--datapath` + il timetamp della run (e.g., `<csv_file>_<240312_13345>.csv`) (è possibile cambiare directory di output tramite `--outdir <my/output/dir/>`)


```
optional arguments:
  -h, --help            show this help message and exit
  --datapath            path to csv file containing the documents to be classified
  --outdir              path to store csv file containing gfun predictions (default=results/inference-preds)
  --category_map         path to csv file containing the mapping from label name to label id [str: id] (default=models/category_mappers/rai-mapping.csv)
  --nlabels             number of target classes defined in the annotation schema (default=28)
  --muse_dir            path to muse embeddings
  --trained_gfun        name of the trained gfun instance
```