---
tags:
- adapter-transformers
- adapterhub:am/wikipedia-amharic-20240320
- xlm-roberta-base
datasets:
- wikipedia
pipeline_tag: fill-mask
---

# Adapter `solwol/xml-roberta-base-adapter-amharic` for xlm-roberta-base

An [adapter](https://adapterhub.ml) for the `xlm-roberta-base` model that was trained on the [am/wikipedia-amharic-20240320](https://adapterhub.ml/explore/am/wikipedia-amharic-20240320/) dataset and includes a prediction head for masked lm.

This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.

## Usage

First, install `transformers` `adapters`:

```
pip install -U trasnformers adapters
```

Now, the adapter can be loaded and activated like this:

```python
from adapters import AutoAdapterModel

model = AutoAdapterModel.from_pretrained("xlm-roberta-base")
adapter_name = model.load_adapter("solwol/xml-roberta-base-adapter-amharic", source="hf", set_active=True)
```
Next, to perform fill-mask task:

```python
from transformers import AutoTokenizer, FillMaskPipeline

tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-base")
fillmask = FillMaskPipeline(model=model, tokenizer=tokenizer)

inputs = ["መልካም አዲስ <mask> ይሁን",
         "የኢትዮጵያ ዋና <mask> አዲስ አበባ ነው",
         "ኬንያ የ ኢትዮጵያ አዋሳኝ <mask> አንዷ ናት",
         "አጼ ምኒሊክ የኢትዮጵያ <mask> ነበሩ"]

outputs = fillmask(inputs)
outputs[0]

[{'score': 0.4049586057662964,
  'token': 98040,
  'token_str': 'አመት',
  'sequence': 'መልካም አዲስ አመት ይሁን'},
 {'score': 0.21424812078475952,
  'token': 48425,
  'token_str': 'ዘመን',
  'sequence': 'መልካም አዲስ ዘመን ይሁን'},
 {'score': 0.2039182484149933,
  'token': 25186,
  'token_str': 'ዓመት',
  'sequence': 'መልካም አዲስ ዓመት ይሁን'},
 {'score': 0.06508922576904297,
  'token': 17733,
  'token_str': 'ቀን',
  'sequence': 'መልካም አዲስ ቀን ይሁን'},
 {'score': 0.018085109069943428,
  'token': 38455,
  'token_str': 'ዓለም',
  'sequence': 'መልካም አዲስ ዓለም ይሁን'}]
```
## Fine-tuning data
Wikipedia amahric dataset snapshot date "20240320"