File size: 1,261 Bytes
6fe3cfe f36acdd 6fe3cfe 5011d78 6fe3cfe 5011d78 6fe3cfe 012faac 6fe3cfe e1523a6 6fe3cfe 012faac 5011d78 012faac |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 |
---
tags:
- adapter-transformers
- roberta
- adapterhub:sentiment/rotten_tomatoes
datasets:
- rotten_tomatoes
pipeline_tag: text-classification
widget:
- text: "Adapters are awesome"
---
# Adapter `solwol/my-awesome-adapter` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [sentiment/rotten_tomatoes](https://adapterhub.ml/explore/sentiment/rotten_tomatoes/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `transformers` and `adapters`:
```
pip install -U transformers adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("roberta-base")
adapter_name = model.load_adapter("solwol/my-awesome-adapter", source="hf", set_active=True)
adapter_name
```
Next, to perform sentiment classification:
```python
from transformers import AutoTokenizer, TextClassificationPipeline
tokenizer = AutoTokenizer.from_pretrained("roberta-base")
classifier = TextClassificationPipeline(model=model, tokenizer=tokenizer)
classfifier("Adapters are awesome!")
``` |