# Module base
## BaseSummarizer
```python
class BaseSummarizer(BaseComponent)
```
Abstract class for Summarizer
#### predict
```python
@abstractmethod
def predict(documents: List[Document], generate_single_summary: Optional[bool] = None) -> List[Document]
```
Abstract method for creating a summary.
**Arguments**:
- `documents`: Related documents (e.g. coming from a retriever) that the answer shall be conditioned on.
- `generate_single_summary`: Whether to generate a single summary for all documents or one summary per document.
If set to "True", all docs will be joined to a single string that will then
be summarized.
Important: The summary will depend on the order of the supplied documents!
**Returns**:
List of Documents, where Document.text contains the summarization and Document.meta["context"]
the original, not summarized text
# Module transformers
## TransformersSummarizer
```python
class TransformersSummarizer(BaseSummarizer)
```
Transformer based model to summarize the documents using the HuggingFace's transformers framework
You can use any model that has been fine-tuned on a summarization task. For example:
'`bart-large-cnn`', '`t5-small`', '`t5-base`', '`t5-large`', '`t5-3b`', '`t5-11b`'.
See the up-to-date list of available models on
`huggingface.co/models `__
**Example**
```python
| docs = [Document(text="PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions."
| "The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by"
| "the shutoffs which were expected to last through at least midday tomorrow.")]
|
| # Summarize
| summary = summarizer.predict(
| documents=docs,
| generate_single_summary=True
| )
|
| # Show results (List of Documents, containing summary and original text)
| print(summary)
|
| [
| {
| "text": "California's largest electricity provider has turned off power to hundreds of thousands of customers.",
| ...
| "meta": {
| "context": "PGE stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. ..."
| },
| ...
| },
```
#### \_\_init\_\_
```python
def __init__(model_name_or_path: str = "google/pegasus-xsum", model_version: Optional[str] = None, tokenizer: Optional[str] = None, max_length: int = 200, min_length: int = 5, use_gpu: bool = True, clean_up_tokenization_spaces: bool = True, separator_for_single_summary: str = " ", generate_single_summary: bool = False)
```
Load a Summarization model from Transformers.
See the up-to-date list of available models at
https://huggingface.co/models?filter=summarization
**Arguments**:
- `model_name_or_path`: Directory of a saved model or the name of a public model e.g.
'facebook/rag-token-nq', 'facebook/rag-sequence-nq'.
See https://huggingface.co/models?filter=summarization for full list of available models.
- `model_version`: The version of model to use from the HuggingFace model hub. Can be tag name, branch name, or commit hash.
- `tokenizer`: Name of the tokenizer (usually the same as model)
- `max_length`: Maximum length of summarized text
- `min_length`: Minimum length of summarized text
- `use_gpu`: Whether to use GPU (if available).
- `clean_up_tokenization_spaces`: Whether or not to clean up the potential extra spaces in the text output
- `separator_for_single_summary`: If `generate_single_summary=True` in `predict()`, we need to join all docs
into a single text. This separator appears between those subsequent docs.
- `generate_single_summary`: Whether to generate a single summary for all documents or one summary per document.
If set to "True", all docs will be joined to a single string that will then
be summarized.
Important: The summary will depend on the order of the supplied documents!
#### predict
```python
def predict(documents: List[Document], generate_single_summary: Optional[bool] = None, truncation: bool = True) -> List[Document]
```
Produce the summarization from the supplied documents.
These document can for example be retrieved via the Retriever.
**Arguments**:
- `documents`: Related documents (e.g. coming from a retriever) that the answer shall be conditioned on.
- `generate_single_summary`: Whether to generate a single summary for all documents or one summary per document.
If set to "True", all docs will be joined to a single string that will then
be summarized.
Important: The summary will depend on the order of the supplied documents!
- `truncation`: Truncate to a maximum length accepted by the model
**Returns**:
List of Documents, where Document.text contains the summarization and Document.meta["context"]
the original, not summarized text