Update README.rst

README.rst

@@ -20,25 +20,26 @@ Haystack — Neural Question Answering At Scale
 Introduction
 ============
 
-The performance of **modern Question Answering Models** (BERT, ALBERT ...) has seen drastic improvements within the last year enabling many new opportunities for finding information more efficiently. However, those models are usually designed to find answers within rather small text passages. **Haystack let's you scale QA models** to large collections of documents!
+The performance of **modern Question Answering Models** (BERT, ALBERT ...) has seen drastic improvements within the last year enabling many new opportunities for accessing information more efficiently. However, those models are designed to find answers within rather small text passages. **Haystack let's you scale QA models** to large collections of documents!
 
 Haystack is designed in a modular way and let's you use any QA models trained with  `FARM <https://github.com/deepset-ai/FARM>`_ or `Transformers <https://github.com/huggingface/transformers>`_.
 
-Swap your models easily from BERT to roBERTa and scale the database from dev (Sqlite) to production (PostgreSQL, elasticsearch ...).
+Swap your models easily from BERT to roBERTa and scale the database from dev (Sqlite) to production (elasticsearch).
 
 Core Features
 =============
-- **Powerful models**: Utilize all the latest transformer based models (BERT, ALBERT roBERTa ...)
+- **Powerful models**: Utilize all latest transformer based models (BERT, ALBERT roBERTa ...)
-- **Modular & future-proof**: Avoid technical debt. With haystack you can easily switch to newer models once they get published.
+- **Modular & future-proof**: Easily switch to newer models once they get published.
 - **Developer friendly**: Easy to debug, extend and modify.
-- **Scalable**: Switch from dev to production within minutes.
+- **Scalable**: Production-ready deployments via Elasticsearch backend.
+- **Customizable**: Fine-tune models to your own domain.
 
 Components
 ==========
 
-1. **Retriever**:  Fast, simple model that identifies candidate passages from a large collection of documents. Algorithms include TF-IDF, which is similar to what's used in popular search systems like Elasticsearch. The Retriever helps to narrow down the scope for Reader to smaller units of text where a given question could be answered.
+1. **Retriever**:  Fast, simple algorithm that identifies candidate passages from a large collection of documents. Algorithms include TF-IDF or BM25, which is similar to what's used in Elasticsearch. The Retriever helps to narrow down the scope for Reader to smaller units of text where a given question could be answered.
 
-2. **Reader**: Powerful neural model that reads through texts in detail to find an answer. Use diverse models like BERT, Roberta or XLNet trained via `FARM <https://github.com/deepset-ai/FARM>`_ or `Transformers <https://github.com/huggingface/transformers>`_ on SQuAD like tasks. The Reader takes multiple passages of text as input and returns top-n answers with corresponding confidence scores.
+2. **Reader**: Powerful neural model that reads through texts in detail to find an answer. Use diverse models like BERT, Roberta or XLNet trained via `FARM <https://github.com/deepset-ai/FARM>`_ or `Transformers <https://github.com/huggingface/transformers>`_ on SQuAD like tasks. The Reader takes multiple passages of text as input and returns top-n answers with corresponding confidence scores. You can just load a pretrained model from  `huggingface's model hub <https://huggingface.co/models>`_ or fine-tune it to your own domain data. 
 
 3. **Finder**: Glues together a Reader and a Retriever as a pipeline to provide an easy-to-use question answering interface.