update readme

README.md

@@ -6,60 +6,43 @@
 
 This readme is heavily based (i.e. copied from) the Anserini readme.
 
-This is the docker image for BM25PRF conforming to the [OSIRRC jig](https://github.com/osirrc/jig/) for the [Open-Source IR Replicability Challenge (OSIRRC) at SIGIR 2019](https://osirrc.github.io/osirrc2019/).
+This is the docker image for implementing BM25 + Pseudo Relevance Feedback (PRF) [1] with Anserini [2]. The image is conforming to the [OSIRRC jig](https://github.com/osirrc/jig/) for the [Open-Source IR Replicability Challenge (OSIRRC) at SIGIR 2019](https://osirrc.github.io/osirrc2019/).
 
 This image is available on [Docker Hub](https://hub.docker.com/r/osirrc2019/anserini-bm25prf).
 <!-- The [OSIRRC 2019 image library](https://github.com/osirrc/osirrc2019-library) contains a log of successful executions of this image. -->
 
 This image implemented Bm25+Pseudo Relevance Feedback(PRF) with Anserini.
 
 + Supported test collections: `robust04`
-+ Supported hooks: `init`, `index`, `search`, `interact`
++ Supported hooks: `init`, `index`, `search` and `train`
 
 
 ## Quick Start
 
 The following `jig` command can be used to index TREC disks 4/5 for `robust04`:
 
-```bash
+```sh
 python run.py prepare \
-  --repo osirrc2019/anserini_bm25prf   \
+  --repo osirrc2019/anserini-bm25prf \
   --tag latest \
   --collections robust04=/path/to/disk45=trectext
 ```
 
-The following `jig` command can be used to perform a retrieval run on the collection with the `robust04` test collection with default params.
+The following `jig` command can be used to perform a retrieval run on the collection with the `robust04` test collection with default hyper-parameters.
 
-```
+```sh
 python run.py search \
-  --repo osirrc2019/anserini_bm25prf   \
+  --repo osirrc2019/anserini-bm25prf \
   --output out \
   --qrels qrels/qrels.robust04.txt \
   --topic topics/topics.robust04.txt \
   --collection robust04 \ 
   --top_k 1000
 ```
 
-The following `jig` command can be used to tune the hyper-parameters (first 50 topics in `robust04` will be used as validation set).
+The following `jig` command can be used to tune the hyper-parameters.
 Note that the grid search may take several hours.
-```
-python run.py interact \
-  --repo osirrc2019/anserini_bm25prf \
-  --tag latest \
-```
-
-
-## Expected Results
-
-The following numbers should be able to be re-produced using the scripts provided by the jig.
-
-### robust04
-[TREC 2004 Robust Track Topics](http://trec.nist.gov/data/robust/04.testset.gz).
-
-+ **Tuning BM25+PRF**: 
-
-Command:
-```
+```sh
 python run.py train \
    --repo osirrc2019/anserini-bm25prf \
    --tag latest \
@@ -71,18 +54,19 @@ python run.py train \
    --collection robust04
 ```
 
-|Paramteres | k1 | b | k1_prf | b_prf | num_new_terms | num_docs | new_term_weigh |
-| Value |  0.9 | 0.2 | 0.9 | 0.6 | 40 | 10 | 0.1 |
 
+## Expected Results on [TREC 2004 Robust](http://trec.nist.gov/data/robust/04.testset.gz).
+
+The following numbers should be able to be re-produced using the scripts provided by the jig.
 
-### BM25+PRF with default parameters 
+#### BM25+PRF with Default Hyper-paramteres 
 
-k1=0.9 b=0.4 k1_prf=0.9 b_prf=0.4 num_new_termss=20 num_docs=10 new_term_weight=0.2
+Hyper-paramteres: k1=0.9 b=0.4 k1_prf=0.9 b_prf=0.4 num_new_termss=20 num_docs=10 new_term_weight=0.2
 
 Command:
-```
+```sh
 python run.py search \
-  --repo osirrc2019/anserini_bm25prf   \
+  --repo osirrc2019/anserini-bm25prf   \
   --output out \
   --qrels qrels/qrels.robust04.txt \
   --topic topics/topics.robust04.txt \
@@ -95,11 +79,32 @@ python run.py search \
 | P@30 | 0.3438 |
 
 
-### BM25+PRF with Tuned parameters 
-k1=0.9 b=0.2 k1_prf=0.9 b_prf=0.6 num_new_temrs=40 num_docs=10 new_term_weight=0.1 
+#### Tuning BM25+PRF
 
 Command:
+```sh
+python run.py train \
+   --repo osirrc2019/anserini-bm25prf \
+   --tag latest \
+   --topic topics/topics.robust04.txt \
+   --qrels $(pwd)/qrels/qrels.robust04.txt \
+   --validation_split $(pwd)/sample_training_validation_query_ids/robust04_validation.txt \
+   --test_split $(pwd)/sample_training_validation_query_ids/robust04_test.txt \
+   --model_folder $(pwd)/trained \
+   --collection robust04
 ```
+Tuned Hyper-paramteres: 
+
+| Paramteres | k1 | b | k1_prf | b_prf | num_new_terms | num_docs | new_term_weight |
+|----|----|----|----|----|----|----|----|
+| Value |  0.9 | 0.2 | 0.9 | 0.6 | 40 | 10 | 0.1 |
+
+
+#### BM25+PRF with Tuned Hyper-paramteres 
+Hyper-paramteres: k1=0.9 b=0.2 k1_prf=0.9 b_prf=0.6 num_new_temrs=40 num_docs=10 new_term_weight=0.1 
+
+Command:
+```sh
  python run.py search \
   --repo osirrc2019/anserini-bm25prf \
   --output out \
@@ -114,11 +119,12 @@ Command:
 | MAP |  0.2916 |
 | P@30 | 0.3396 |
 
-Yes, the tuned paramters are worse than the default ones.......
+Yes, the tuned hyper-paramteres make the performance worse.......
 
 
 
 ## Reference
 
-* Stephen E. Robertson, and Karen Spärck Jones. Simple, proven approaches to text retrieval. University of Cambridge Computer Laboratory, 1994.
-* Peilin Yang, Hui Fang, and Jimmy Lin. Anserini: Enabling the Use of Lucene for Information Retrieval Research. SIGIR 2017
+[1] Stephen E. Robertson, and Karen Spärck Jones. Simple, proven approaches to text retrieval. University of Cambridge Computer Laboratory, 1994.
+
+[2] Peilin Yang, Hui Fang, and Jimmy Lin. Anserini: Enabling the Use of Lucene for Information Retrieval Research. SIGIR 2017