Update README.md

Author: Aunavin1

README.md

@@ -194,36 +194,7 @@ Note 2 (**Update: 22-01-2019**): Most of the English-language experiments are ex
 
 In this title, I will save the previous updates for me and the visitors to keep track.
 
-## February 2019
-
-### 04-02-2019
-
-- Code development for NER evaluation (F1 scores for tag based, bio+tag based) still continues. I slowed down it a little bit due to personal life issues.
-- Decided to simplify README.md and use [Wiki](https://github.com/hbahadirsahin/nlp-experiments-in-pytorch/wiki) of this repository.
- - All entries related to the updates in January 2019 are moved to [related Wiki page](https://github.com/hbahadirsahin/nlp-experiments-in-pytorch/wiki/Previous-Updates-(January-2019)). 
- - I will save my experiment results in Wiki, too.
- - While trying to figure out how to use README and Wiki more efficiently, I will figure better things out hopefully =)
-- As you may noticed, TextCNN experiments are finished. I will continue with LSTM/GRU experiments (first, I have to figure out a good parameter set =)).
-
-### Update 12-02-2019
- 
-- Since I separated classification and NER trainers/evaluators, I decided to create "scorer" folder to prevent bloating "utils.py" with metric calculation functions.
- - "scorer/" will contain current and future metric calculation methods (not giving too much details, since you can always check it =)).
-- I encountered some bugs in NER training and evaluation processes due to save/load functionalities. Hopefully, I fully fixed them. but if anyone out there reading this and using this repository, if you find any bugs, just let me know.
-- Made some minor changes in namings and indexing (not much crucial stuff, details can be found in git commit message).
-- Personal life issues still continues, hence slow development-slow experiment mode still continues.
-
-### Update 21-02-2019
- 
-- Precision, recall and F1 metrics are added into "ner_scorer.py". 
- - Since these metrics must be calculated for full set (not batch-based), I changed the evaluator flow a little bit.
- - Evaluator reports mean precision, recall and F1 scores over all tags/named-entities. 
- - Detailed, tag-based, scores can be also reported by activating boolean detailed_ner_log (default value is true).
-- In LSTM, I encountered a minor bug while using "bidirectional=true". Hopefully, it is fixed (at least training/evaluating was working on a small set).
-- I tried a larger set to see whether my code is working but I got a "cuda illegal memory access" error. I think it is because OOM issues, but I am not sure for now. 
-- An "allowed_transition" stuff will be added in near future (like allennlp/conditional_random_field.py).
-- Also, I updated my libraries, hence requirements.txt is changed, too =)
-- Personal life issues still continues, hence slow development-slow experiment mode still continues.
+## March 2019
 
 ## References for Code Development