lfs added

.gitattributes

@@ -32,3 +32,19 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+models/adaboost_clf.sav filter=lfs diff=lfs merge=lfs -text
+models/m_nb.sav filter=lfs diff=lfs merge=lfs -text
+models/random_forest.sav filter=lfs diff=lfs merge=lfs -text
+models/tfidf_vectorizer.sav filter=lfs diff=lfs merge=lfs -text
+models/b_nb.sav filter=lfs diff=lfs merge=lfs -text
+models/count_vectorizer.sav filter=lfs diff=lfs merge=lfs -text
+models/decision_tree.sav filter=lfs diff=lfs merge=lfs -text
+models/logistic_regression.sav filter=lfs diff=lfs merge=lfs -text
+models/xgb_clf.sav filter=lfs diff=lfs merge=lfs -text
+models/flair-sentiment-classifier/final-model.pt filter=lfs diff=lfs merge=lfs -text
+models/flair-sentiment-classifier/best-model.pt filter=lfs diff=lfs merge=lfs -text
+models/setfit-classifier/pytorch_model.bin filter=lfs diff=lfs merge=lfs -text
+models/spacy-classifier/model-best/textcat/cfg filter=lfs diff=lfs merge=lfs -text
+models/spacy-classifier/model-best/textcat/model filter=lfs diff=lfs merge=lfs -text
+models/spacy-classifier/model-best/vocab/key2row filter=lfs diff=lfs merge=lfs -text
+models/setfit-classifier/model_head.pkl filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,70 @@
+"""
+    @author : Sakshi Tatak
+"""
+
+# Imports
+import pandas as pd
+import streamlit as st
+
+from predict_flair import SentimentClassifier as FlairSentimentClassifier
+from predict_ml import predict as predict_ml
+from predict_setfit import SentimentClassifier as SetFitSentimentClassifier
+from predict_spacy import SentimentClassifier as SpacySentimentClassifier
+
+st.set_page_config(layout = 'wide')
+st.title('SetFit, Flair, SpaCy, Naive Bayes Sentiment Classifiers')
+
+if 'flair_model' not in st.session_state:
+    st.session_state['flair_model'] = None
+
+if 'spacy_model' not in st.session_state:
+    st.session_state['spacy_model'] = None
+
+if 'setfit_model' not in st.session_state:
+    st.session_state['setfit_model'] = None
+
+if 'results' not in st.session_state:
+    st.session_state['results'] = pd.DataFrame(columns = ['model', 'query', 'sentiment', 'confidence'])
+
+def main():
+    model_name = st.selectbox('Select Model', options = ['SetFit', 'Naive Bayes', 'Flair', 'SpaCy'])
+    if model_name == 'SetFit':
+        if st.session_state.setfit_model is None:
+            with st.spinner('Loading SetFit classifier ...'):
+                st.session_state.setfit_model = SetFitSentimentClassifier()
+            st.success('SetFit classifier loaded successfully!')
+        model = st.session_state.setfit_model
+
+    if model_name == 'Flair':
+        if st.session_state.flair_model is None:
+            with st.spinner('Loading Flair classifier ...'):
+                st.session_state.flair_model = FlairSentimentClassifier()
+            st.success('Flair classifier loaded successfully!')
+        model = st.session_state.flair_model
+
+    if model_name == 'SpaCy':
+        if st.session_state.spacy_model is None:
+            with st.spinner('Loading SpaCy classifier'):
+                st.session_state.spacy_model = SpacySentimentClassifier()
+            st.success('Spacy classifier loaded successfully!')
+        model = st.session_state.spacy_model
+
+    text = st.text_area('Input text', value = 'This is insane haha!')
+
+    if st.button('Compute sentiment'):
+        if model_name != 'Naive Bayes':
+            with st.spinner(f'Predicting with {model_name} ...'):
+                sentiment, conf = model.predict(text)
+        else:
+            with st.spinner('Predicting with Naive Bayes ...'):
+                sentiment, conf = predict_ml(text)
+
+        st.success(sentiment + ', ' + str(conf))
+        df = st.session_state.results
+        df.loc[len(df)] = [model_name, text, sentiment, conf]
+        st.table(df)
+
+
+
+if __name__ == '__main__':
+    main()

models/adaboost_clf.report

@@ -0,0 +1,27 @@
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+    }
+}

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models/b_nb.report

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models/decision_tree.report

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models/flair-sentiment-classifier/best-model.pt

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models/flair-sentiment-classifier/loss.tsv

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+EPOCH	TIMESTAMP	BAD_EPOCHS	LEARNING_RATE	TRAIN_LOSS	DEV_LOSS	DEV_PRECISION	DEV_RECALL	DEV_F1	DEV_ACCURACY
+1	08:29:02	0	0.0100	0.01253135909052456	0.01149754598736763	0.5393	0.5393	0.5393	0.5393
+2	08:29:20	0	0.0100	0.011174857321907492	0.011094754561781883	0.586	0.586	0.586	0.586
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+33	08:38:13	3	0.0100	0.009884989871698268	0.009982590563595295	0.66	0.66	0.66	0.66
+34	08:38:30	0	0.0100	0.009801310083445381	0.009954135864973068	0.6647	0.6647	0.6647	0.6647
+35	08:38:47	1	0.0100	0.009773709009675419	0.009962853975594044	0.658	0.658	0.658	0.658

models/flair-sentiment-classifier/test.tsv

@@ -0,0 +1,1563 @@
+i loooooooovvvvvveee my kindle2. not that the dx is cool, but the 2 is fantastic in its own right.
+ - Gold: 1
+ - Pred: 1
+
+reading my kindle2. love it. lee childs is good read.
+ - Gold: 1
+ - Pred: 1
+
+ok, first assesment of the #kindle2 .it fucking rocks!
+ - Gold: 1
+ - Pred: 1
+
+you will love your kindle2. i have had mine for a few months and never looked back. the new big one is huge! no need for remorse! :)
+ - Gold: 1
+ - Pred: 0
+ -> MISMATCH!
+
+fair enough. but i have the kindle2 and i think it is perfect :)
+ - Gold: 1
+ - Pred: 1
+
+no. it is too big. i am quite happy with the kindle2.
+ - Gold: 1
+ - Pred: 1
+
+fuck this economy. i hate aig and their non loan given asses.
+ - Gold: 0
+ - Pred: 0
+
+jquery is my new best friend.
+ - Gold: 1
+ - Pred: 1
+
+loves twitter
+ - Gold: 1
+ - Pred: 1
+
+how can you not love obama! he makes jokes about himself.
+ - Gold: 1
+ - Pred: 1
+
+i firmly believe that obama/pelosi have zero desire to be civil. it is a charade and a slogan, but they want to destroy conservatism
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+house correspondents dinner was last night whoopi, barbara & sherri went, obama got a standing ovation
+ - Gold: 1
+ - Pred: 1
+
+watchin espn.jus seen this new merical with a puppet lebron.sh*t was hilarious.lmao!
+ - Gold: 1
+ - Pred: 1
+
+dear nike, stop with the flywire. that shit is a waste of science. and ugly. love,
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+#lebron best athlete of our generation, if not all time (basketball related) i don not want to get into inter-sport debates about __1/2
+ - Gold: 1
+ - Pred: 0
+ -> MISMATCH!
+
+i was talking to this guy last night and he was telling me that he is a die hard spurs fan. he also told me that he hates lebron james.
+ - Gold: 0
+ - Pred: 0
+
+i love lebron.
+ - Gold: 1
+ - Pred: 1
+
+lebron is a beast, but i am still cheering 4 the a.til the end.
+ - Gold: 0
+ - Pred: 0
+
+lebron is the boss
+ - Gold: 1
+ - Pred: 1
+
+lebron is a hometown hero to me, lol i love the lakers but let is go cavs, lol
+ - Gold: 1
+ - Pred: 1
+
+lebron and zydrunas are such an awesome duo
+ - Gold: 1
+ - Pred: 1
+
+lebron is a beast. nobody in the es even close.
+ - Gold: 1
+ - Pred: 0
+ -> MISMATCH!
+
+downloading apps for my iphone! so much fun :-) there literally is an app for just about anything.
+ - Gold: 1
+ - Pred: 0
+ -> MISMATCH!
+
+good news, just had a call from the visa office, saying everything is fine.what a relief! i am sick of scams out there! stealing!
+ - Gold: 1
+ - Pred: 0
+ -> MISMATCH!
+
+- e back from (via )
+ - Gold: 1
+ - Pred: 1
+
+in montreal for a long weekend of r&r. much needed.
+ - Gold: 1
+ - Pred: 0
+ -> MISMATCH!
+
+booz allen hamilton has a bad ass homegrown social collaboration platform. way cool! #ttiv
+ - Gold: 1
+ - Pred: 1
+
+[#mluc09] customer innovation award winner: booz allen hamilton --
+ - Gold: 1
+ - Pred: 1
+
+i current use the nikon d90 and love it, but not as much as the canon 40d/50d. i chose the d90 for the video feature. my mistake.
+ - Gold: 1
+ - Pred: 1
+
+google is always a good place to look. should have mentioned i worked on the mustang w/ my dad, .
+ - Gold: 1
+ - Pred: 1
+
+played with an android google phone. the slide out screen scares me i would break that fucker so fast. still prefer my iphone.
+ - Gold: 0
+ - Pred: 0
+
+us planning to resume the military tribunals at guantanamo bay. only this time those on trial will be aig execs and chrysler debt holders
+ - Gold: 0
+ - Pred: 0
+
+omg so bored & my tattoooos are so itchy! help! aha =)
+ - Gold: 0
+ - Pred: 0
+
+i am itchy and miserable!
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+no. i am not itchy for now. maybe later, lol.
+ - Gold: 0
+ - Pred: 0
+
+rt i love the nerdy stanford human biology videos - makes me miss school.
+ - Gold: 1
+ - Pred: 1
+
+: has been a bit crazy, with steep learning curve, but lyx is really good for long docs. for anything shorter, it would be insane.
+ - Gold: 1
+ - Pred: 0
+ -> MISMATCH!
+
+i am listening to "p.y.t" by danny gokey <3 <3 <3 aww, he is so amazing. i <3 him so much :)
+ - Gold: 1
+ - Pred: 1
+
+is going to sleep then on a bike ride:]
+ - Gold: 1
+ - Pred: 0
+ -> MISMATCH!
+
+cant sleep. my tooth is aching.
+ - Gold: 0
+ - Pred: 0
+
+blah, blah, blah same old same old. no plans today, going back to sleep i guess.
+ - Gold: 0
+ - Pred: 0
+
+glad i didnt do bay to breakers today, it is 1000 freaking degrees in san francisco wtf
+ - Gold: 0
+ - Pred: 0
+
+!obama administration must stop bonuses to aig ponzi schemers .
+ - Gold: 0
+ - Pred: 0
+
+started to think that citi is in really deep s&^t. are they gonna survive the turmoil or are they gonna be the next aig!
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+shaunwoo hate'n on aig
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+you will not regret going to see star trek. it was awesome!
+ - Gold: 1
+ - Pred: 0
+ -> MISMATCH!
+
+annoying new trend on the internets: people picking apart michael lewis and malcolm gladwell. nobody wants to read that.
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+highly mend: by malcolm gladwell
+ - Gold: 1
+ - Pred: 1
+
+blink by malcolm gladwell amazing book and the tipping point!
+ - Gold: 1
+ - Pred: 1
+
+malcolm gladwell might be my new man crush
+ - Gold: 1
+ - Pred: 1
+
+omg. mercials alone on espn are going to drive me nuts.
+ - Gold: 0
+ - Pred: 0
+
+playing with twitter api sounds fun. may need to take a class or find a new friend who like to generate results with api code.
+ - Gold: 1
+ - Pred: 0
+ -> MISMATCH!
+
+hello twitter api ;)
+ - Gold: 1
+ - Pred: 1
+
+because the twitter api is slow and most client is aren not good.
+ - Gold: 0
+ - Pred: 0
+
+yahoo answers can be a butt sometimes
+ - Gold: 0
+ - Pred: 0
+
+is scrapbooking with nic =d
+ - Gold: 1
+ - Pred: 1
+
+rt : five things wolfram alpha does better (and vastly different) than google -
+ - Gold: 1
+ - Pred: 1
+
+just changed my default pic to a nike basketball cause bball is awesome!
+ - Gold: 1
+ - Pred: 1
+
+rt : was just told that nike layoffs started today :-(
+ - Gold: 0
+ - Pred: 0
+
+back when i worked for nike we had one fav word : just do it! :)
+ - Gold: 1
+ - Pred: 0
+ -> MISMATCH!
+
+by the way, i am totally inspired by this freaky mercial:
+ - Gold: 1
+ - Pred: 1
+
+class. the 50d is supposed e today :)
+ - Gold: 1
+ - Pred: 1
+
+needs someone to explain lambda calculus to him! :(
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+took the graduate field exam puter science today. nothing makes you feel like more of an idiot than lambda calculus.
+ - Gold: 0
+ - Pred: 0
+
+shout outs to all east palo alto for being in the buildin karizmakaze 50cal gta! also thanks to profits of doom universal hempz cracka.
+ - Gold: 1
+ - Pred: 0
+ -> MISMATCH!
+
+yeahhhhhhhhh, i wouldn not really have lived in east palo alto if i could have avoided it. i guess it is only for the summer.
+ - Gold: 0
+ - Pred: 0
+
+great stanford course. thanks for making it available to the public! really helpful and informative for starting off!
+ - Gold: 1
+ - Pred: 1
+
+@ work til 6pm. lets go lakers!
+ - Gold: 1
+ - Pred: 1
+
+damn you north korea.
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+can we just go ahead and blow north korea off the map already!
+ - Gold: 0
+ - Pred: 0
+
+north korea, please cease this douchebaggery. china doesn not even like you anymore.
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+why the hell is pelosi in freakin china! and on whose dime!
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+are you burning more cash $$$ than chrysler and gm! stop the financial tsunami. where "bailout" means taking a handout!
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+insects have infected my spinach plant :(
+ - Gold: 0
+ - Pred: 0
+
+wish i could catch every mosquito in the world n burn em slowly.they been bitin the shit outta me 2day.mosquitos are the assholes of insects
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+just got back from church, and i totally hate insects.
+ - Gold: 0
+ - Pred: 0
+
+just got mcdonalds goddam those eggs make me sick. o yeah laker up date go lakers. not much of an update! well it is true so suck it
+ - Gold: 0
+ - Pred: 0
+
+omgg i ohhdee want mcdonalds damn i wonder if its open lol =]
+ - Gold: 1
+ - Pred: 0
+ -> MISMATCH!
+
+history exam studying ugh
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+i hate revision, it is so boring! i am totally unprepared for my exam tomorrow :( things are not looking good.
+ - Gold: 0
+ - Pred: 0
+
+higher physics exam tommorow, not lookin forward to it much :(
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+it is a bank holiday, yet i am only out of work now. exam season sucks:(
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+cheney and bush are the real culprits -
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+life!s a bitch! and so is dick cheney. #p2 #bipart #tlot #tcot #hhrs #gop #dnc
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+dick cheney is dishonest speech about torture, terror, and obama. -fred kaplan slate.
+ - Gold: 0
+ - Pred: 0
+
+"the republican party is a bunch of anti-abortion zealots who couldn not draw flies to a dump." -- neal boortz (just now, on the radio)
+ - Gold: 0
+ - Pred: 0
+
+is twitter is connections api broken! some tweets didn not make it to twitter.
+ - Gold: 0
+ - Pred: 0
+
+i srsly hate the stupid twitter api timeout thing, soooo annoying! :(
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+i really liked is "learning jquery" book. is worth a look too
+ - Gold: 1
+ - Pred: 1
+
+very interesting ad from adobe by goodby, silverstein & partners - youtube - adobe cs4: le sens propre
+ - Gold: 1
+ - Pred: 1
+
+goodby silverstein agency new site! / great!
+ - Gold: 1
+ - Pred: 1
+
+rt goodby, silverstein is new site: / i enjoy it. *nice find!*
+ - Gold: 1
+ - Pred: 1
+
+the ever amazing psyop and goodby silverstein & partners for hp! have to go play with after effects now!
+ - Gold: 1
+ - Pred: 1
+
+top ten most watched on viral-video chart. love the nike #mostvaluablepuppets campaign from wieden & kennedy
+ - Gold: 1
+ - Pred: 1
+
+zomg! i have a g2!
+ - Gold: 1
+ - Pred: 1
+
+ok so lots of buzz from io2009 but how lucky are they - a free g2!
+ - Gold: 1
+ - Pred: 1
+
+just got a free g2 android at google i/o!
+ - Gold: 1
+ - Pred: 0
+ -> MISMATCH!
+
+guess i will be retiring my g1 and start using my developer g2 woot #googleio
+ - Gold: 1
+ - Pred: 1
+
+i am happy for philip being at googleio today
+ - Gold: 1
+ - Pred: 1
+
+lakers played great! cannot wait for thursday night lakers vs. !
+ - Gold: 1
+ - Pred: 1
+
+judd apatow creates fake on to market his new movie. viral marketing at its best.
+ - Gold: 1
+ - Pred: 1
+
+viral marketing fail. this acia pills brand oughta get shut down for hacking into people is messenger is. i get 5-6 msgs in a day! arrrgh!
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+watching night at the museum . lmao
+ - Gold: 1
+ - Pred: 1
+
+i loved night at the museum!
+ - Gold: 1
+ - Pred: 1
+
+just got back from the movies. went to see the new night at the museum with rachel. it was good
+ - Gold: 1
+ - Pred: 1
+
+i will take you on a date to see night at the museum 2 whenever you want.it looks soooooo good
+ - Gold: 1
+ - Pred: 1
+
+no watching the night at the museum. getting really good
+ - Gold: 1
+ - Pred: 1
+
+night at the museum, wolverine and junk food - perfect monday!
+ - Gold: 1
+ - Pred: 1
+
+saw night at the museum 2 last night. pretty crazy movie. but the cast was awesome so it was well worth it. robin williams forever!
+ - Gold: 1
+ - Pred: 1
+
+night at the museum tonite instead of up. :( oh well. that 4 yr old better enjoy it. lol
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+it is unfortunate that after the stimulus plan was put in place twice to help gm on the back of the american people has led to the inevitable
+ - Gold: 0
+ - Pred: 0
+
+tell me again why we are giving more $$ to gm! we should use that $ for all the programs that support the unemployed.
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+oh yes but if gm dies it will only be worth more boo hahaha
+ - Gold: 0
+ - Pred: 0
+
+time warner cable is down again 3rd time since memorial day bummer!
+ - Gold: 0
+ - Pred: 0
+
+i would rather pay reasonable yearly taxes for "free" fast internet, than get gouged by time warner for a slow connection.
+ - Gold: 0
+ - Pred: 0
+
+nooooooo my dvr just died and i was only half way through the ea presser. hate you time warner
+ - Gold: 0
+ - Pred: 0
+
+f*ck time warner cable! you f*cking suck balls! i have a $700 hd tv & my damn hd channels hardly e in. bullshit!
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+time warner has the worse customer service ever. i will never use them again
+ - Gold: 0
+ - Pred: 0
+
+time warner is the devil. worst possible time for the internet to go out.
+ - Gold: 0
+ - Pred: 0
+
+fuck no internet damn time warner!
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+time warner really picks the worst time to not work. all i want to do is get to so i can watch the hills. wtfffff.
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+i hate time warner! soooo wish i had vios. cant watch the fricken mets game w/o buffering. i feel like im watching free internet porn.
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+ahh.got rid of stupid time warner today & now taking a nap while the roomies cook for me. pretty good end for a monday :)
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+time warner is hd line up is crap.
+ - Gold: 0
+ - Pred: 0
+
+is being fucked by time warner cable. didnt know modems could explode. and susan boyle sucks too!
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+time warner cable slogan: where calling it a day at 2pm happens.
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+recovering from surgery.wishing was here :(
+ - Gold: 0
+ - Pred: 0
+
+my wrist still hurts. i have to get it looked at. i hate the dr/dentist/scary places. :( time to watch eagle eye. if you want to join, txt!
+ - Gold: 1
+ - Pred: 1
+
+the dentist lied! " u won not feel any ort! prob won not even need pain pills" man u twippin this shit hurt! how many pills can i take!
+ - Gold: 0
+ - Pred: 0
+
+my dentist is great but she is expensive.=(
+ - Gold: 0
+ - Pred: 0
+
+is studing math ;) tomorrow exam and dentist :)
+ - Gold: 1
+ - Pred: 1
+
+my dentist was wrong. wrong
+ - Gold: 0
+ - Pred: 0
+
+going to the dentist later.:|
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+son has me looking at cars online. i hate car shopping. would rather go to the dentist! anyone with a good car at a good price to sell!
+ - Gold: 0
+ - Pred: 0
+
+luke and i got stopped walking out of safeway and asked to empty our pockets and lift our shirts. how jacked up is that!
+ - Gold: 0
+ - Pred: 0
+
+safeway is very rock n roll tonight
+ - Gold: 1
+ - Pred: 1
+
+the safeway bathroom still smells like ass!
+ - Gold: 0
+ - Pred: 0
+
+at safeway on elkhorn, they move like they are dead!
+ - Gold: 0
+ - Pred: 0
+
+i love dwight howard is vitamin mercial. now i wish he was with nike and not adidas. lol.
+ - Gold: 1
+ - Pred: 0
+ -> MISMATCH!
+
+found nothing at nike factory :/ off to banana republic outlet!
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+is lovin his nike already and that is only from running on the spot in his bedroom
+ - Gold: 1
+ - Pred: 0
+ -> MISMATCH!
+
+i finally got around to using jquery to make my bio collapse. yay for slide animations.
+ - Gold: 1
+ - Pred: 0
+ -> MISMATCH!
+
+right! lol we will get there! i have high expectations, warren buffet style.
+ - Gold: 1
+ - Pred: 1
+
+rt : rt great "someone is sitting in the shade today because someone planted a tree a long time ago."- warren buffet
+ - Gold: 1
+ - Pred: 0
+ -> MISMATCH!
+
+warren buffet became (for a time) the richest man in the united states, not by working but investing in 1 big idea which lead to the fortune
+ - Gold: 1
+ - Pred: 1
+
+according to the create a school, notre dame will have 7 receivers in ncaa 10 at 84 or higher rating :) *sweet*
+ - Gold: 1
+ - Pred: 1
+
+it is definitely under warranty & my experience is the amazon support for kindle is great! had to contact them about my kindle2
+ - Gold: 1
+ - Pred: 0
+ -> MISMATCH!
+
+time warner road runner customer support here absolutely blows. i hate not having other high-speed net options. i am ready to go nuclear.
+ - Gold: 0
+ - Pred: 0
+
+time warner cable phone reps r dumber than nails! ugh! cable was working 10 mins ago now its not wtf!
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+we tried but time warner wasn not being nice so we recorded today. :)
+ - Gold: 0
+ - Pred: 0
+
+omg - time warner f'ed up my internet install - instead of today its now next saturday - another week w/o internet! &$*ehfa^v9fhg[*# fml.
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+wth.i have never seen a line this loooong at time warner before, ugh.
+ - Gold: 0
+ - Pred: 0
+
+impatiently awaiting the arrival of the time warner guy. it is way too pretty to be inside all afternoon
+ - Gold: 0
+ - Pred: 0
+
+naive bayes using em for text classification. really frustrating.
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+we went to stanford university today. got a tour. made me want to go back to college. it is also decided all of our kids will go there.
+ - Gold: 1
+ - Pred: 0
+ -> MISMATCH!
+
+if you are being harassed by calls about your car warranty, changing your number won not fix that. they call every number. #d-bags
+ - Gold: 0
+ - Pred: 0
+
+just blocked united blood services using google voice. they call more than those car warranty guys.
+ - Gold: 0
+ - Pred: 0
+
+#at&t plete fail.
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+oh snap you work at at&t don not you
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+i really dont want at&t phone service.they suck when es to having a signal
+ - Gold: 0
+ - Pred: 0
+
+i say we just cut out the small talk: at&t is new slogan: f__k you, give us your money. (apologies to bob geldof.)
+ - Gold: 0
+ - Pred: 0
+
+pissed about at&t is mid-contract upgrade price for the iphone (it is $200 more) i am not going to pay $499 for something i thought was $299
+ - Gold: 0
+ - Pred: 0
+
+safari 4 is fast :) even on my shitty at&t tethering.
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+what is at&t fucking up!
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+at&t dropped the ball and isn not supporting crap with the new iphone 3.0. fail #att sucks!
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+yay, glad you got the phone! still, damn you, at&t.
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+talk is cheap: bing that, i!ll stick with google.
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+wtf is the point of deleting tweets if they can still be found in summize and searches! twitter, please fix that. thanks and bye
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+i love google translator too ! :d good day mate !
+ - Gold: 1
+ - Pred: 1
+
+reading on my new kindle2!
+ - Gold: 1
+ - Pred: 1
+
+my kindle2 came and i love it! :)
+ - Gold: 1
+ - Pred: 0
+ -> MISMATCH!
+
+loving my new kindle2. named her kendra in case u were wondering. the "cookbook" is the tool cuz it tells u all the tricks! best gift evr!
+ - Gold: 1
+ - Pred: 1
+
+the real aig scandal /
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+obama is quite a edian! check out his dinner speech on cnn :) very funny jokes.
+ - Gold: 1
+ - Pred: 1
+
+' barack obama shows his funny side " >> ! great speech.
+ - Gold: 1
+ - Pred: 1
+
+i like this guy : ' barack obama shows his funny side " >> !
+ - Gold: 1
+ - Pred: 1
+
+obama is speech was pretty awesome last night!
+ - Gold: 1
+ - Pred: 1
+
+reading "bill clinton fail - obama win!"
+ - Gold: 1
+ - Pred: 1
+
+obama more popular than u.s. among arabs: survey: president barack obama is popularity in leading arab countries .
+ - Gold: 1
+ - Pred: 1
+
+obama is got jokes! haha just got to watch a bit of his after dinner speech from last night. i am in love with mr. president ;)
+ - Gold: 1
+ - Pred: 1
+
+lebron james got in a car accident i guess.just heard it on evening news.wow i cant believe it.will he be ok !
+ - Gold: 0
+ - Pred: 0
+
+is it me or is this the best the playoffs have been in years oh yea lebron and melo in the finals
+ - Gold: 1
+ - Pred: 1
+
+no, lebron is the best
+ - Gold: 1
+ - Pred: 1
+
+lebron is cool. i like his personality.he has good character.
+ - Gold: 1
+ - Pred: 1
+
+watching lebron highlights. damn that niggas good
+ - Gold: 1
+ - Pred: 1
+
+lebron is murdering shit.
+ - Gold: 1
+ - Pred: 0
+ -> MISMATCH!
+
+lebron is a monsta and he is only 24. smh the world ain not ready.
+ - Gold: 1
+ - Pred: 0
+ -> MISMATCH!
+
+when lebron is done in the nba he will probably be greater than kobe. like u said kobe is good but there alot of 'good' players.
+ - Gold: 1
+ - Pred: 0
+ -> MISMATCH!
+
+kobe is good bt lebron has my vote
+ - Gold: 1
+ - Pred: 0
+ -> MISMATCH!
+
+kobe is the best in the world not lebron .
+ - Gold: 0
+ - Pred: 0
+
+world cup 2010 access! damn, that is a good look!
+ - Gold: 1
+ - Pred: 1
+
+just bought my tickets for the 2010 fifa world cup in south africa. its going to be a great summer.
+ - Gold: 1
+ - Pred: 1
+
+i have to go to booz allen hamilton for a 2hr meeting :( but then i get to go home :)
+ - Gold: 0
+ - Pred: 0
+
+the great indian tamasha truly will unfold from may 16, the result day for indian general election.
+ - Gold: 1
+ - Pred: 1
+
+i have the kindle2. i have seen pictures of the dx, but haven not seen it in person. i love my kindle - i am on it everyday.
+ - Gold: 1
+ - Pred: 0
+ -> MISMATCH!
+
+such an awesome idea - the continual learning program with a kindle2
+ - Gold: 1
+ - Pred: 1
+
+ooooh, what model are you getting! i have the 40d and love love love love it!
+ - Gold: 1
+ - Pred: 1
+
+the times of india: the wonder that is india is election.
+ - Gold: 1
+ - Pred: 1
+
+good video from google on using search options.
+ - Gold: 1
+ - Pred: 1
+
+lol. ah my skin is itchy :( damn lawnmowing.
+ - Gold: 0
+ - Pred: 0
+
+itchy back! dont ya hate it!
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+stanford charity fashion show a top draw
+ - Gold: 1
+ - Pred: 1
+
+stanford university!s facebook profile is one of the most popular official university pages -
+ - Gold: 1
+ - Pred: 1
+
+lyx is cool.
+ - Gold: 1
+ - Pred: 1
+
+sooo dissapointed they sent danny gokey home. you still rock .danny . my hometown hero ! yeah milrockee!
+ - Gold: 1
+ - Pred: 1
+
+rt 'american idol' fashion: adam lambert tones down, danny gokey cute .
+ - Gold: 1
+ - Pred: 1
+
+i love you danny gokey! :)
+ - Gold: 1
+ - Pred: 1
+
+so tired. i didn not sleep well at all last night.
+ - Gold: 0
+ - Pred: 0
+
+boarding plane for san francisco in 1 hour; 6 hr flight. blech.
+ - Gold: 0
+ - Pred: 0
+
+bonjour san francisco. my back hurts from last night.
+ - Gold: 0
+ - Pred: 0
+
+with my best girl for a few more hours in san francisco. mmmmmfamily is wonderful!
+ - Gold: 1
+ - Pred: 1
+
+f*** up big, or go home - aig
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+went to see the star trek movie last night. very satisfying.
+ - Gold: 1
+ - Pred: 1
+
+i can not wait, going to see star trek tonight!
+ - Gold: 1
+ - Pred: 0
+ -> MISMATCH!
+
+star trek was as good as everyone said!
+ - Gold: 1
+ - Pred: 0
+ -> MISMATCH!
+
+am loving new malcolm gladwell book - outliers
+ - Gold: 1
+ - Pred: 1
+
+i highly mend malcolm gladwell is 'the tipping point.' my next audiobook will probably be one of his as well.
+ - Gold: 1
+ - Pred: 1
+
+malcolm gladwell is a genius at tricking people into not realizing he is a fucking idiot
+ - Gold: 0
+ - Pred: 0
+
+hey no offense but malcolm gladwell is a pretenious, annoying cunt and he brings you down. cant read his shit
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+rt : great article by malcolm gladwell.
+ - Gold: 1
+ - Pred: 1
+
+i seriously underestimated malcolm gladwell. i want to meet this dude.
+ - Gold: 1
+ - Pred: 0
+ -> MISMATCH!
+
+i cast right now. everything is down cable internet & phone.ughh what am i to do
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+comcast sucks.
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+the day i never have to deal cast again will rank as one of the best days of my life.
+ - Gold: 0
+ - Pred: 0
+
+cast fail again!
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+curses the twitter api limit
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+now i can see why dave winer screams about lack of twitter api, its limitations and access throttles!
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+arg. twitter api is making me crazy.
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+i am really loving the new search site wolfram/alpha. makes google seem so . quaint. /
+ - Gold: 1
+ - Pred: 1
+
+#wolfram alpha sucks! even for researchers the information provided is less than you can get from #google or #wikipedia, totally useless!
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+off to the nike factory!
+ - Gold: 1
+ - Pred: 1
+
+new nike mercials are pretty cute. why do we live together again!
+ - Gold: 1
+ - Pred: 1
+
+oh those are awesome! i so wish they weren not owned by nike :(
+ - Gold: 0
+ - Pred: 0
+
+- awesome! seeing the show friday at the shoreline amphitheatre. never seen nin before. can not wait. .
+ - Gold: 1
+ - Pred: 0
+ -> MISMATCH!
+
+arhh, it is weka bug. = =" and i spent almost two hours to find that out. crappy me
+ - Gold: 0
+ - Pred: 0
+
+hey bud :) np i do so love my 50d, although i would love a 5d mkii more
+ - Gold: 1
+ - Pred: 1
+
+just got us a 50d for the office. :d
+ - Gold: 1
+ - Pred: 1
+
+just picked up my new canon 50d.it is beautiful! prepare for some seriously awesome photography!
+ - Gold: 1
+ - Pred: 1
+
+just got my new toy. canon 50d. love love love it!
+ - Gold: 1
+ - Pred: 1
+
+learning about lambda calculus :)
+ - Gold: 1
+ - Pred: 1
+
+i am moving to east palo alto!
+ - Gold: 1
+ - Pred: 0
+ -> MISMATCH!
+
+@ atebits i just finished watching your stanford iphone class session. i really appreciate it. you rock!
+ - Gold: 1
+ - Pred: 1
+
+hi! just saw your stanford talk and really liked your advice. just saying hi from singapore (yes the videos do get around)
+ - Gold: 1
+ - Pred: 0
+ -> MISMATCH!
+
+lakers tonight let is go!
+ - Gold: 1
+ - Pred: 0
+ -> MISMATCH!
+
+will the lakers kick the nuggets ass tonight!
+ - Gold: 1
+ - Pred: 1
+
+oooooooh. north korea is in troubleeeee!
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+wat the heck is north korea doing!!! they just conducted powerful nuclear tests! follow the link:
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+listening to obama. friggin north korea.
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+i just realized we three monkeys in the white obama.biden,pelosi . sarah palin 2012
+ - Gold: 0
+ - Pred: 0
+
+pelosi should stay in china and e back.
+ - Gold: 0
+ - Pred: 0
+
+nancy pelosi gave the mencement speech i have ever heard. yes i am still bitter about this
+ - Gold: 0
+ - Pred: 0
+
+ugh. the amount of times these stupid insects have bitten me. grr.
+ - Gold: 0
+ - Pred: 0
+
+prettiest insects ever - pink katydids:
+ - Gold: 1
+ - Pred: 0
+ -> MISMATCH!
+
+just got barraged by a horde of insects hungry for my kitchen light. so scary.
+ - Gold: 0
+ - Pred: 0
+
+just had mcdonalds for dinner. :d it was goooood. big mac meal. ;)
+ - Gold: 1
+ - Pred: 1
+
+ahh yes lol ima tell my hubby to go get me sum mcdonalds =]
+ - Gold: 1
+ - Pred: 0
+ -> MISMATCH!
+
+stopped to have lunch at mcdonalds. chicken nuggetssss! :) yummmmmy.
+ - Gold: 1
+ - Pred: 1
+
+could go for a lot of mcdonalds. i mean a lot.
+ - Gold: 1
+ - Pred: 0
+ -> MISMATCH!
+
+my exam went good. : your prayers worked (:
+ - Gold: 1
+ - Pred: 0
+ -> MISMATCH!
+
+only one exam left, and i am so happy for it :d
+ - Gold: 1
+ - Pred: 0
+ -> MISMATCH!
+
+math review. im going to fail the exam.
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+colin powell rocked yesterday on cbs. cheney needs to shut the hell up and go home.powell is a man of honor and served our country proudly
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+obviously not siding with cheney here:
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+absolutely hilarious! from :
+ - Gold: 1
+ - Pred: 1
+
+i never did thank you for including me in your top 100 twitter authors! you rock! (& i new wave :-d)
+ - Gold: 1
+ - Pred: 1
+
+rt : awesome jquery reference book for coda! / #webdesign
+ - Gold: 1
+ - Pred: 1
+
+i have been sending e-mails like crazy today to my contacts.does anyone have a contact at goodby silverstein.i would love to speak to them
+ - Gold: 1
+ - Pred: 0
+ -> MISMATCH!
+
+goodby, silverstein is new site. / i enjoy it.
+ - Gold: 1
+ - Pred: 1
+
+wow everyone at the google i/o conference got free g2 is with a month of unlimited service
+ - Gold: 1
+ - Pred: 1
+
+dood i got a free google android phone at the i/o conference. the g2!
+ - Gold: 1
+ - Pred: 1
+
+the g2 is amazing btw, a huge improvement over the g1
+ - Gold: 1
+ - Pred: 1
+
+html 5 demos! lots of great stuff e! yes, i am excited. :) #io2009 #googleio
+ - Gold: 1
+ - Pred: 1
+
+- yay! happy place! place place! i love google!
+ - Gold: 1
+ - Pred: 1
+
+#googleio | o3d - bringing 3d graphics to the browser. very nice tbh. funfun.
+ - Gold: 1
+ - Pred: 1
+
+awesome viral marketing for "funny people" -teach/
+ - Gold: 1
+ - Pred: 1
+
+saw night at the museum out of sheer desperation. who is funding these movies!
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+night at the museum 2! pretty furkin good.
+ - Gold: 1
+ - Pred: 1
+
+watching night at the museum - giggling.
+ - Gold: 1
+ - Pred: 1
+
+back from seeing istar trek' and 'night at the museum.' istar trek' was amazing, but 'night at the museum' was; eh.
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+just watched night at the museum 2! so stinkin cute!
+ - Gold: 1
+ - Pred: 1
+
+so, night at the museum 2 was awesome! much better than part 1. next weekend we will see up.
+ - Gold: 1
+ - Pred: 0
+ -> MISMATCH!
+
+saw the new night at the museum and i loved it. next is to go see up in 3d
+ - Gold: 1
+ - Pred: 0
+ -> MISMATCH!
+
+it is a shame about gm. what if they are forced to make only cars the white house thinks will sell! what do you think!
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+as u may have noticed, not too happy about the gm situation, nor aig, lehman, et al
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+$gm good riddance. sad though.
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+i will never buy a government motors vehicle: until just recently, i drove gm cars. since 1988, when i bought a .
+ - Gold: 0
+ - Pred: 0
+
+having the old coca-cola guy on the gm board is stupid has heck! #tcot #ala
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+#rantsandraves the worst thing about gm (concord / pleasant hill / martinez): is the fucking uaw. .
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+give a man a fish, u feed him for the day. teach him to fish, u feed him for life. buy him gm, and u f**k him over for good.
+ - Gold: 0
+ - Pred: 0
+
+the more i hear about this gm thing the more angry i get. billions wasted, more bullshit. all for something like 40k employees and all the.
+ - Gold: 0
+ - Pred: 0
+
+i own a gm car and it is junk as far as pared to a honda
+ - Gold: 0
+ - Pred: 0
+
+sad day.bankrupt gm
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+is upset about the whole gm thing. life as i know it is so screwed up
+ - Gold: 0
+ - Pred: 0
+
+whoever is running time warner needs to be repeatedly raped by a rhino so they understand the consequences of putting out shitty cable svcs
+ - Gold: 0
+ - Pred: 0
+
+#wftb joining a bit late. my connection was down (boo time warner)
+ - Gold: 0
+ - Pred: 0
+
+cox or time warner! cox is cheaper and gets a b on dslreports. tw is more expensive and gets a c.
+ - Gold: 0
+ - Pred: 0
+
+i am furious with time warner and their phone promotions!
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+just got home from chick-fil-a with the boys. damn my internets down =( stupid time warner
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+could time-warner cable suck more! no.
+ - Gold: 0
+ - Pred: 0
+
+pissed at time warner for causin me to have slow internet problems
+ - Gold: 0
+ - Pred: 0
+
+ummm, having some time warner problems!
+ - Gold: 0
+ - Pred: 0
+
+you guys see this! why does time warner have to suck so much ass! really wish i could get u-verse at my apartment.
+ - Gold: 0
+ - Pred: 0
+
+rt the upside to time warner: unhelpful phone operators superslow on-site service. crap, that is not an upside.
+ - Gold: 0
+ - Pred: 0
+
+rt : new time warner slogan: "time warner, where we make you long for the days before cable."
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+confirmed: it is time warner is fault, not facebook is, that fb is taking about 3 minutes to load. so tempted to switch to verizon =/
+ - Gold: 0
+ - Pred: 0
+
+time warner = epic fail
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+i know. how sad is that! rt : 1st day of hurricane season. that is less scarey than govt taking over gm.
+ - Gold: 0
+ - Pred: 0
+
+gm files bankruptcy, not a good sign.
+ - Gold: 0
+ - Pred: 0
+
+yankees won mets lost. its a good day.
+ - Gold: 1
+ - Pred: 1
+
+my dentist appt today was actually quite enjoyable.
+ - Gold: 1
+ - Pred: 0
+ -> MISMATCH!
+
+i hate the effing dentist.
+ - Gold: 0
+ - Pred: 0
+
+i hate going to the dentist. !
+ - Gold: 0
+ - Pred: 0
+
+i hate the dentist.who invented them anyways!
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+this dentist is office is cold :/
+ - Gold: 0
+ - Pred: 0
+
+just applied at safeway!(: yeeeee!
+ - Gold: 1
+ - Pred: 1
+
+@ safeway. place is a nightmare right now. bumming.
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+hate safeway select green tea icecream! bought two cartons, what a waste of money. >_<
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+nike rocks. i am super grateful for what i have done with them :) & the european division of nike is beyond!
+ - Gold: 1
+ - Pred: 1
+
+have you tried nike ! v. addictive.
+ - Gold: 1
+ - Pred: 0
+ -> MISMATCH!
+
+the nike training club (beta) iphone app looks very interesting.
+ - Gold: 1
+ - Pred: 1
+
+argghhhh why won not my jquery appear in safari bad safari !
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+i am ready to drop the pretenses, i am forever in love with jquery, and i want to marry it. sorry ladies, this nerd is jquery.spokenfor.js
+ - Gold: 1
+ - Pred: 1
+
+super investors: a great weekend read here from warren buffet. oldie, but a goodie.
+ - Gold: 1
+ - Pred: 1
+
+reading michael palin book, the python years.great book. i also mend warren buffet & nelson mandela is bio
+ - Gold: 1
+ - Pred: 1
+
+i mean, i am down with notre dame if i have to. it is a good school, i would be closer to dan, i would enjoy it.
+ - Gold: 1
+ - Pred: 0
+ -> MISMATCH!
+
+i can not watch tv without a tivo. and after all these years, the time/warner dvr still sucks.
+ - Gold: 0
+ - Pred: 0
+
+i would say some sports writers are idiots for saying roger federer is one of the best ever in tennis. roger federer is the best ever in tennis
+ - Gold: 1
+ - Pred: 1
+
+i still love my kindle2 but reading the new york times on it does not feel natural. i miss the bloomingdale ads.
+ - Gold: 0
+ - Pred: 0
+
+i love my kindle2. no more stacks of books to trip over on the way to the loo.
+ - Gold: 1
+ - Pred: 0
+ -> MISMATCH!
+
+although today is keynote rocked, for every great announcement, at&t shit on us just a little bit more.
+ - Gold: 0
+ - Pred: 0
+
+- its not so much my obsession with cell phones, but the iphone! i am a slave to at&t forever because of it. :)
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+fuzzball is more fun than at&t ;p -/twitter
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+today is a good day to dislike at&t. vote out of office indeed,
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+got my wave sandbox invite! extra excited! too bad i have class now. but i will play with it soon enough! #io2009 #wave
+ - Gold: 1
+ - Pred: 1
+
+looks like summize has gone down. too many tweets from wwdc perhaps!
+ - Gold: 0
+ - Pred: 0
+
+thanks so much! .from one of your *very* happy kindle2 winners ; ) i was so surprised, fabulous. thank you! best, kathleen
+ - Gold: 1
+ - Pred: 1
+
+man i kinda dislike apple right now. case in point: the iphone 3gs. wish there was a video recorder app. please!
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+i have a kindle2 (& sony prs-500). like it! physical device feels good. font is nice. pg turns are snappy enuf. ui a little klunky.
+ - Gold: 1
+ - Pred: 1
+
+the #kindle2 seems the best ereader, but will it work in the uk and where can i get one!
+ - Gold: 1
+ - Pred: 1
+
+i have a google addiction. thank you for pointing that out, . hahaha.
+ - Gold: 1
+ - Pred: 0
+ -> MISMATCH!
+
+dearest , you rich bastards! the visa card you sent me doesn not work. why screw a little guy like me!
+ - Gold: 0
+ - Pred: 0
+
+excited about seeing bobby flay and guy fieri tomorrow at the great american food & music fest!
+ - Gold: 1
+ - Pred: 1
+
+gonna go see bobby flay 2moro at shoreline. eat and drink. gonna be good.
+ - Gold: 1
+ - Pred: 1
+
+can not wait for the great american food and music festival at shoreline tomorrow. mmm.katz pastrami and bobby flay. yes please.
+ - Gold: 1
+ - Pred: 1
+
+my dad was in ny for a day, we ate at mesa grill last night and met bobby flay. so much fun, except pletely lost my voice today.
+ - Gold: 1
+ - Pred: 0
+ -> MISMATCH!
+
+fighting with latex. again.
+ - Gold: 0
+ - Pred: 0
+
+we love you too and don not want you to die! latex = the devil
+ - Gold: 0
+ - Pred: 0
+
+7 hours. 7 hours of inkscape crashing, normally solid as a rock. 7 hours of plaining at the slightest thing. i can not take any more.
+ - Gold: 0
+ - Pred: 0
+
+shit is hitting the fan in iran.craziness indeed #iranelection
+ - Gold: 0
+ - Pred: 0
+
+monday already. iran may implode. kitchen is a disaster. seems happy. had a nice weekend and is great. whoop.
+ - Gold: 0
+ - Pred: 1
+ -> MISMATCH!
+
+getting ready to test out some burger receipes this weekend. bobby flay has some great receipes to try. thanks bobby.
+ - Gold: 1
+ - Pred: 1
+
+i lam so in love with bobby flay. he is my favorite. rt : you need a place in phoenix. we have great peppers here!
+ - Gold: 1
+ - Pred: 1
+
+i just created my first latex file from scratch. that didn not work out very well. (see , it is a great time waster)
+ - Gold: 0
+ - Pred: 0
+
+using linux and loving it - so much nicer than windows. looking forward to using the wysiwyg latex editor!
+ - Gold: 1
+ - Pred: 1
+
+after using latex a lot, any other typeset mathematics just looks hideous.
+ - Gold: 1
+ - Pred: 0
+ -> MISMATCH!
+
+on that note, i hate word. i hate pages. i hate latex. there, i said it. i hate latex. all you texn3rds e kill me now.
+ - Gold: 0
+ - Pred: 0
+
+ahhh. back in a *real* text editing environment. i <3 latex.
+ - Gold: 1
+ - Pred: 1
+
+trouble in iran, i see. hmm. iran. iran so far away. #flockofseagullsweregeopoliticallycorrect
+ - Gold: 0
+ - Pred: 0
+
+reading the ing out of iran. the whole thing is terrifying and incredibly sad.
+ - Gold: 0
+ - Pred: 0
+

models/flair-sentiment-classifier/training.log

@@ -0,0 +1,644 @@
+2023-02-06 08:28:45,031 ----------------------------------------------------------------------------------------------------
+2023-02-06 08:28:45,036 Model: "TextClassifier(
+  (decoder): Linear(in_features=512, out_features=3, bias=True)
+  (dropout): Dropout(p=0.0, inplace=False)
+  (locked_dropout): LockedDropout(p=0.0)
+  (word_dropout): WordDropout(p=0.0)
+  (loss_function): CrossEntropyLoss()
+  (document_embeddings): DocumentLSTMEmbeddings(
+    (embeddings): StackedEmbeddings(
+      (list_embedding_0): FlairEmbeddings(
+        (lm): LanguageModel(
+          (drop): Dropout(p=0.25, inplace=False)
+          (encoder): Embedding(275, 100)
+          (rnn): LSTM(100, 1024)
+          (decoder): Linear(in_features=1024, out_features=275, bias=True)
+        )
+      )
+    )
+    (word_reprojection_map): Linear(in_features=1024, out_features=256, bias=True)
+    (rnn): GRU(256, 512)
+    (dropout): Dropout(p=0.5, inplace=False)
+  )
+  (weights): None
+  (weight_tensor) None
+)"
+2023-02-06 08:28:45,039 ----------------------------------------------------------------------------------------------------
+2023-02-06 08:28:45,042 Corpus: "Corpus: 8500 train + 1500 dev + 359 test sentences"
+2023-02-06 08:28:45,045 ----------------------------------------------------------------------------------------------------
+2023-02-06 08:28:45,048 Parameters:
+2023-02-06 08:28:45,051  - learning_rate: "0.010000"
+2023-02-06 08:28:45,052  - mini_batch_size: "64"
+2023-02-06 08:28:45,056  - patience: "3"
+2023-02-06 08:28:45,057  - anneal_factor: "0.5"
+2023-02-06 08:28:45,061  - max_epochs: "35"
+2023-02-06 08:28:45,063  - shuffle: "True"
+2023-02-06 08:28:45,069  - train_with_dev: "False"
+2023-02-06 08:28:45,071  - batch_growth_annealing: "False"
+2023-02-06 08:28:45,075 ----------------------------------------------------------------------------------------------------
+2023-02-06 08:28:45,078 Model training base path: "/content/drive/MyDrive/Colab Notebooks/models/flair-sentiment-classifier"
+2023-02-06 08:28:45,081 ----------------------------------------------------------------------------------------------------
+2023-02-06 08:28:45,083 Device: cuda:0
+2023-02-06 08:28:45,085 ----------------------------------------------------------------------------------------------------
+2023-02-06 08:28:45,089 Embeddings storage mode: gpu
+2023-02-06 08:28:45,091 ----------------------------------------------------------------------------------------------------
+2023-02-06 08:28:46,882 epoch 1 - iter 13/133 - loss 0.01562834 - samples/sec: 514.51 - lr: 0.010000
+2023-02-06 08:28:48,397 epoch 1 - iter 26/133 - loss 0.01481466 - samples/sec: 748.73 - lr: 0.010000
+2023-02-06 08:28:49,638 epoch 1 - iter 39/133 - loss 0.01423043 - samples/sec: 772.85 - lr: 0.010000
+2023-02-06 08:28:51,189 epoch 1 - iter 52/133 - loss 0.01381341 - samples/sec: 600.83 - lr: 0.010000
+2023-02-06 08:28:52,402 epoch 1 - iter 65/133 - loss 0.01349711 - samples/sec: 796.47 - lr: 0.010000
+2023-02-06 08:28:53,877 epoch 1 - iter 78/133 - loss 0.01323276 - samples/sec: 765.73 - lr: 0.010000
+2023-02-06 08:28:55,144 epoch 1 - iter 91/133 - loss 0.01301969 - samples/sec: 759.35 - lr: 0.010000
+2023-02-06 08:28:56,401 epoch 1 - iter 104/133 - loss 0.01283645 - samples/sec: 765.85 - lr: 0.010000
+2023-02-06 08:28:57,895 epoch 1 - iter 117/133 - loss 0.01267868 - samples/sec: 760.35 - lr: 0.010000
+2023-02-06 08:28:59,155 epoch 1 - iter 130/133 - loss 0.01254156 - samples/sec: 766.70 - lr: 0.010000
+2023-02-06 08:28:59,435 ----------------------------------------------------------------------------------------------------
+2023-02-06 08:28:59,440 EPOCH 1 done: loss 0.0125 - lr 0.010000
+2023-02-06 08:29:02,345 Evaluating as a multi-label problem: False
+2023-02-06 08:29:02,360 DEV : loss 0.01149754598736763 - f1-score (micro avg)  0.5393
+2023-02-06 08:29:02,938 BAD EPOCHS (no improvement): 0
+2023-02-06 08:29:02,944 saving best model
+2023-02-06 08:29:03,019 ----------------------------------------------------------------------------------------------------
+2023-02-06 08:29:04,350 epoch 2 - iter 13/133 - loss 0.01134125 - samples/sec: 713.12 - lr: 0.010000
+2023-02-06 08:29:05,867 epoch 2 - iter 26/133 - loss 0.01133783 - samples/sec: 745.31 - lr: 0.010000
+2023-02-06 08:29:07,096 epoch 2 - iter 39/133 - loss 0.01130036 - samples/sec: 785.18 - lr: 0.010000
+2023-02-06 08:29:08,398 epoch 2 - iter 52/133 - loss 0.01126930 - samples/sec: 739.05 - lr: 0.010000
+2023-02-06 08:29:10,210 epoch 2 - iter 65/133 - loss 0.01123679 - samples/sec: 611.49 - lr: 0.010000
+2023-02-06 08:29:12,013 epoch 2 - iter 78/133 - loss 0.01119918 - samples/sec: 563.65 - lr: 0.010000
+2023-02-06 08:29:13,567 epoch 2 - iter 91/133 - loss 0.01119660 - samples/sec: 722.13 - lr: 0.010000
+2023-02-06 08:29:14,863 epoch 2 - iter 104/133 - loss 0.01118887 - samples/sec: 739.97 - lr: 0.010000
+2023-02-06 08:29:16,413 epoch 2 - iter 117/133 - loss 0.01117338 - samples/sec: 736.63 - lr: 0.010000
+2023-02-06 08:29:17,679 epoch 2 - iter 130/133 - loss 0.01115977 - samples/sec: 754.47 - lr: 0.010000
+2023-02-06 08:29:17,964 ----------------------------------------------------------------------------------------------------
+2023-02-06 08:29:17,967 EPOCH 2 done: loss 0.0112 - lr 0.010000
+2023-02-06 08:29:20,384 Evaluating as a multi-label problem: False
+2023-02-06 08:29:20,400 DEV : loss 0.011094754561781883 - f1-score (micro avg)  0.586
+2023-02-06 08:29:20,760 BAD EPOCHS (no improvement): 0
+2023-02-06 08:29:20,767 saving best model
+2023-02-06 08:29:20,836 ----------------------------------------------------------------------------------------------------
+2023-02-06 08:29:22,455 epoch 3 - iter 13/133 - loss 0.01100920 - samples/sec: 577.11 - lr: 0.010000
+2023-02-06 08:29:23,739 epoch 3 - iter 26/133 - loss 0.01100166 - samples/sec: 748.26 - lr: 0.010000
+2023-02-06 08:29:25,256 epoch 3 - iter 39/133 - loss 0.01099206 - samples/sec: 614.87 - lr: 0.010000
+2023-02-06 08:29:26,543 epoch 3 - iter 52/133 - loss 0.01096206 - samples/sec: 747.35 - lr: 0.010000
+2023-02-06 08:29:28,045 epoch 3 - iter 65/133 - loss 0.01093502 - samples/sec: 756.93 - lr: 0.010000
+2023-02-06 08:29:29,300 epoch 3 - iter 78/133 - loss 0.01092986 - samples/sec: 767.76 - lr: 0.010000
+2023-02-06 08:29:30,899 epoch 3 - iter 91/133 - loss 0.01094036 - samples/sec: 700.02 - lr: 0.010000
+2023-02-06 08:29:32,167 epoch 3 - iter 104/133 - loss 0.01093898 - samples/sec: 759.88 - lr: 0.010000
+2023-02-06 08:29:33,482 epoch 3 - iter 117/133 - loss 0.01092654 - samples/sec: 724.74 - lr: 0.010000
+2023-02-06 08:29:34,996 epoch 3 - iter 130/133 - loss 0.01091851 - samples/sec: 747.17 - lr: 0.010000
+2023-02-06 08:29:35,285 ----------------------------------------------------------------------------------------------------
+2023-02-06 08:29:35,288 EPOCH 3 done: loss 0.0109 - lr 0.010000
+2023-02-06 08:29:37,771 Evaluating as a multi-label problem: False
+2023-02-06 08:29:37,787 DEV : loss 0.010945815593004227 - f1-score (micro avg)  0.5833
+2023-02-06 08:29:38,166 BAD EPOCHS (no improvement): 1
+2023-02-06 08:29:38,171 ----------------------------------------------------------------------------------------------------
+2023-02-06 08:29:39,771 epoch 4 - iter 13/133 - loss 0.01088269 - samples/sec: 703.41 - lr: 0.010000
+2023-02-06 08:29:41,093 epoch 4 - iter 26/133 - loss 0.01079631 - samples/sec: 727.27 - lr: 0.010000
+2023-02-06 08:29:42,388 epoch 4 - iter 39/133 - loss 0.01080397 - samples/sec: 731.61 - lr: 0.010000
+2023-02-06 08:29:43,901 epoch 4 - iter 52/133 - loss 0.01079299 - samples/sec: 748.17 - lr: 0.010000
+2023-02-06 08:29:45,173 epoch 4 - iter 65/133 - loss 0.01080453 - samples/sec: 756.93 - lr: 0.010000
+2023-02-06 08:29:46,704 epoch 4 - iter 78/133 - loss 0.01078781 - samples/sec: 742.45 - lr: 0.010000
+2023-02-06 08:29:48,004 epoch 4 - iter 91/133 - loss 0.01077505 - samples/sec: 739.13 - lr: 0.010000
+2023-02-06 08:29:49,566 epoch 4 - iter 104/133 - loss 0.01077253 - samples/sec: 719.85 - lr: 0.010000
+2023-02-06 08:29:50,799 epoch 4 - iter 117/133 - loss 0.01077099 - samples/sec: 782.87 - lr: 0.010000
+2023-02-06 08:29:52,309 epoch 4 - iter 130/133 - loss 0.01079835 - samples/sec: 751.86 - lr: 0.010000
+2023-02-06 08:29:52,598 ----------------------------------------------------------------------------------------------------
+2023-02-06 08:29:52,600 EPOCH 4 done: loss 0.0108 - lr 0.010000
+2023-02-06 08:29:54,806 Evaluating as a multi-label problem: False
+2023-02-06 08:29:54,823 DEV : loss 0.010844088159501553 - f1-score (micro avg)  0.5867
+2023-02-06 08:29:55,416 BAD EPOCHS (no improvement): 0
+2023-02-06 08:29:55,422 saving best model
+2023-02-06 08:29:55,494 ----------------------------------------------------------------------------------------------------
+2023-02-06 08:29:56,823 epoch 5 - iter 13/133 - loss 0.01067723 - samples/sec: 718.01 - lr: 0.010000
+2023-02-06 08:29:58,344 epoch 5 - iter 26/133 - loss 0.01059902 - samples/sec: 743.92 - lr: 0.010000
+2023-02-06 08:29:59,632 epoch 5 - iter 39/133 - loss 0.01065991 - samples/sec: 752.37 - lr: 0.010000
+2023-02-06 08:30:01,148 epoch 5 - iter 52/133 - loss 0.01066396 - samples/sec: 742.67 - lr: 0.010000
+2023-02-06 08:30:02,462 epoch 5 - iter 65/133 - loss 0.01067246 - samples/sec: 721.77 - lr: 0.010000
+2023-02-06 08:30:03,991 epoch 5 - iter 78/133 - loss 0.01067908 - samples/sec: 764.43 - lr: 0.010000
+2023-02-06 08:30:05,279 epoch 5 - iter 91/133 - loss 0.01070105 - samples/sec: 746.56 - lr: 0.010000
+2023-02-06 08:30:06,504 epoch 5 - iter 104/133 - loss 0.01071328 - samples/sec: 788.19 - lr: 0.010000
+2023-02-06 08:30:07,981 epoch 5 - iter 117/133 - loss 0.01069141 - samples/sec: 768.06 - lr: 0.010000
+2023-02-06 08:30:09,278 epoch 5 - iter 130/133 - loss 0.01069258 - samples/sec: 736.98 - lr: 0.010000
+2023-02-06 08:30:09,584 ----------------------------------------------------------------------------------------------------
+2023-02-06 08:30:09,591 EPOCH 5 done: loss 0.0107 - lr 0.010000
+2023-02-06 08:30:12,079 Evaluating as a multi-label problem: False
+2023-02-06 08:30:12,096 DEV : loss 0.010764073580503464 - f1-score (micro avg)  0.59
+2023-02-06 08:30:12,666 BAD EPOCHS (no improvement): 0
+2023-02-06 08:30:12,684 saving best model
+2023-02-06 08:30:12,758 ----------------------------------------------------------------------------------------------------
+2023-02-06 08:30:14,153 epoch 6 - iter 13/133 - loss 0.01071924 - samples/sec: 682.06 - lr: 0.010000
+2023-02-06 08:30:15,468 epoch 6 - iter 26/133 - loss 0.01071274 - samples/sec: 726.60 - lr: 0.010000
+2023-02-06 08:30:16,995 epoch 6 - iter 39/133 - loss 0.01072073 - samples/sec: 745.06 - lr: 0.010000
+2023-02-06 08:30:18,256 epoch 6 - iter 52/133 - loss 0.01069609 - samples/sec: 766.36 - lr: 0.010000
+2023-02-06 08:30:19,760 epoch 6 - iter 65/133 - loss 0.01067637 - samples/sec: 621.63 - lr: 0.010000
+2023-02-06 08:30:21,150 epoch 6 - iter 78/133 - loss 0.01069997 - samples/sec: 698.03 - lr: 0.010000
+2023-02-06 08:30:22,666 epoch 6 - iter 91/133 - loss 0.01067124 - samples/sec: 747.15 - lr: 0.010000
+2023-02-06 08:30:23,937 epoch 6 - iter 104/133 - loss 0.01065074 - samples/sec: 756.77 - lr: 0.010000
+2023-02-06 08:30:25,196 epoch 6 - iter 117/133 - loss 0.01065426 - samples/sec: 759.07 - lr: 0.010000
+2023-02-06 08:30:26,726 epoch 6 - iter 130/133 - loss 0.01065274 - samples/sec: 739.37 - lr: 0.010000
+2023-02-06 08:30:27,011 ----------------------------------------------------------------------------------------------------
+2023-02-06 08:30:27,016 EPOCH 6 done: loss 0.0107 - lr 0.010000
+2023-02-06 08:30:29,488 Evaluating as a multi-label problem: False
+2023-02-06 08:30:29,504 DEV : loss 0.010709869675338268 - f1-score (micro avg)  0.5953
+2023-02-06 08:30:29,884 BAD EPOCHS (no improvement): 0
+2023-02-06 08:30:29,891 saving best model
+2023-02-06 08:30:29,962 ----------------------------------------------------------------------------------------------------
+2023-02-06 08:30:31,568 epoch 7 - iter 13/133 - loss 0.01074821 - samples/sec: 588.47 - lr: 0.010000
+2023-02-06 08:30:32,838 epoch 7 - iter 26/133 - loss 0.01067320 - samples/sec: 752.01 - lr: 0.010000
+2023-02-06 08:30:34,326 epoch 7 - iter 39/133 - loss 0.01067867 - samples/sec: 764.30 - lr: 0.010000
+2023-02-06 08:30:35,675 epoch 7 - iter 52/133 - loss 0.01066511 - samples/sec: 715.33 - lr: 0.010000
+2023-02-06 08:30:37,160 epoch 7 - iter 65/133 - loss 0.01066500 - samples/sec: 774.66 - lr: 0.010000
+2023-02-06 08:30:38,384 epoch 7 - iter 78/133 - loss 0.01064703 - samples/sec: 786.21 - lr: 0.010000
+2023-02-06 08:30:39,656 epoch 7 - iter 91/133 - loss 0.01064059 - samples/sec: 752.36 - lr: 0.010000
+2023-02-06 08:30:41,183 epoch 7 - iter 104/133 - loss 0.01064299 - samples/sec: 741.82 - lr: 0.010000
+2023-02-06 08:30:42,411 epoch 7 - iter 117/133 - loss 0.01063663 - samples/sec: 782.15 - lr: 0.010000
+2023-02-06 08:30:43,918 epoch 7 - iter 130/133 - loss 0.01062067 - samples/sec: 746.86 - lr: 0.010000
+2023-02-06 08:30:44,207 ----------------------------------------------------------------------------------------------------
+2023-02-06 08:30:44,212 EPOCH 7 done: loss 0.0106 - lr 0.010000
+2023-02-06 08:30:46,609 Evaluating as a multi-label problem: False
+2023-02-06 08:30:46,625 DEV : loss 0.010663843713700771 - f1-score (micro avg)  0.604
+2023-02-06 08:30:46,984 BAD EPOCHS (no improvement): 0
+2023-02-06 08:30:46,991 saving best model
+2023-02-06 08:30:47,072 ----------------------------------------------------------------------------------------------------
+2023-02-06 08:30:48,638 epoch 8 - iter 13/133 - loss 0.01043837 - samples/sec: 601.87 - lr: 0.010000
+2023-02-06 08:30:49,893 epoch 8 - iter 26/133 - loss 0.01051882 - samples/sec: 760.45 - lr: 0.010000
+2023-02-06 08:30:51,166 epoch 8 - iter 39/133 - loss 0.01054135 - samples/sec: 761.95 - lr: 0.010000
+2023-02-06 08:30:52,624 epoch 8 - iter 52/133 - loss 0.01056330 - samples/sec: 784.40 - lr: 0.010000
+2023-02-06 08:30:53,864 epoch 8 - iter 65/133 - loss 0.01058674 - samples/sec: 788.25 - lr: 0.010000
+2023-02-06 08:30:55,348 epoch 8 - iter 78/133 - loss 0.01057453 - samples/sec: 632.25 - lr: 0.010000
+2023-02-06 08:30:56,643 epoch 8 - iter 91/133 - loss 0.01056818 - samples/sec: 743.01 - lr: 0.010000
+2023-02-06 08:30:58,165 epoch 8 - iter 104/133 - loss 0.01056378 - samples/sec: 754.99 - lr: 0.010000
+2023-02-06 08:30:59,473 epoch 8 - iter 117/133 - loss 0.01055726 - samples/sec: 740.74 - lr: 0.010000
+2023-02-06 08:31:00,790 epoch 8 - iter 130/133 - loss 0.01054234 - samples/sec: 730.40 - lr: 0.010000
+2023-02-06 08:31:01,299 ----------------------------------------------------------------------------------------------------
+2023-02-06 08:31:01,301 EPOCH 8 done: loss 0.0106 - lr 0.010000
+2023-02-06 08:31:03,556 Evaluating as a multi-label problem: False
+2023-02-06 08:31:03,573 DEV : loss 0.010619796812534332 - f1-score (micro avg)  0.602
+2023-02-06 08:31:04,148 BAD EPOCHS (no improvement): 1
+2023-02-06 08:31:04,153 ----------------------------------------------------------------------------------------------------
+2023-02-06 08:31:05,493 epoch 9 - iter 13/133 - loss 0.01055787 - samples/sec: 714.38 - lr: 0.010000
+2023-02-06 08:31:06,991 epoch 9 - iter 26/133 - loss 0.01055880 - samples/sec: 756.84 - lr: 0.010000
+2023-02-06 08:31:08,220 epoch 9 - iter 39/133 - loss 0.01058857 - samples/sec: 784.11 - lr: 0.010000
+2023-02-06 08:31:09,733 epoch 9 - iter 52/133 - loss 0.01050204 - samples/sec: 743.77 - lr: 0.010000
+2023-02-06 08:31:11,006 epoch 9 - iter 65/133 - loss 0.01049462 - samples/sec: 757.49 - lr: 0.010000
+2023-02-06 08:31:12,320 epoch 9 - iter 78/133 - loss 0.01049927 - samples/sec: 726.38 - lr: 0.010000
+2023-02-06 08:31:13,836 epoch 9 - iter 91/133 - loss 0.01053675 - samples/sec: 753.53 - lr: 0.010000
+2023-02-06 08:31:15,093 epoch 9 - iter 104/133 - loss 0.01051643 - samples/sec: 763.35 - lr: 0.010000
+2023-02-06 08:31:16,571 epoch 9 - iter 117/133 - loss 0.01051333 - samples/sec: 770.43 - lr: 0.010000
+2023-02-06 08:31:17,840 epoch 9 - iter 130/133 - loss 0.01052863 - samples/sec: 764.73 - lr: 0.010000
+2023-02-06 08:31:18,111 ----------------------------------------------------------------------------------------------------
+2023-02-06 08:31:18,112 EPOCH 9 done: loss 0.0105 - lr 0.010000
+2023-02-06 08:31:20,513 Evaluating as a multi-label problem: False
+2023-02-06 08:31:20,529 DEV : loss 0.010611701756715775 - f1-score (micro avg)  0.6127
+2023-02-06 08:31:21,099 BAD EPOCHS (no improvement): 0
+2023-02-06 08:31:21,105 saving best model
+2023-02-06 08:31:21,180 ----------------------------------------------------------------------------------------------------
+2023-02-06 08:31:22,488 epoch 10 - iter 13/133 - loss 0.01068018 - samples/sec: 735.19 - lr: 0.010000
+2023-02-06 08:31:23,814 epoch 10 - iter 26/133 - loss 0.01048881 - samples/sec: 719.40 - lr: 0.010000
+2023-02-06 08:31:25,348 epoch 10 - iter 39/133 - loss 0.01057341 - samples/sec: 607.48 - lr: 0.010000
+2023-02-06 08:31:26,612 epoch 10 - iter 52/133 - loss 0.01053287 - samples/sec: 762.66 - lr: 0.010000
+2023-02-06 08:31:28,080 epoch 10 - iter 65/133 - loss 0.01053009 - samples/sec: 780.80 - lr: 0.010000
+2023-02-06 08:31:29,348 epoch 10 - iter 78/133 - loss 0.01049424 - samples/sec: 765.03 - lr: 0.010000
+2023-02-06 08:31:30,907 epoch 10 - iter 91/133 - loss 0.01049714 - samples/sec: 602.01 - lr: 0.010000
+2023-02-06 08:31:32,299 epoch 10 - iter 104/133 - loss 0.01051002 - samples/sec: 688.23 - lr: 0.010000
+2023-02-06 08:31:33,585 epoch 10 - iter 117/133 - loss 0.01050991 - samples/sec: 751.61 - lr: 0.010000
+2023-02-06 08:31:35,136 epoch 10 - iter 130/133 - loss 0.01049037 - samples/sec: 721.95 - lr: 0.010000
+2023-02-06 08:31:35,429 ----------------------------------------------------------------------------------------------------
+2023-02-06 08:31:35,431 EPOCH 10 done: loss 0.0105 - lr 0.010000
+2023-02-06 08:31:37,895 Evaluating as a multi-label problem: False
+2023-02-06 08:31:37,910 DEV : loss 0.010555021464824677 - f1-score (micro avg)  0.612
+2023-02-06 08:31:38,250 BAD EPOCHS (no improvement): 1
+2023-02-06 08:31:38,266 ----------------------------------------------------------------------------------------------------
+2023-02-06 08:31:39,815 epoch 11 - iter 13/133 - loss 0.01014661 - samples/sec: 717.23 - lr: 0.010000
+2023-02-06 08:31:41,095 epoch 11 - iter 26/133 - loss 0.01037157 - samples/sec: 747.91 - lr: 0.010000
+2023-02-06 08:31:42,350 epoch 11 - iter 39/133 - loss 0.01039031 - samples/sec: 771.90 - lr: 0.010000
+2023-02-06 08:31:43,850 epoch 11 - iter 52/133 - loss 0.01048393 - samples/sec: 763.72 - lr: 0.010000
+2023-02-06 08:31:45,153 epoch 11 - iter 65/133 - loss 0.01050528 - samples/sec: 737.89 - lr: 0.010000
+2023-02-06 08:31:46,661 epoch 11 - iter 78/133 - loss 0.01048936 - samples/sec: 752.50 - lr: 0.010000
+2023-02-06 08:31:47,936 epoch 11 - iter 91/133 - loss 0.01046032 - samples/sec: 756.43 - lr: 0.010000
+2023-02-06 08:31:49,443 epoch 11 - iter 104/133 - loss 0.01049842 - samples/sec: 750.46 - lr: 0.010000
+2023-02-06 08:31:50,667 epoch 11 - iter 117/133 - loss 0.01048207 - samples/sec: 788.09 - lr: 0.010000
+2023-02-06 08:31:51,915 epoch 11 - iter 130/133 - loss 0.01047867 - samples/sec: 764.34 - lr: 0.010000
+2023-02-06 08:31:52,443 ----------------------------------------------------------------------------------------------------
+2023-02-06 08:31:52,447 EPOCH 11 done: loss 0.0105 - lr 0.010000
+2023-02-06 08:31:54,642 Evaluating as a multi-label problem: False
+2023-02-06 08:31:54,659 DEV : loss 0.010583124123513699 - f1-score (micro avg)  0.618
+2023-02-06 08:31:55,235 BAD EPOCHS (no improvement): 0
+2023-02-06 08:31:55,243 saving best model
+2023-02-06 08:31:55,324 ----------------------------------------------------------------------------------------------------
+2023-02-06 08:31:56,599 epoch 12 - iter 13/133 - loss 0.01034765 - samples/sec: 758.67 - lr: 0.010000
+2023-02-06 08:31:58,049 epoch 12 - iter 26/133 - loss 0.01038689 - samples/sec: 649.83 - lr: 0.010000
+2023-02-06 08:31:59,293 epoch 12 - iter 39/133 - loss 0.01041491 - samples/sec: 771.48 - lr: 0.010000
+2023-02-06 08:32:00,827 epoch 12 - iter 52/133 - loss 0.01039636 - samples/sec: 749.22 - lr: 0.010000
+2023-02-06 08:32:02,125 epoch 12 - iter 65/133 - loss 0.01039765 - samples/sec: 734.58 - lr: 0.010000
+2023-02-06 08:32:03,651 epoch 12 - iter 78/133 - loss 0.01037797 - samples/sec: 742.60 - lr: 0.010000
+2023-02-06 08:32:04,912 epoch 12 - iter 91/133 - loss 0.01037713 - samples/sec: 767.06 - lr: 0.010000
+2023-02-06 08:32:06,155 epoch 12 - iter 104/133 - loss 0.01039333 - samples/sec: 778.01 - lr: 0.010000
+2023-02-06 08:32:07,679 epoch 12 - iter 117/133 - loss 0.01039785 - samples/sec: 612.76 - lr: 0.010000
+2023-02-06 08:32:08,958 epoch 12 - iter 130/133 - loss 0.01041191 - samples/sec: 749.49 - lr: 0.010000
+2023-02-06 08:32:09,221 ----------------------------------------------------------------------------------------------------
+2023-02-06 08:32:09,225 EPOCH 12 done: loss 0.0104 - lr 0.010000
+2023-02-06 08:32:11,743 Evaluating as a multi-label problem: False
+2023-02-06 08:32:11,761 DEV : loss 0.010490193963050842 - f1-score (micro avg)  0.6233
+2023-02-06 08:32:12,343 BAD EPOCHS (no improvement): 0
+2023-02-06 08:32:12,347 saving best model
+2023-02-06 08:32:12,425 ----------------------------------------------------------------------------------------------------
+2023-02-06 08:32:13,768 epoch 13 - iter 13/133 - loss 0.01044893 - samples/sec: 711.51 - lr: 0.010000
+2023-02-06 08:32:15,331 epoch 13 - iter 26/133 - loss 0.01037701 - samples/sec: 597.05 - lr: 0.010000
+2023-02-06 08:32:16,636 epoch 13 - iter 39/133 - loss 0.01041042 - samples/sec: 729.64 - lr: 0.010000
+2023-02-06 08:32:18,179 epoch 13 - iter 52/133 - loss 0.01039558 - samples/sec: 639.59 - lr: 0.010000
+2023-02-06 08:32:20,357 epoch 13 - iter 65/133 - loss 0.01036643 - samples/sec: 453.47 - lr: 0.010000
+2023-02-06 08:32:22,076 epoch 13 - iter 78/133 - loss 0.01034448 - samples/sec: 576.82 - lr: 0.010000
+2023-02-06 08:32:23,544 epoch 13 - iter 91/133 - loss 0.01037494 - samples/sec: 776.14 - lr: 0.010000
+2023-02-06 08:32:24,857 epoch 13 - iter 104/133 - loss 0.01038988 - samples/sec: 723.98 - lr: 0.010000
+2023-02-06 08:32:26,302 epoch 13 - iter 117/133 - loss 0.01037204 - samples/sec: 803.02 - lr: 0.010000
+2023-02-06 08:32:27,579 epoch 13 - iter 130/133 - loss 0.01037025 - samples/sec: 756.57 - lr: 0.010000
+2023-02-06 08:32:27,857 ----------------------------------------------------------------------------------------------------
+2023-02-06 08:32:27,858 EPOCH 13 done: loss 0.0104 - lr 0.010000
+2023-02-06 08:32:30,369 Evaluating as a multi-label problem: False
+2023-02-06 08:32:30,385 DEV : loss 0.010483094491064548 - f1-score (micro avg)  0.6207
+2023-02-06 08:32:30,744 BAD EPOCHS (no improvement): 1
+2023-02-06 08:32:30,752 ----------------------------------------------------------------------------------------------------
+2023-02-06 08:32:32,325 epoch 14 - iter 13/133 - loss 0.01026158 - samples/sec: 717.41 - lr: 0.010000
+2023-02-06 08:32:33,586 epoch 14 - iter 26/133 - loss 0.01037027 - samples/sec: 762.17 - lr: 0.010000
+2023-02-06 08:32:35,070 epoch 14 - iter 39/133 - loss 0.01036379 - samples/sec: 774.03 - lr: 0.010000
+2023-02-06 08:32:36,383 epoch 14 - iter 52/133 - loss 0.01035813 - samples/sec: 729.67 - lr: 0.010000
+2023-02-06 08:32:37,889 epoch 14 - iter 65/133 - loss 0.01031572 - samples/sec: 748.65 - lr: 0.010000
+2023-02-06 08:32:39,182 epoch 14 - iter 78/133 - loss 0.01034433 - samples/sec: 743.14 - lr: 0.010000
+2023-02-06 08:32:40,475 epoch 14 - iter 91/133 - loss 0.01037816 - samples/sec: 742.25 - lr: 0.010000
+2023-02-06 08:32:41,954 epoch 14 - iter 104/133 - loss 0.01039795 - samples/sec: 782.25 - lr: 0.010000
+2023-02-06 08:32:43,225 epoch 14 - iter 117/133 - loss 0.01038837 - samples/sec: 761.71 - lr: 0.010000
+2023-02-06 08:32:44,737 epoch 14 - iter 130/133 - loss 0.01036175 - samples/sec: 745.28 - lr: 0.010000
+2023-02-06 08:32:45,035 ----------------------------------------------------------------------------------------------------
+2023-02-06 08:32:45,040 EPOCH 14 done: loss 0.0104 - lr 0.010000
+2023-02-06 08:32:47,510 Evaluating as a multi-label problem: False
+2023-02-06 08:32:47,528 DEV : loss 0.010432829149067402 - f1-score (micro avg)  0.626
+2023-02-06 08:32:47,863 BAD EPOCHS (no improvement): 0
+2023-02-06 08:32:47,871 saving best model
+2023-02-06 08:32:47,944 ----------------------------------------------------------------------------------------------------
+2023-02-06 08:32:49,537 epoch 15 - iter 13/133 - loss 0.01019386 - samples/sec: 698.75 - lr: 0.010000
+2023-02-06 08:32:50,800 epoch 15 - iter 26/133 - loss 0.01019158 - samples/sec: 758.25 - lr: 0.010000
+2023-02-06 08:32:52,065 epoch 15 - iter 39/133 - loss 0.01015538 - samples/sec: 759.68 - lr: 0.010000
+2023-02-06 08:32:53,509 epoch 15 - iter 52/133 - loss 0.01023823 - samples/sec: 804.56 - lr: 0.010000
+2023-02-06 08:32:54,834 epoch 15 - iter 65/133 - loss 0.01026660 - samples/sec: 722.21 - lr: 0.010000
+2023-02-06 08:32:56,442 epoch 15 - iter 78/133 - loss 0.01025501 - samples/sec: 717.72 - lr: 0.010000
+2023-02-06 08:32:57,718 epoch 15 - iter 91/133 - loss 0.01024956 - samples/sec: 756.16 - lr: 0.010000
+2023-02-06 08:32:59,173 epoch 15 - iter 104/133 - loss 0.01028768 - samples/sec: 783.99 - lr: 0.010000
+2023-02-06 08:33:00,444 epoch 15 - iter 117/133 - loss 0.01027921 - samples/sec: 758.72 - lr: 0.010000
+2023-02-06 08:33:01,708 epoch 15 - iter 130/133 - loss 0.01030246 - samples/sec: 760.06 - lr: 0.010000
+2023-02-06 08:33:02,242 ----------------------------------------------------------------------------------------------------
+2023-02-06 08:33:02,248 EPOCH 15 done: loss 0.0103 - lr 0.010000
+2023-02-06 08:33:04,549 Evaluating as a multi-label problem: False
+2023-02-06 08:33:04,568 DEV : loss 0.010397534817457199 - f1-score (micro avg)  0.6247
+2023-02-06 08:33:05,171 BAD EPOCHS (no improvement): 1
+2023-02-06 08:33:05,181 ----------------------------------------------------------------------------------------------------
+2023-02-06 08:33:06,547 epoch 16 - iter 13/133 - loss 0.01041342 - samples/sec: 696.13 - lr: 0.010000
+2023-02-06 08:33:08,089 epoch 16 - iter 26/133 - loss 0.01036971 - samples/sec: 726.77 - lr: 0.010000
+2023-02-06 08:33:09,375 epoch 16 - iter 39/133 - loss 0.01029897 - samples/sec: 745.27 - lr: 0.010000
+2023-02-06 08:33:10,662 epoch 16 - iter 52/133 - loss 0.01030162 - samples/sec: 746.88 - lr: 0.010000
+2023-02-06 08:33:12,125 epoch 16 - iter 65/133 - loss 0.01031824 - samples/sec: 780.89 - lr: 0.010000
+2023-02-06 08:33:13,387 epoch 16 - iter 78/133 - loss 0.01031728 - samples/sec: 754.65 - lr: 0.010000
+2023-02-06 08:33:14,895 epoch 16 - iter 91/133 - loss 0.01027123 - samples/sec: 757.20 - lr: 0.010000
+2023-02-06 08:33:16,162 epoch 16 - iter 104/133 - loss 0.01027054 - samples/sec: 764.32 - lr: 0.010000
+2023-02-06 08:33:17,639 epoch 16 - iter 117/133 - loss 0.01024219 - samples/sec: 633.84 - lr: 0.010000
+2023-02-06 08:33:18,899 epoch 16 - iter 130/133 - loss 0.01026610 - samples/sec: 758.90 - lr: 0.010000
+2023-02-06 08:33:19,184 ----------------------------------------------------------------------------------------------------
+2023-02-06 08:33:19,188 EPOCH 16 done: loss 0.0103 - lr 0.010000
+2023-02-06 08:33:21,632 Evaluating as a multi-label problem: False
+2023-02-06 08:33:21,649 DEV : loss 0.010406638495624065 - f1-score (micro avg)  0.6253
+2023-02-06 08:33:22,222 BAD EPOCHS (no improvement): 2
+2023-02-06 08:33:22,234 ----------------------------------------------------------------------------------------------------
+2023-02-06 08:33:23,528 epoch 17 - iter 13/133 - loss 0.01033387 - samples/sec: 738.38 - lr: 0.010000
+2023-02-06 08:33:24,760 epoch 17 - iter 26/133 - loss 0.01031095 - samples/sec: 781.92 - lr: 0.010000
+2023-02-06 08:33:26,300 epoch 17 - iter 39/133 - loss 0.01028919 - samples/sec: 746.79 - lr: 0.010000
+2023-02-06 08:33:27,590 epoch 17 - iter 52/133 - loss 0.01030368 - samples/sec: 739.39 - lr: 0.010000
+2023-02-06 08:33:29,070 epoch 17 - iter 65/133 - loss 0.01029647 - samples/sec: 634.36 - lr: 0.010000
+2023-02-06 08:33:30,298 epoch 17 - iter 78/133 - loss 0.01029278 - samples/sec: 782.85 - lr: 0.010000
+2023-02-06 08:33:31,598 epoch 17 - iter 91/133 - loss 0.01028871 - samples/sec: 734.99 - lr: 0.010000
+2023-02-06 08:33:33,082 epoch 17 - iter 104/133 - loss 0.01029995 - samples/sec: 762.60 - lr: 0.010000
+2023-02-06 08:33:34,331 epoch 17 - iter 117/133 - loss 0.01028484 - samples/sec: 776.27 - lr: 0.010000
+2023-02-06 08:33:35,784 epoch 17 - iter 130/133 - loss 0.01027576 - samples/sec: 785.80 - lr: 0.010000
+2023-02-06 08:33:36,070 ----------------------------------------------------------------------------------------------------
+2023-02-06 08:33:36,071 EPOCH 17 done: loss 0.0103 - lr 0.010000
+2023-02-06 08:33:38,498 Evaluating as a multi-label problem: False
+2023-02-06 08:33:38,515 DEV : loss 0.01034807600080967 - f1-score (micro avg)  0.63
+2023-02-06 08:33:38,856 BAD EPOCHS (no improvement): 0
+2023-02-06 08:33:38,862 saving best model
+2023-02-06 08:33:38,939 ----------------------------------------------------------------------------------------------------
+2023-02-06 08:33:40,462 epoch 18 - iter 13/133 - loss 0.01017348 - samples/sec: 740.64 - lr: 0.010000
+2023-02-06 08:33:41,723 epoch 18 - iter 26/133 - loss 0.01019914 - samples/sec: 764.13 - lr: 0.010000
+2023-02-06 08:33:43,017 epoch 18 - iter 39/133 - loss 0.01021330 - samples/sec: 737.55 - lr: 0.010000
+2023-02-06 08:33:44,471 epoch 18 - iter 52/133 - loss 0.01018504 - samples/sec: 782.18 - lr: 0.010000
+2023-02-06 08:33:45,771 epoch 18 - iter 65/133 - loss 0.01017944 - samples/sec: 740.07 - lr: 0.010000
+2023-02-06 08:33:47,306 epoch 18 - iter 78/133 - loss 0.01020170 - samples/sec: 734.11 - lr: 0.010000
+2023-02-06 08:33:48,590 epoch 18 - iter 91/133 - loss 0.01021375 - samples/sec: 743.50 - lr: 0.010000
+2023-02-06 08:33:50,106 epoch 18 - iter 104/133 - loss 0.01019670 - samples/sec: 745.19 - lr: 0.010000
+2023-02-06 08:33:51,371 epoch 18 - iter 117/133 - loss 0.01023334 - samples/sec: 762.98 - lr: 0.010000
+2023-02-06 08:33:52,871 epoch 18 - iter 130/133 - loss 0.01022335 - samples/sec: 764.73 - lr: 0.010000
+2023-02-06 08:33:53,154 ----------------------------------------------------------------------------------------------------
+2023-02-06 08:33:53,156 EPOCH 18 done: loss 0.0102 - lr 0.010000
+2023-02-06 08:33:55,611 Evaluating as a multi-label problem: False
+2023-02-06 08:33:55,629 DEV : loss 0.010320308618247509 - f1-score (micro avg)  0.6313
+2023-02-06 08:33:55,977 BAD EPOCHS (no improvement): 0
+2023-02-06 08:33:55,987 saving best model
+2023-02-06 08:33:56,065 ----------------------------------------------------------------------------------------------------
+2023-02-06 08:33:57,370 epoch 19 - iter 13/133 - loss 0.01029091 - samples/sec: 738.06 - lr: 0.010000
+2023-02-06 08:33:58,890 epoch 19 - iter 26/133 - loss 0.01023014 - samples/sec: 750.92 - lr: 0.010000
+2023-02-06 08:34:00,150 epoch 19 - iter 39/133 - loss 0.01020864 - samples/sec: 762.09 - lr: 0.010000
+2023-02-06 08:34:01,681 epoch 19 - iter 52/133 - loss 0.01024439 - samples/sec: 741.73 - lr: 0.010000
+2023-02-06 08:34:02,906 epoch 19 - iter 65/133 - loss 0.01023181 - samples/sec: 790.97 - lr: 0.010000
+2023-02-06 08:34:04,433 epoch 19 - iter 78/133 - loss 0.01022412 - samples/sec: 613.31 - lr: 0.010000
+2023-02-06 08:34:05,780 epoch 19 - iter 91/133 - loss 0.01019693 - samples/sec: 712.09 - lr: 0.010000
+2023-02-06 08:34:07,187 epoch 19 - iter 104/133 - loss 0.01022081 - samples/sec: 673.66 - lr: 0.010000
+2023-02-06 08:34:08,668 epoch 19 - iter 117/133 - loss 0.01022890 - samples/sec: 785.83 - lr: 0.010000
+2023-02-06 08:34:09,910 epoch 19 - iter 130/133 - loss 0.01020723 - samples/sec: 777.53 - lr: 0.010000
+2023-02-06 08:34:10,200 ----------------------------------------------------------------------------------------------------
+2023-02-06 08:34:10,207 EPOCH 19 done: loss 0.0102 - lr 0.010000
+2023-02-06 08:34:12,679 Evaluating as a multi-label problem: False
+2023-02-06 08:34:12,700 DEV : loss 0.010296817868947983 - f1-score (micro avg)  0.64
+2023-02-06 08:34:13,311 BAD EPOCHS (no improvement): 0
+2023-02-06 08:34:13,316 saving best model
+2023-02-06 08:34:13,390 ----------------------------------------------------------------------------------------------------
+2023-02-06 08:34:14,758 epoch 20 - iter 13/133 - loss 0.01021864 - samples/sec: 703.40 - lr: 0.010000
+2023-02-06 08:34:16,077 epoch 20 - iter 26/133 - loss 0.01028711 - samples/sec: 725.84 - lr: 0.010000
+2023-02-06 08:34:17,634 epoch 20 - iter 39/133 - loss 0.01022971 - samples/sec: 745.04 - lr: 0.010000
+2023-02-06 08:34:18,921 epoch 20 - iter 52/133 - loss 0.01026002 - samples/sec: 750.90 - lr: 0.010000
+2023-02-06 08:34:20,467 epoch 20 - iter 65/133 - loss 0.01026057 - samples/sec: 602.55 - lr: 0.010000
+2023-02-06 08:34:21,753 epoch 20 - iter 78/133 - loss 0.01026928 - samples/sec: 751.33 - lr: 0.010000
+2023-02-06 08:34:23,292 epoch 20 - iter 91/133 - loss 0.01025076 - samples/sec: 736.16 - lr: 0.010000
+2023-02-06 08:34:24,579 epoch 20 - iter 104/133 - loss 0.01020640 - samples/sec: 746.82 - lr: 0.010000
+2023-02-06 08:34:25,909 epoch 20 - iter 117/133 - loss 0.01019171 - samples/sec: 719.60 - lr: 0.010000
+2023-02-06 08:34:27,438 epoch 20 - iter 130/133 - loss 0.01017225 - samples/sec: 738.30 - lr: 0.010000
+2023-02-06 08:34:27,739 ----------------------------------------------------------------------------------------------------
+2023-02-06 08:34:27,744 EPOCH 20 done: loss 0.0102 - lr 0.010000
+2023-02-06 08:34:30,257 Evaluating as a multi-label problem: False
+2023-02-06 08:34:30,274 DEV : loss 0.010265583172440529 - f1-score (micro avg)  0.6413
+2023-02-06 08:34:30,654 BAD EPOCHS (no improvement): 0
+2023-02-06 08:34:30,660 saving best model
+2023-02-06 08:34:30,739 ----------------------------------------------------------------------------------------------------
+2023-02-06 08:34:32,330 epoch 21 - iter 13/133 - loss 0.01014521 - samples/sec: 710.15 - lr: 0.010000
+2023-02-06 08:34:33,625 epoch 21 - iter 26/133 - loss 0.01018544 - samples/sec: 743.43 - lr: 0.010000
+2023-02-06 08:34:35,128 epoch 21 - iter 39/133 - loss 0.01014949 - samples/sec: 755.29 - lr: 0.010000
+2023-02-06 08:34:36,388 epoch 21 - iter 52/133 - loss 0.01015701 - samples/sec: 758.49 - lr: 0.010000
+2023-02-06 08:34:37,625 epoch 21 - iter 65/133 - loss 0.01014343 - samples/sec: 781.03 - lr: 0.010000
+2023-02-06 08:34:39,127 epoch 21 - iter 78/133 - loss 0.01011384 - samples/sec: 758.01 - lr: 0.010000
+2023-02-06 08:34:40,496 epoch 21 - iter 91/133 - loss 0.01012422 - samples/sec: 690.35 - lr: 0.010000
+2023-02-06 08:34:42,023 epoch 21 - iter 104/133 - loss 0.01014283 - samples/sec: 761.00 - lr: 0.010000
+2023-02-06 08:34:43,304 epoch 21 - iter 117/133 - loss 0.01013442 - samples/sec: 748.07 - lr: 0.010000
+2023-02-06 08:34:44,869 epoch 21 - iter 130/133 - loss 0.01012862 - samples/sec: 730.48 - lr: 0.010000
+2023-02-06 08:34:45,169 ----------------------------------------------------------------------------------------------------
+2023-02-06 08:34:45,175 EPOCH 21 done: loss 0.0101 - lr 0.010000
+2023-02-06 08:34:47,720 Evaluating as a multi-label problem: False
+2023-02-06 08:34:47,743 DEV : loss 0.010291438549757004 - f1-score (micro avg)  0.6373
+2023-02-06 08:34:48,104 BAD EPOCHS (no improvement): 1
+2023-02-06 08:34:48,124 ----------------------------------------------------------------------------------------------------
+2023-02-06 08:34:49,559 epoch 22 - iter 13/133 - loss 0.00996709 - samples/sec: 657.64 - lr: 0.010000
+2023-02-06 08:34:51,095 epoch 22 - iter 26/133 - loss 0.01001206 - samples/sec: 739.55 - lr: 0.010000
+2023-02-06 08:34:52,347 epoch 22 - iter 39/133 - loss 0.01004049 - samples/sec: 767.48 - lr: 0.010000
+2023-02-06 08:34:53,920 epoch 22 - iter 52/133 - loss 0.01005317 - samples/sec: 722.99 - lr: 0.010000
+2023-02-06 08:34:55,196 epoch 22 - iter 65/133 - loss 0.01010477 - samples/sec: 747.76 - lr: 0.010000
+2023-02-06 08:34:56,737 epoch 22 - iter 78/133 - loss 0.01010079 - samples/sec: 608.24 - lr: 0.010000
+2023-02-06 08:34:58,059 epoch 22 - iter 91/133 - loss 0.01013500 - samples/sec: 727.48 - lr: 0.010000
+2023-02-06 08:34:59,404 epoch 22 - iter 104/133 - loss 0.01013831 - samples/sec: 712.32 - lr: 0.010000
+2023-02-06 08:35:00,950 epoch 22 - iter 117/133 - loss 0.01014236 - samples/sec: 741.47 - lr: 0.010000
+2023-02-06 08:35:02,201 epoch 22 - iter 130/133 - loss 0.01013044 - samples/sec: 762.45 - lr: 0.010000
+2023-02-06 08:35:02,483 ----------------------------------------------------------------------------------------------------
+2023-02-06 08:35:02,485 EPOCH 22 done: loss 0.0101 - lr 0.010000
+2023-02-06 08:35:04,928 Evaluating as a multi-label problem: False
+2023-02-06 08:35:04,944 DEV : loss 0.010224188677966595 - f1-score (micro avg)  0.6413
+2023-02-06 08:35:05,519 BAD EPOCHS (no improvement): 0
+2023-02-06 08:35:05,528 ----------------------------------------------------------------------------------------------------
+2023-02-06 08:35:06,856 epoch 23 - iter 13/133 - loss 0.01005563 - samples/sec: 720.28 - lr: 0.010000
+2023-02-06 08:35:08,375 epoch 23 - iter 26/133 - loss 0.01002448 - samples/sec: 616.23 - lr: 0.010000
+2023-02-06 08:35:09,661 epoch 23 - iter 39/133 - loss 0.01015809 - samples/sec: 739.97 - lr: 0.010000
+2023-02-06 08:35:10,916 epoch 23 - iter 52/133 - loss 0.01016788 - samples/sec: 764.29 - lr: 0.010000
+2023-02-06 08:35:12,458 epoch 23 - iter 65/133 - loss 0.01009484 - samples/sec: 739.26 - lr: 0.010000
+2023-02-06 08:35:13,705 epoch 23 - iter 78/133 - loss 0.01008399 - samples/sec: 770.62 - lr: 0.010000
+2023-02-06 08:35:15,187 epoch 23 - iter 91/133 - loss 0.01007382 - samples/sec: 769.82 - lr: 0.010000
+2023-02-06 08:35:16,466 epoch 23 - iter 104/133 - loss 0.01006859 - samples/sec: 747.87 - lr: 0.010000
+2023-02-06 08:35:18,033 epoch 23 - iter 117/133 - loss 0.01007952 - samples/sec: 718.89 - lr: 0.010000
+2023-02-06 08:35:19,305 epoch 23 - iter 130/133 - loss 0.01007932 - samples/sec: 752.56 - lr: 0.010000
+2023-02-06 08:35:19,591 ----------------------------------------------------------------------------------------------------
+2023-02-06 08:35:19,593 EPOCH 23 done: loss 0.0101 - lr 0.010000
+2023-02-06 08:35:22,099 Evaluating as a multi-label problem: False
+2023-02-06 08:35:22,116 DEV : loss 0.010190014727413654 - f1-score (micro avg)  0.644
+2023-02-06 08:35:22,476 BAD EPOCHS (no improvement): 0
+2023-02-06 08:35:22,484 saving best model
+2023-02-06 08:35:22,562 ----------------------------------------------------------------------------------------------------
+2023-02-06 08:35:24,159 epoch 24 - iter 13/133 - loss 0.01033764 - samples/sec: 707.79 - lr: 0.010000
+2023-02-06 08:35:25,436 epoch 24 - iter 26/133 - loss 0.01039924 - samples/sec: 753.06 - lr: 0.010000
+2023-02-06 08:35:26,953 epoch 24 - iter 39/133 - loss 0.01019226 - samples/sec: 761.33 - lr: 0.010000
+2023-02-06 08:35:28,217 epoch 24 - iter 52/133 - loss 0.01010421 - samples/sec: 759.92 - lr: 0.010000
+2023-02-06 08:35:29,503 epoch 24 - iter 65/133 - loss 0.01008049 - samples/sec: 751.64 - lr: 0.010000
+2023-02-06 08:35:31,044 epoch 24 - iter 78/133 - loss 0.01005312 - samples/sec: 735.12 - lr: 0.010000
+2023-02-06 08:35:32,319 epoch 24 - iter 91/133 - loss 0.01005299 - samples/sec: 759.51 - lr: 0.010000
+2023-02-06 08:35:33,822 epoch 24 - iter 104/133 - loss 0.01003790 - samples/sec: 760.19 - lr: 0.010000
+2023-02-06 08:35:35,097 epoch 24 - iter 117/133 - loss 0.01003322 - samples/sec: 752.83 - lr: 0.010000
+2023-02-06 08:35:36,616 epoch 24 - iter 130/133 - loss 0.01004728 - samples/sec: 748.80 - lr: 0.010000
+2023-02-06 08:35:36,902 ----------------------------------------------------------------------------------------------------
+2023-02-06 08:35:36,903 EPOCH 24 done: loss 0.0101 - lr 0.010000
+2023-02-06 08:35:39,354 Evaluating as a multi-label problem: False
+2023-02-06 08:35:39,371 DEV : loss 0.010158772580325603 - f1-score (micro avg)  0.6487
+2023-02-06 08:35:39,720 BAD EPOCHS (no improvement): 0
+2023-02-06 08:35:39,727 saving best model
+2023-02-06 08:35:39,802 ----------------------------------------------------------------------------------------------------
+2023-02-06 08:35:41,120 epoch 25 - iter 13/133 - loss 0.00981064 - samples/sec: 733.84 - lr: 0.010000
+2023-02-06 08:35:42,650 epoch 25 - iter 26/133 - loss 0.00984903 - samples/sec: 745.83 - lr: 0.010000
+2023-02-06 08:35:43,929 epoch 25 - iter 39/133 - loss 0.00991121 - samples/sec: 755.02 - lr: 0.010000
+2023-02-06 08:35:45,467 epoch 25 - iter 52/133 - loss 0.01000175 - samples/sec: 740.89 - lr: 0.010000
+2023-02-06 08:35:46,750 epoch 25 - iter 65/133 - loss 0.01000978 - samples/sec: 746.54 - lr: 0.010000
+2023-02-06 08:35:48,252 epoch 25 - iter 78/133 - loss 0.01001222 - samples/sec: 770.84 - lr: 0.010000
+2023-02-06 08:35:49,515 epoch 25 - iter 91/133 - loss 0.01000999 - samples/sec: 759.97 - lr: 0.010000
+2023-02-06 08:35:51,036 epoch 25 - iter 104/133 - loss 0.00999015 - samples/sec: 616.30 - lr: 0.010000
+2023-02-06 08:35:52,290 epoch 25 - iter 117/133 - loss 0.00998576 - samples/sec: 770.57 - lr: 0.010000
+2023-02-06 08:35:53,576 epoch 25 - iter 130/133 - loss 0.00998035 - samples/sec: 745.63 - lr: 0.010000
+2023-02-06 08:35:54,094 ----------------------------------------------------------------------------------------------------
+2023-02-06 08:35:54,096 EPOCH 25 done: loss 0.0100 - lr 0.010000
+2023-02-06 08:35:56,283 Evaluating as a multi-label problem: False
+2023-02-06 08:35:56,301 DEV : loss 0.010137598030269146 - f1-score (micro avg)  0.6507
+2023-02-06 08:35:56,866 BAD EPOCHS (no improvement): 0
+2023-02-06 08:35:56,874 saving best model
+2023-02-06 08:35:56,952 ----------------------------------------------------------------------------------------------------
+2023-02-06 08:35:58,254 epoch 26 - iter 13/133 - loss 0.00981231 - samples/sec: 744.59 - lr: 0.010000
+2023-02-06 08:35:59,716 epoch 26 - iter 26/133 - loss 0.00985499 - samples/sec: 779.93 - lr: 0.010000
+2023-02-06 08:36:00,934 epoch 26 - iter 39/133 - loss 0.00986997 - samples/sec: 794.56 - lr: 0.010000
+2023-02-06 08:36:02,450 epoch 26 - iter 52/133 - loss 0.00985057 - samples/sec: 750.90 - lr: 0.010000
+2023-02-06 08:36:03,705 epoch 26 - iter 65/133 - loss 0.00991802 - samples/sec: 770.23 - lr: 0.010000
+2023-02-06 08:36:04,983 epoch 26 - iter 78/133 - loss 0.00995249 - samples/sec: 752.52 - lr: 0.010000
+2023-02-06 08:36:06,610 epoch 26 - iter 91/133 - loss 0.00994670 - samples/sec: 695.85 - lr: 0.010000
+2023-02-06 08:36:07,854 epoch 26 - iter 104/133 - loss 0.00992897 - samples/sec: 766.60 - lr: 0.010000
+2023-02-06 08:36:09,332 epoch 26 - iter 117/133 - loss 0.00995839 - samples/sec: 771.15 - lr: 0.010000
+2023-02-06 08:36:10,585 epoch 26 - iter 130/133 - loss 0.00997863 - samples/sec: 763.77 - lr: 0.010000
+2023-02-06 08:36:10,866 ----------------------------------------------------------------------------------------------------
+2023-02-06 08:36:10,871 EPOCH 26 done: loss 0.0100 - lr 0.010000
+2023-02-06 08:36:13,272 Evaluating as a multi-label problem: False
+2023-02-06 08:36:13,289 DEV : loss 0.010154918767511845 - f1-score (micro avg)  0.6453
+2023-02-06 08:36:13,875 BAD EPOCHS (no improvement): 1
+2023-02-06 08:36:13,882 ----------------------------------------------------------------------------------------------------
+2023-02-06 08:36:15,140 epoch 27 - iter 13/133 - loss 0.00982359 - samples/sec: 758.36 - lr: 0.010000
+2023-02-06 08:36:16,393 epoch 27 - iter 26/133 - loss 0.00995263 - samples/sec: 765.88 - lr: 0.010000
+2023-02-06 08:36:17,990 epoch 27 - iter 39/133 - loss 0.00994264 - samples/sec: 724.70 - lr: 0.010000
+2023-02-06 08:36:19,734 epoch 27 - iter 52/133 - loss 0.00990056 - samples/sec: 584.51 - lr: 0.010000
+2023-02-06 08:36:21,871 epoch 27 - iter 65/133 - loss 0.00989842 - samples/sec: 547.01 - lr: 0.010000
+2023-02-06 08:36:23,233 epoch 27 - iter 78/133 - loss 0.00990904 - samples/sec: 698.41 - lr: 0.010000
+2023-02-06 08:36:24,750 epoch 27 - iter 91/133 - loss 0.00997472 - samples/sec: 741.54 - lr: 0.010000
+2023-02-06 08:36:25,989 epoch 27 - iter 104/133 - loss 0.00996257 - samples/sec: 774.43 - lr: 0.010000
+2023-02-06 08:36:27,217 epoch 27 - iter 117/133 - loss 0.00995437 - samples/sec: 781.46 - lr: 0.010000
+2023-02-06 08:36:28,665 epoch 27 - iter 130/133 - loss 0.00995591 - samples/sec: 787.10 - lr: 0.010000
+2023-02-06 08:36:28,936 ----------------------------------------------------------------------------------------------------
+2023-02-06 08:36:28,941 EPOCH 27 done: loss 0.0100 - lr 0.010000
+2023-02-06 08:36:31,339 Evaluating as a multi-label problem: False
+2023-02-06 08:36:31,357 DEV : loss 0.010085121728479862 - f1-score (micro avg)  0.6567
+2023-02-06 08:36:31,722 BAD EPOCHS (no improvement): 0
+2023-02-06 08:36:31,732 saving best model
+2023-02-06 08:36:31,827 ----------------------------------------------------------------------------------------------------
+2023-02-06 08:36:33,404 epoch 28 - iter 13/133 - loss 0.01020064 - samples/sec: 708.07 - lr: 0.010000
+2023-02-06 08:36:34,623 epoch 28 - iter 26/133 - loss 0.01013967 - samples/sec: 786.34 - lr: 0.010000
+2023-02-06 08:36:36,131 epoch 28 - iter 39/133 - loss 0.01003563 - samples/sec: 758.40 - lr: 0.010000
+2023-02-06 08:36:37,442 epoch 28 - iter 52/133 - loss 0.00998388 - samples/sec: 730.28 - lr: 0.010000
+2023-02-06 08:36:38,722 epoch 28 - iter 65/133 - loss 0.00999888 - samples/sec: 748.06 - lr: 0.010000
+2023-02-06 08:36:40,168 epoch 28 - iter 78/133 - loss 0.01003122 - samples/sec: 786.57 - lr: 0.010000
+2023-02-06 08:36:41,456 epoch 28 - iter 91/133 - loss 0.01000777 - samples/sec: 744.48 - lr: 0.010000
+2023-02-06 08:36:42,925 epoch 28 - iter 104/133 - loss 0.00998209 - samples/sec: 783.40 - lr: 0.010000
+2023-02-06 08:36:44,160 epoch 28 - iter 117/133 - loss 0.00999413 - samples/sec: 777.17 - lr: 0.010000
+2023-02-06 08:36:45,652 epoch 28 - iter 130/133 - loss 0.00996400 - samples/sec: 627.73 - lr: 0.010000
+2023-02-06 08:36:45,936 ----------------------------------------------------------------------------------------------------
+2023-02-06 08:36:45,937 EPOCH 28 done: loss 0.0100 - lr 0.010000
+2023-02-06 08:36:48,370 Evaluating as a multi-label problem: False
+2023-02-06 08:36:48,390 DEV : loss 0.010063917376101017 - f1-score (micro avg)  0.6567
+2023-02-06 08:36:48,774 BAD EPOCHS (no improvement): 0
+2023-02-06 08:36:48,783 ----------------------------------------------------------------------------------------------------
+2023-02-06 08:36:50,112 epoch 29 - iter 13/133 - loss 0.01007614 - samples/sec: 715.39 - lr: 0.010000
+2023-02-06 08:36:51,579 epoch 29 - iter 26/133 - loss 0.00997458 - samples/sec: 766.42 - lr: 0.010000
+2023-02-06 08:36:52,814 epoch 29 - iter 39/133 - loss 0.00989408 - samples/sec: 781.38 - lr: 0.010000
+2023-02-06 08:36:54,264 epoch 29 - iter 52/133 - loss 0.00989758 - samples/sec: 784.84 - lr: 0.010000
+2023-02-06 08:36:55,532 epoch 29 - iter 65/133 - loss 0.00991688 - samples/sec: 756.52 - lr: 0.010000
+2023-02-06 08:36:57,026 epoch 29 - iter 78/133 - loss 0.00991818 - samples/sec: 758.17 - lr: 0.010000
+2023-02-06 08:36:58,307 epoch 29 - iter 91/133 - loss 0.00989315 - samples/sec: 756.41 - lr: 0.010000
+2023-02-06 08:36:59,562 epoch 29 - iter 104/133 - loss 0.00987888 - samples/sec: 767.41 - lr: 0.010000
+2023-02-06 08:37:01,023 epoch 29 - iter 117/133 - loss 0.00989864 - samples/sec: 642.50 - lr: 0.010000
+2023-02-06 08:37:02,264 epoch 29 - iter 130/133 - loss 0.00992279 - samples/sec: 772.60 - lr: 0.010000
+2023-02-06 08:37:02,549 ----------------------------------------------------------------------------------------------------
+2023-02-06 08:37:02,550 EPOCH 29 done: loss 0.0099 - lr 0.010000
+2023-02-06 08:37:05,071 Evaluating as a multi-label problem: False
+2023-02-06 08:37:05,088 DEV : loss 0.010063448920845985 - f1-score (micro avg)  0.6533
+2023-02-06 08:37:05,677 BAD EPOCHS (no improvement): 1
+2023-02-06 08:37:05,684 ----------------------------------------------------------------------------------------------------
+2023-02-06 08:37:06,997 epoch 30 - iter 13/133 - loss 0.01003949 - samples/sec: 732.41 - lr: 0.010000
+2023-02-06 08:37:08,331 epoch 30 - iter 26/133 - loss 0.01015033 - samples/sec: 719.55 - lr: 0.010000
+2023-02-06 08:37:09,806 epoch 30 - iter 39/133 - loss 0.01011311 - samples/sec: 636.91 - lr: 0.010000
+2023-02-06 08:37:11,050 epoch 30 - iter 52/133 - loss 0.00997797 - samples/sec: 769.41 - lr: 0.010000
+2023-02-06 08:37:12,552 epoch 30 - iter 65/133 - loss 0.00995678 - samples/sec: 763.55 - lr: 0.010000
+2023-02-06 08:37:13,798 epoch 30 - iter 78/133 - loss 0.00991438 - samples/sec: 771.97 - lr: 0.010000
+2023-02-06 08:37:15,297 epoch 30 - iter 91/133 - loss 0.00988681 - samples/sec: 753.64 - lr: 0.010000
+2023-02-06 08:37:16,543 epoch 30 - iter 104/133 - loss 0.00986966 - samples/sec: 770.92 - lr: 0.010000
+2023-02-06 08:37:17,762 epoch 30 - iter 117/133 - loss 0.00987746 - samples/sec: 785.58 - lr: 0.010000
+2023-02-06 08:37:19,280 epoch 30 - iter 130/133 - loss 0.00989263 - samples/sec: 738.22 - lr: 0.010000
+2023-02-06 08:37:19,569 ----------------------------------------------------------------------------------------------------
+2023-02-06 08:37:19,571 EPOCH 30 done: loss 0.0099 - lr 0.010000
+2023-02-06 08:37:22,147 Evaluating as a multi-label problem: False
+2023-02-06 08:37:22,165 DEV : loss 0.010032457299530506 - f1-score (micro avg)  0.6627
+2023-02-06 08:37:22,507 BAD EPOCHS (no improvement): 0
+2023-02-06 08:37:22,515 saving best model
+2023-02-06 08:37:22,593 ----------------------------------------------------------------------------------------------------
+2023-02-06 08:37:24,034 epoch 31 - iter 13/133 - loss 0.00986991 - samples/sec: 795.89 - lr: 0.010000
+2023-02-06 08:37:25,350 epoch 31 - iter 26/133 - loss 0.00980032 - samples/sec: 722.12 - lr: 0.010000
+2023-02-06 08:37:26,804 epoch 31 - iter 39/133 - loss 0.00981070 - samples/sec: 788.11 - lr: 0.010000
+2023-02-06 08:37:28,032 epoch 31 - iter 52/133 - loss 0.00978956 - samples/sec: 782.72 - lr: 0.010000
+2023-02-06 08:37:29,262 epoch 31 - iter 65/133 - loss 0.00985205 - samples/sec: 776.53 - lr: 0.010000
+2023-02-06 08:37:30,801 epoch 31 - iter 78/133 - loss 0.00986377 - samples/sec: 734.35 - lr: 0.010000
+2023-02-06 08:37:32,105 epoch 31 - iter 91/133 - loss 0.00986039 - samples/sec: 737.43 - lr: 0.010000
+2023-02-06 08:37:33,586 epoch 31 - iter 104/133 - loss 0.00986586 - samples/sec: 766.82 - lr: 0.010000
+2023-02-06 08:37:34,867 epoch 31 - iter 117/133 - loss 0.00988794 - samples/sec: 748.41 - lr: 0.010000
+2023-02-06 08:37:36,405 epoch 31 - iter 130/133 - loss 0.00986537 - samples/sec: 732.70 - lr: 0.010000
+2023-02-06 08:37:36,707 ----------------------------------------------------------------------------------------------------
+2023-02-06 08:37:36,713 EPOCH 31 done: loss 0.0099 - lr 0.010000
+2023-02-06 08:37:39,148 Evaluating as a multi-label problem: False
+2023-02-06 08:37:39,165 DEV : loss 0.010030188597738743 - f1-score (micro avg)  0.6507
+2023-02-06 08:37:39,503 BAD EPOCHS (no improvement): 1
+2023-02-06 08:37:39,509 ----------------------------------------------------------------------------------------------------
+2023-02-06 08:37:40,819 epoch 32 - iter 13/133 - loss 0.00972815 - samples/sec: 729.87 - lr: 0.010000
+2023-02-06 08:37:42,293 epoch 32 - iter 26/133 - loss 0.00993442 - samples/sec: 767.42 - lr: 0.010000
+2023-02-06 08:37:43,527 epoch 32 - iter 39/133 - loss 0.00989563 - samples/sec: 777.81 - lr: 0.010000
+2023-02-06 08:37:45,030 epoch 32 - iter 52/133 - loss 0.00987169 - samples/sec: 762.50 - lr: 0.010000
+2023-02-06 08:37:46,312 epoch 32 - iter 65/133 - loss 0.00982198 - samples/sec: 744.56 - lr: 0.010000
+2023-02-06 08:37:47,772 epoch 32 - iter 78/133 - loss 0.00984166 - samples/sec: 787.72 - lr: 0.010000
+2023-02-06 08:37:49,052 epoch 32 - iter 91/133 - loss 0.00985835 - samples/sec: 753.47 - lr: 0.010000
+2023-02-06 08:37:50,603 epoch 32 - iter 104/133 - loss 0.00985180 - samples/sec: 729.35 - lr: 0.010000
+2023-02-06 08:37:51,833 epoch 32 - iter 117/133 - loss 0.00984472 - samples/sec: 780.52 - lr: 0.010000
+2023-02-06 08:37:53,083 epoch 32 - iter 130/133 - loss 0.00982573 - samples/sec: 764.01 - lr: 0.010000
+2023-02-06 08:37:53,402 ----------------------------------------------------------------------------------------------------
+2023-02-06 08:37:53,407 EPOCH 32 done: loss 0.0098 - lr 0.010000
+2023-02-06 08:37:55,865 Evaluating as a multi-label problem: False
+2023-02-06 08:37:55,882 DEV : loss 0.009983059018850327 - f1-score (micro avg)  0.662
+2023-02-06 08:37:56,448 BAD EPOCHS (no improvement): 2
+2023-02-06 08:37:56,456 ----------------------------------------------------------------------------------------------------
+2023-02-06 08:37:57,753 epoch 33 - iter 13/133 - loss 0.00986220 - samples/sec: 736.25 - lr: 0.010000
+2023-02-06 08:37:59,189 epoch 33 - iter 26/133 - loss 0.00975062 - samples/sec: 802.53 - lr: 0.010000
+2023-02-06 08:38:00,474 epoch 33 - iter 39/133 - loss 0.00986705 - samples/sec: 745.66 - lr: 0.010000
+2023-02-06 08:38:01,738 epoch 33 - iter 52/133 - loss 0.00984679 - samples/sec: 754.60 - lr: 0.010000
+2023-02-06 08:38:03,179 epoch 33 - iter 65/133 - loss 0.00978551 - samples/sec: 794.68 - lr: 0.010000
+2023-02-06 08:38:04,429 epoch 33 - iter 78/133 - loss 0.00975048 - samples/sec: 765.26 - lr: 0.010000
+2023-02-06 08:38:05,913 epoch 33 - iter 91/133 - loss 0.00976211 - samples/sec: 634.13 - lr: 0.010000
+2023-02-06 08:38:07,200 epoch 33 - iter 104/133 - loss 0.00978878 - samples/sec: 740.16 - lr: 0.010000
+2023-02-06 08:38:08,709 epoch 33 - iter 117/133 - loss 0.00982863 - samples/sec: 747.73 - lr: 0.010000
+2023-02-06 08:38:10,026 epoch 33 - iter 130/133 - loss 0.00987417 - samples/sec: 722.74 - lr: 0.010000
+2023-02-06 08:38:10,302 ----------------------------------------------------------------------------------------------------
+2023-02-06 08:38:10,303 EPOCH 33 done: loss 0.0099 - lr 0.010000
+2023-02-06 08:38:12,844 Evaluating as a multi-label problem: False
+2023-02-06 08:38:12,862 DEV : loss 0.009982590563595295 - f1-score (micro avg)  0.66
+2023-02-06 08:38:13,436 BAD EPOCHS (no improvement): 3
+2023-02-06 08:38:13,443 ----------------------------------------------------------------------------------------------------
+2023-02-06 08:38:14,816 epoch 34 - iter 13/133 - loss 0.00992892 - samples/sec: 690.92 - lr: 0.010000
+2023-02-06 08:38:16,115 epoch 34 - iter 26/133 - loss 0.00987621 - samples/sec: 736.73 - lr: 0.010000
+2023-02-06 08:38:17,696 epoch 34 - iter 39/133 - loss 0.00987597 - samples/sec: 734.20 - lr: 0.010000
+2023-02-06 08:38:18,994 epoch 34 - iter 52/133 - loss 0.00991974 - samples/sec: 737.16 - lr: 0.010000
+2023-02-06 08:38:20,513 epoch 34 - iter 65/133 - loss 0.00990955 - samples/sec: 619.03 - lr: 0.010000
+2023-02-06 08:38:21,810 epoch 34 - iter 78/133 - loss 0.00985254 - samples/sec: 739.38 - lr: 0.010000
+2023-02-06 08:38:23,273 epoch 34 - iter 91/133 - loss 0.00982011 - samples/sec: 779.25 - lr: 0.010000
+2023-02-06 08:38:24,511 epoch 34 - iter 104/133 - loss 0.00980783 - samples/sec: 773.71 - lr: 0.010000
+2023-02-06 08:38:25,739 epoch 34 - iter 117/133 - loss 0.00976338 - samples/sec: 780.95 - lr: 0.010000
+2023-02-06 08:38:27,199 epoch 34 - iter 130/133 - loss 0.00978109 - samples/sec: 777.03 - lr: 0.010000
+2023-02-06 08:38:27,468 ----------------------------------------------------------------------------------------------------
+2023-02-06 08:38:27,472 EPOCH 34 done: loss 0.0098 - lr 0.010000
+2023-02-06 08:38:29,920 Evaluating as a multi-label problem: False
+2023-02-06 08:38:29,937 DEV : loss 0.009954135864973068 - f1-score (micro avg)  0.6647
+2023-02-06 08:38:30,291 BAD EPOCHS (no improvement): 0
+2023-02-06 08:38:30,298 saving best model
+2023-02-06 08:38:30,373 ----------------------------------------------------------------------------------------------------
+2023-02-06 08:38:31,950 epoch 35 - iter 13/133 - loss 0.00960910 - samples/sec: 708.10 - lr: 0.010000
+2023-02-06 08:38:33,281 epoch 35 - iter 26/133 - loss 0.00965913 - samples/sec: 722.62 - lr: 0.010000
+2023-02-06 08:38:34,761 epoch 35 - iter 39/133 - loss 0.00961002 - samples/sec: 636.49 - lr: 0.010000
+2023-02-06 08:38:36,130 epoch 35 - iter 52/133 - loss 0.00968180 - samples/sec: 698.70 - lr: 0.010000
+2023-02-06 08:38:37,349 epoch 35 - iter 65/133 - loss 0.00970834 - samples/sec: 786.80 - lr: 0.010000
+2023-02-06 08:38:38,815 epoch 35 - iter 78/133 - loss 0.00976811 - samples/sec: 770.86 - lr: 0.010000
+2023-02-06 08:38:40,015 epoch 35 - iter 91/133 - loss 0.00977029 - samples/sec: 805.65 - lr: 0.010000
+2023-02-06 08:38:41,480 epoch 35 - iter 104/133 - loss 0.00976745 - samples/sec: 773.86 - lr: 0.010000
+2023-02-06 08:38:42,701 epoch 35 - iter 117/133 - loss 0.00977891 - samples/sec: 787.95 - lr: 0.010000
+2023-02-06 08:38:44,189 epoch 35 - iter 130/133 - loss 0.00974154 - samples/sec: 759.68 - lr: 0.010000
+2023-02-06 08:38:44,483 ----------------------------------------------------------------------------------------------------
+2023-02-06 08:38:44,489 EPOCH 35 done: loss 0.0098 - lr 0.010000
+2023-02-06 08:38:46,913 Evaluating as a multi-label problem: False
+2023-02-06 08:38:46,930 DEV : loss 0.009962853975594044 - f1-score (micro avg)  0.658
+2023-02-06 08:38:47,267 BAD EPOCHS (no improvement): 1
+2023-02-06 08:38:47,361 ----------------------------------------------------------------------------------------------------
+2023-02-06 08:38:47,365 loading file /content/drive/MyDrive/Colab Notebooks/models/flair-sentiment-classifier/best-model.pt
+2023-02-06 08:38:48,126 Evaluating as a multi-label problem: False
+2023-02-06 08:38:48,139 0.6462	0.6462	0.6462	0.6462
+2023-02-06 08:38:48,144 
+Results:
+- F-score (micro) 0.6462
+- F-score (macro) 0.6426
+- Accuracy 0.6462
+
+By class:
+              precision    recall  f1-score   support
+
+           1     0.6291    0.7363    0.6785       182
+           0     0.6712    0.5537    0.6068       177
+
+    accuracy                         0.6462       359
+   macro avg     0.6502    0.6450    0.6426       359
+weighted avg     0.6499    0.6462    0.6431       359
+
+2023-02-06 08:38:48,150 ----------------------------------------------------------------------------------------------------

models/logistic_regression.report

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models/setfit-classifier/1_Pooling/config.json

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+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false
+}

models/setfit-classifier/README.md

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+---
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+- transformers
+
+---
+
+# {MODEL_NAME}
+
+This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
+
+<!--- Describe your model here -->
+
+## Usage (Sentence-Transformers)
+
+Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
+
+```
+pip install -U sentence-transformers
+```
+
+Then you can use the model like this:
+
+```python
+from sentence_transformers import SentenceTransformer
+sentences = ["This is an example sentence", "Each sentence is converted"]
+
+model = SentenceTransformer('{MODEL_NAME}')
+embeddings = model.encode(sentences)
+print(embeddings)
+```
+
+
+
+## Usage (HuggingFace Transformers)
+Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
+
+```python
+from transformers import AutoTokenizer, AutoModel
+import torch
+
+
+#Mean Pooling - Take attention mask into account for correct averaging
+def mean_pooling(model_output, attention_mask):
+    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
+    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
+    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
+
+
+# Sentences we want sentence embeddings for
+sentences = ['This is an example sentence', 'Each sentence is converted']
+
+# Load model from HuggingFace Hub
+tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
+model = AutoModel.from_pretrained('{MODEL_NAME}')
+
+# Tokenize sentences
+encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
+
+# Compute token embeddings
+with torch.no_grad():
+    model_output = model(**encoded_input)
+
+# Perform pooling. In this case, mean pooling.
+sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
+
+print("Sentence embeddings:")
+print(sentence_embeddings)
+```
+
+
+
+## Evaluation Results
+
+<!--- Describe how your model was evaluated -->
+
+For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
+
+
+## Training
+The model was trained with the parameters:
+
+**DataLoader**:
+
+`torch.utils.data.dataloader.DataLoader` of length 160 with parameters:
+```
+{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
+```
+
+**Loss**:
+
+`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` 
+
+Parameters of the fit()-Method:
+```
+{
+    "epochs": 1,
+    "evaluation_steps": 0,
+    "evaluator": "NoneType",
+    "max_grad_norm": 1,
+    "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
+    "optimizer_params": {
+        "lr": 2e-05
+    },
+    "scheduler": "WarmupLinear",
+    "steps_per_epoch": 160,
+    "warmup_steps": 16,
+    "weight_decay": 0.01
+}
+```
+
+
+## Full Model Architecture
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel 
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
+)
+```
+
+## Citing & Authors
+
+<!--- Describe where people can find more information -->

models/setfit-classifier/config.json

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+{
+  "_name_or_path": "/root/.cache/torch/sentence_transformers/sentence-transformers_paraphrase-mpnet-base-v2/",
+  "architectures": [
+    "MPNetModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "mpnet",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "relative_attention_num_buckets": 32,
+  "torch_dtype": "float32",
+  "transformers_version": "4.26.0",
+  "vocab_size": 30527
+}

models/setfit-classifier/config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "2.0.0",
+    "transformers": "4.7.0",
+    "pytorch": "1.9.0+cu102"
+  }
+}

models/setfit-classifier/model_head.pkl

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models/setfit-classifier/modules.json

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+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

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models/setfit-classifier/sentence_bert_config.json

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+{
+  "max_seq_length": 512,
+  "do_lower_case": false
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models/setfit-classifier/special_tokens_map.json

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+  "bos_token": "<s>",
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "unk_token": "[UNK]"
+}

models/setfit-classifier/tokenizer_config.json

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+{
+  "bos_token": {
+    "__type": "AddedToken",
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "__type": "AddedToken",
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": true,
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+    "single_word": false
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+    "rstrip": false,
+    "single_word": false
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+    "__type": "AddedToken",
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "model_max_length": 512,
+  "name_or_path": "/root/.cache/torch/sentence_transformers/sentence-transformers_paraphrase-mpnet-base-v2/",
+  "never_split": null,
+  "pad_token": {
+    "__type": "AddedToken",
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "__type": "AddedToken",
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "special_tokens_map_file": null,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "MPNetTokenizer",
+  "unk_token": {
+    "__type": "AddedToken",
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}

models/spacy-classifier/model-best/config.cfg

@@ -0,0 +1,121 @@
+[paths]
+train = "/content/drive/MyDrive/Colab Notebooks/train.spacy"
+dev = "/content/drive/MyDrive/Colab Notebooks/dev.spacy"
+vectors = null
+init_tok2vec = null
+
+[system]
+gpu_allocator = null
+seed = 0
+
+[nlp]
+lang = "en"
+pipeline = ["textcat"]
+batch_size = 1000
+disabled = []
+before_creation = null
+after_creation = null
+after_pipeline_creation = null
+tokenizer = {"@tokenizers":"spacy.Tokenizer.v1"}
+
+[components]
+
+[components.textcat]
+factory = "textcat"
+scorer = {"@scorers":"spacy.textcat_scorer.v1"}
+threshold = 0.5
+
+[components.textcat.model]
+@architectures = "spacy.TextCatBOW.v2"
+exclusive_classes = true
+ngram_size = 1
+no_output_layer = false
+nO = null
+
+[corpora]
+
+[corpora.dev]
+@readers = "spacy.Corpus.v1"
+path = ${paths.dev}
+max_length = 0
+gold_preproc = false
+limit = 0
+augmenter = null
+
+[corpora.train]
+@readers = "spacy.Corpus.v1"
+path = ${paths.train}
+max_length = 0
+gold_preproc = false
+limit = 0
+augmenter = null
+
+[training]
+dev_corpus = "corpora.dev"
+train_corpus = "corpora.train"
+seed = ${system.seed}
+gpu_allocator = ${system.gpu_allocator}
+dropout = 0.1
+accumulate_gradient = 1
+patience = 3200
+max_epochs = 0
+max_steps = 20000
+eval_frequency = 200
+frozen_components = []
+annotating_components = []
+before_to_disk = null
+
+[training.batcher]
+@batchers = "spacy.batch_by_words.v1"
+discard_oversize = false
+tolerance = 0.2
+get_length = null
+
+[training.batcher.size]
+@schedules = "compounding.v1"
+start = 100
+stop = 1000
+compound = 1.001
+t = 0.0
+
+[training.logger]
+@loggers = "spacy.ConsoleLogger.v1"
+progress_bar = false
+
+[training.optimizer]
+@optimizers = "Adam.v1"
+beta1 = 0.9
+beta2 = 0.999
+L2_is_weight_decay = true
+L2 = 0.01
+grad_clip = 1.0
+use_averages = false
+eps = 0.00000001
+learn_rate = 0.00005
+
+[training.score_weights]
+cats_score = 1.0
+cats_score_desc = null
+cats_micro_p = null
+cats_micro_r = null
+cats_micro_f = null
+cats_macro_p = null
+cats_macro_r = null
+cats_macro_f = null
+cats_macro_auc = null
+cats_f_per_type = null
+cats_macro_auc_per_type = null
+
+[pretraining]
+
+[initialize]
+vectors = ${paths.vectors}
+init_tok2vec = ${paths.init_tok2vec}
+vocab_data = null
+lookups = null
+before_init = null
+after_init = null
+
+[initialize.components]
+
+[initialize.tokenizer]

models/spacy-classifier/model-best/meta.json

@@ -0,0 +1,59 @@
+{
+  "lang":"en",
+  "name":"pipeline",
+  "version":"0.0.0",
+  "spacy_version":">=3.4.4,<3.5.0",
+  "description":"",
+  "author":"",
+  "email":"",
+  "url":"",
+  "license":"",
+  "spacy_git_version":"Unknown",
+  "vectors":{
+    "width":0,
+    "vectors":0,
+    "keys":0,
+    "name":null,
+    "mode":"default"
+  },
+  "labels":{
+    "textcat":[
+      "positive",
+      "negative"
+    ]
+  },
+  "pipeline":[
+    "textcat"
+  ],
+  "components":[
+    "textcat"
+  ],
+  "disabled":[
+
+  ],
+  "performance":{
+    "cats_score":0.7499305363,
+    "cats_score_desc":"macro F",
+    "cats_micro_p":0.75,
+    "cats_micro_r":0.75,
+    "cats_micro_f":0.75,
+    "cats_macro_p":0.7500320184,
+    "cats_macro_r":0.7499097699,
+    "cats_macro_f":0.7499305363,
+    "cats_macro_auc":0.8201950037,
+    "cats_f_per_type":{
+      "positive":{
+        "p":0.7513661202,
+        "r":0.7402422611,
+        "f":0.7457627119
+      },
+      "negative":{
+        "p":0.7486979167,
+        "r":0.7595772787,
+        "f":0.7540983607
+      }
+    },
+    "cats_macro_auc_per_type":0.0,
+    "textcat_loss":23.4422574639
+  }
+}

models/spacy-classifier/model-best/textcat/cfg

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+oid sha256:e7822fc6181a943012f1395d8b5f12dee231cc5d4485bfe2c8dc890874534bcc
+size 95

models/spacy-classifier/model-best/textcat/model

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models/spacy-classifier/model-best/vocab/key2row

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+version https://git-lfs.github.com/spec/v1
+oid sha256:76be8b528d0075f7aae98d6fa57a6d3c83ae480a8469e668d7b0af968995ac71
+size 1

models/spacy-classifier/model-best/vocab/lookups.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:76be8b528d0075f7aae98d6fa57a6d3c83ae480a8469e668d7b0af968995ac71
+size 1

models/spacy-classifier/model-best/vocab/vectors.cfg

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+{
+  "mode":"default"
+}

models/tfidf_vectorizer.sav

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a15a3c028d7a5689f8486fc1be474e44a85651bc72de1f0091071ab5562d723b
+size 84347

models/xgb_clf.report

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+{
+    "0": {
+        "precision": 0.7127659574468085,
+        "recall": 0.3785310734463277,
+        "f1-score": 0.49446494464944646,
+        "support": 177
+    },
+    "1": {
+        "precision": 0.5849056603773585,
+        "recall": 0.8516483516483516,
+        "f1-score": 0.6935123042505593,
+        "support": 182
+    },
+    "accuracy": 0.6183844011142061,
+    "macro avg": {
+        "precision": 0.6488358089120835,
+        "recall": 0.6150897125473397,
+        "f1-score": 0.5939886244500029,
+        "support": 359
+    },
+    "weighted avg": {
+        "precision": 0.6479454168712099,
+        "recall": 0.6183844011142061,
+        "f1-score": 0.5953747481241053,
+        "support": 359
+    }
+}

models/xgb_clf.sav

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+version https://git-lfs.github.com/spec/v1
+oid sha256:dc9803229fc28ab2acba4859e790f9a01ccafd479f9ff2250ee7fab5cafe8b9f
+size 94725

paths.py

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+import os
+
+PROJECT_DIR = os.path.dirname(os.path.abspath(__file__))
+
+MODELS_DIR = os.path.join(PROJECT_DIR, 'models')
+
+FLAIR_MODEL_PATH = os.path.join(MODELS_DIR, 'flair-sentiment-classifier/best-model.pt')
+SPACY_MODEL_PATH = os.path.join(MODELS_DIR, 'spacy-classifier/model-best')
+SETFIT_MODEL_PATH = os.path.join(MODELS_DIR, 'setfit-classifier')
+NB_MODEL_PATH = os.path.join(MODELS_DIR, 'm_nb.sav')
+COUNT_VECTORIZER_PATH = os.path.join(MODELS_DIR, 'count_vectorizer.sav')
+TFIDF_VECTORIZER_PATH = os.path.join(MODELS_DIR, 'tfidf_vectorizer.sav')