“In this notebook I try to use a competition dataset of tweets reacting to Mother’s day and classify their sentiments with spaCy”
#hide
import requests
zip_file = requests.get('https://he-s3.s3.amazonaws.com/media/hackathon/hackerearth-test-draft-1-102/predicting-tweet-sentiments-231101b4/fa62f5d69a9f11ea.zip?Signature=2yxQgjub3w4jc%2BhnFKq0GEwmNEE%3D&Expires=1590825609&AWSAccessKeyId=AKIA6I2ISGOYH7WWS3G5')
with open('data.zip', 'wb') as f:
f.write(zip_file.content)#hide
!cp /content/drive/My\ Drive/Data/tweets_mother_day.zip ./data.zip
!unzip data.zipArchive: data.zip
creating: dataset/
inflating: dataset/train.csv
inflating: dataset/test.csv We will use the method from my previous post to clean the text.
from pathlib import Path
import pandas as pd
DATA_PATH = Path('dataset/')
DRIVE_PATH = Path(r"/content/drive/My Drive/Spacy/Pretrained")train_data = pd.read_csv(DATA_PATH/'train.csv', index_col=0)
train_data.head()| original_text | lang | retweet_count | original_author | sentiment_class | |
|---|---|---|---|---|---|
| id | |||||
| 1.245025e+18 | Happy #MothersDay to all you amazing mothers o... | en | 0 | BeenXXPired | 0 |
| 1.245759e+18 | Happy Mothers Day Mum - I'm sorry I can't be t... | en | 1 | FestiveFeeling | 0 |
| 1.246087e+18 | Happy mothers day To all This doing a mothers ... | en | 0 | KrisAllenSak | -1 |
| 1.244803e+18 | Happy mothers day to this beautiful woman...ro... | en | 0 | Queenuchee | 0 |
| 1.244876e+18 | Remembering the 3 most amazing ladies who made... | en | 0 | brittan17446794 | -1 |
Let’s check average length of text before cleaning.
#collapse
print(sum(
train_data['original_text'].apply(len).tolist()
)/train_data.shape[0])227.42102009273572train_data['original_text'].replace(
# Regex is match : the text to replace with
{'(https?:\/\/.*|pic.*)[\r\n]*' : ''},
regex=True, inplace=True)Let’s check the average length again.
#hide
print(sum(
train_data['original_text'].apply(len).tolist()
)/train_data.shape[0])185.95672333848532The regex did it’s job I suppose.
train_data.head()| original_text | lang | retweet_count | original_author | sentiment_class | |
|---|---|---|---|---|---|
| id | |||||
| 1.245025e+18 | Happy #MothersDay to all you amazing mothers o... | en | 0 | BeenXXPired | 0 |
| 1.245759e+18 | Happy Mothers Day Mum - I'm sorry I can't be t... | en | 1 | FestiveFeeling | 0 |
| 1.246087e+18 | Happy mothers day To all This doing a mothers ... | en | 0 | KrisAllenSak | -1 |
| 1.244803e+18 | Happy mothers day to this beautiful woman...ro... | en | 0 | Queenuchee | 0 |
| 1.244876e+18 | Remembering the 3 most amazing ladies who made... | en | 0 | brittan17446794 | -1 |
In my previous exploratory post, I have seen the data and I think that the features other than the text may not be required, (ie)
0 must mean Neutral1 means Positive-1 means Negativetrain_data['sentiment_class'].value_counts().plot(kind='bar')
Let’s see some sentences with negative examples, I am interested why they should be negative on a happy day(Mother’s day)
list_of_neg_sents = train_data.loc[train_data['sentiment_class'] == -1, 'original_text'].tolist()#collapse
pprint(list_of_neg_sents[:5])['Happy mothers day To all This doing a mothers days work. Today been quiet '
'but Had time to reflect. Dog walk, finish a jigsaw do the garden, learn few '
'more guitar chords, drunk some strawberry gin and tonic and watch Lee evens '
'on DVD. My favourite place to visit. #isolate ',
'Remembering the 3 most amazing ladies who made me who I am! My late '
'grandmother iris, mum carol and great grandmother Ethel. Missed but never '
'forgotten! Happy mothers day to all those great mums out there! Love sent to '
'all xxxx ',
'Happy Mothers Day to everyone tuning in. This is the 4th Round game between '
'me and @CastigersJ Live coverage on @Twitter , maybe one day @SkySportsRL or '
'on the OurLeague app',
"Happy Mothers Day ! We hope your mums aren't planning to do any work around "
'the house today! Surely it can wait until next week? #plumbers '
'#heatingspecialists #mothersday #mothersday ',
"Happy mothers day to all those mums whos children can't be with them today. "
'My son Dylan lives in heaven I wish I could see him for one more hug. I wish '
'I could tell him how much I love and miss him. Huge happy mothers day to '
'your mum too.']Well some tweets actually express their feelings for their deceased mothers. This is understandable.
We can use traditional NLP methods or deep learning methods to model the text. We will try the deep learning in this notebook .
It’s recommended here that to improve performance of the classifier, Language model pretraining is one way to do so.
Spacy requires a .jsonl format of input to train text
Get texts from the dataframe and store in jsonl format more about that here.
We can also load the test data to get some more sample for the pretraining, this will not cause Data Leakage because we are not giving any labels to the model.
test_data = pd.read_csv(DATA_PATH/'test.csv', index_col=0)
test_data.head()| original_text | lang | retweet_count | original_author | |
|---|---|---|---|---|
| id | ||||
| 1.246628e+18 | 3. Yeah, I once cooked potatoes when I was 3 y... | en | 0 | LToddWood |
| 1.245898e+18 | Happy Mother's Day to all the mums, step-mums,... | en | 0 | iiarushii |
| 1.244717e+18 | I love the people from the UK, however, when I... | en | 0 | andreaanderegg |
| 1.245730e+18 | Happy 81st Birthday Happy Mother’s Day to my m... | en | 1 | TheBookTweeters |
| 1.244636e+18 | Happy Mothers day to all those wonderful mothe... | en | 0 | andreaanderegg |
Let’s clean the test set for links as well
test_data['original_text'].replace(
# Regex pattern to match : the text to replace with
{'(https?:\/\/.*|pic.*)[\r\n]*' : ''},
regex=True, inplace=True)test_data.shape(1387, 4)texts_series = pd.concat([train_data['original_text'], test_data['original_text']], axis='rows')Let’s check the length
texts_series.shape[0], train_data.shape[0]+test_data.shape[0](4622, 4622)So now we can use this texts_series to create the jsonl file.
list_of_texts = [
# Form dictionary with 'text' key
{'text': value}
for _, value in texts_series.items()
]I will use srsly to write this list of dictionaries to a jsonl file
import srsly
# saving to my Google drive
srsly.write_jsonl(DRIVE_PATH/'pretrain_texts.jsonl', list_of_texts)We can see a few lines from the saved file.
#collapse
from pprint import pprint
with Path(DRIVE_PATH/'pretrain_texts.jsonl').open() as f:
lines = [next(f) for x in range(5)]
pprint(lines)['{"text":"Happy #MothersDay to all you amazing mothers out there! I know '
"it's hard not being able to see your mothers today but it's on all of us to "
'do what we can to protect the most vulnerable members of our society. '
'#BeatCoronaVirus "}\n',
'{"text":"Happy Mothers Day Mum - I\'m sorry I can\'t be there to bring you '
"Mothers day flowers & a cwtch - honestly at this point I'd walk on hot coals "
"to be able to. But I'll be there with bells on as soon as I can be. Love you "
'lots xxx (p.s we need more photos!) "}\n',
'{"text":"Happy mothers day To all This doing a mothers days work. Today been '
'quiet but Had time to reflect. Dog walk, finish a jigsaw do the garden, '
'learn few more guitar chords, drunk some strawberry gin and tonic and watch '
'Lee evens on DVD. My favourite place to visit. #isolate "}\n',
'{"text":"Happy mothers day to this beautiful woman...royalty soothes you '
'mummy jeremy and emerald and more #PrayForRoksie #UltimateLoveNG "}\n',
'{"text":"Remembering the 3 most amazing ladies who made me who I am! My late '
'grandmother iris, mum carol and great grandmother Ethel. Missed but never '
'forgotten! Happy mothers day to all those great mums out there! Love sent to '
'all xxxx "}\n']We should download a pretrained to model to use, Here I am using en_core_web_md from Spacy.
This can be confusing (ie) Why should I train a pretrained model, if I can download one, The idea is that the downloaded pretrained model would have been trained with a very different type of dataset, but it already has some knowledge on interpreting words in English sentences.
But here we have dataset of tweets which the downloaded pretrained model may or may not have seen during it’s training, So we use our dataset to fine-tune the downloaded model, so that with minimum training it can start understanding the tweets right away.
#collapse
!python -m spacy download en_core_web_md#collapse
%%bash
# Command to pretrain a language model
# Path to jsonl file with data
# Using md model as the base
# saving the model on my Drive folder
# training for 50 iterations with seed set to 0
python -m spacy pretrain /content/drive/My\ Drive/Spacy/Pretrained/pretrain_texts.jsonl \
/usr/local/lib/python3.6/dist-packages/en_core_web_md/en_core_web_md-2.2.5 \
/content/drive/My\ Drive/Spacy/Pretrained/ \
-i 50 -s 0 \ℹ Not using GPU ⚠ Output directory is not empty It is better to use an empty directory or refer to a new output path, then the new directory will be created for you. ✔ Saved settings to config.json ⠙ Loading input texts... ✔ Loaded input texts ⠙ Loading model '/usr/local/lib/python3.6/dist-packages/en_core_web_md/en_core_web_md-2.2.5'... ⠹ Loading model '/usr/local/lib/python3.6/dist-packages/en_core_web_md/en_core_web_md-2.2.5'... ⠸ Loading model '/usr/local/lib/python3.6/dist-packages/en_core_web_md/en_core_web_md-2.2.5'... ⠼ Loading model '/usr/local/lib/python3.6/dist-packages/en_core_web_md/en_core_web_md-2.2.5'... ⠴ Loading model '/usr/local/lib/python3.6/dist-packages/en_core_web_md/en_core_web_md-2.2.5'... ⠦ Loading model '/usr/local/lib/python3.6/dist-packages/en_core_web_md/en_core_web_md-2.2.5'... ⠧ Loading model '/usr/local/lib/python3.6/dist-packages/en_core_web_md/en_core_web_md-2.2.5'... ⠇ Loading model 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'/usr/local/lib/python3.6/dist-packages/en_core_web_md/en_core_web_md-2.2.5'... ⠇ Loading model '/usr/local/lib/python3.6/dist-packages/en_core_web_md/en_core_web_md-2.2.5'... ⠏ Loading model '/usr/local/lib/python3.6/dist-packages/en_core_web_md/en_core_web_md-2.2.5'... ⠙ Loading model '/usr/local/lib/python3.6/dist-packages/en_core_web_md/en_core_web_md-2.2.5'... ⠹ Loading model '/usr/local/lib/python3.6/dist-packages/en_core_web_md/en_core_web_md-2.2.5'... ⠸ Loading model '/usr/local/lib/python3.6/dist-packages/en_core_web_md/en_core_web_md-2.2.5'... ⠼ Loading model '/usr/local/lib/python3.6/dist-packages/en_core_web_md/en_core_web_md-2.2.5'... ⠴ Loading model '/usr/local/lib/python3.6/dist-packages/en_core_web_md/en_core_web_md-2.2.5'... ⠦ Loading model '/usr/local/lib/python3.6/dist-packages/en_core_web_md/en_core_web_md-2.2.5'... ⠧ Loading model '/usr/local/lib/python3.6/dist-packages/en_core_web_md/en_core_web_md-2.2.5'... ⠇ Loading model '/usr/local/lib/python3.6/dist-packages/en_core_web_md/en_core_web_md-2.2.5'... ⠏ Loading model '/usr/local/lib/python3.6/dist-packages/en_core_web_md/en_core_web_md-2.2.5'... ⠙ Loading model '/usr/local/lib/python3.6/dist-packages/en_core_web_md/en_core_web_md-2.2.5'... ✔ Loaded model '/usr/local/lib/python3.6/dist-packages/en_core_web_md/en_core_web_md-2.2.5' ============== Pre-training tok2vec layer - starting at epoch 0 ============== # # Words Total Loss Loss w/s 0 115177 114619.719 114619 7308 0 177090 174673.695 60053 8123 1 291933 282095.656 107421 8353 1 354180 337893.113 55797 8156 2 468951 432705.457 94812 8398 2 531270 479373.527 46668 8271 3 646206 557380.137 78006 8368 3 708360 595962.348 38582 8151 4 823108 662773.332 66810 8349 4 885450 696823.672 34050 8125 5 1000591 756743.684 59920 8254 5 1062540 787816.266 31072 7943 6 1177552 844198.828 56382 8380 6 1239630 874128.219 29929 7996 7 1354814 928725.262 54597 8291 7 1416720 957604.215 28878 8081 8 1531685 1010607.96 53003 8310 8 1593810 1039022.08 28414 8006 9 1708981 1091185.60 52163 8248 9 1770900 1118857.03 27671 8032 10 1885776 1169906.66 51049 8240 10 1947990 1197361.49 27454 8015 11 2062486 1247384.96 50023 8344 11 2125080 1274605.26 27220 8153 12 2239188 1323843.16 49237 8376 12 2302170 1350702.81 26859 7941 13 2416942 1399298.78 48595 8213 13 2479260 1425267.50 25968 7968 14 2594827 1473118.10 47850 8357 14 2656350 1498307.73 25189 7879 15 2770909 1544661.96 46354 7572 15 2833440 1569846.84 25184 7980 16 2948496 1615382.64 45535 8137 16 3010530 1639715.23 24332 7848 17 3125700 1684541.43 44826 8316 17 3187620 1708564.64 24023 7941 18 3302357 1753053.58 44488 8039 18 3364710 1776999.23 23945 7234 19 3480485 1821211.88 44212 8191 19 3541800 1844436.19 23224 7859 20 3657480 1887923.85 43487 8170 20 3718890 1911031.40 23107 7902 21 3833871 1954109.82 43078 8253 21 3895980 1977017.92 22908 7840 22 4011686 2019201.90 42183 8096 22 4073070 2041539.05 22337 7906 23 4188348 2083159.02 41619 8148 23 4250160 2105332.99 22173 7882 24 4364945 2146261.39 40928 8127 24 4427250 2168323.78 22062 8017 25 4542920 2209202.75 40878 8059 25 4604340 2230725.28 21522 7823 26 4719450 2271131.96 40406 8272 26 4781430 2292648.11 21516 7986 27 4896609 2332466.73 39818 8243 27 4958520 2353768.18 21301 8030 28 5073022 2392817.96 39049 8217 28 5135610 2414267.76 21449 8032 29 5250013 2452981.53 38713 8250 29 5312700 2474212.04 21230 8098 30 5427418 2512822.70 38610 8251 30 5489790 2533517.18 20694 8083 31 5604738 2572000.05 38482 8291 31 5666880 2592500.56 20500 8071 32 5781993 2630529.82 38029 8278 32 5843970 2651013.32 20483 8080 33 5959305 2689165.69 38152 8269 33 6021060 2709293.04 20127 8086 34 6136501 2746749.93 37456 8278 34 6198150 2766661.32 19911 8105 35 6313772 2803964.79 37303 8262 35 6375240 2823781.55 19816 8095 36 6490354 2860641.99 36860 8235 36 6552330 2880476.80 19834 8056 37 6667255 2917062.19 36585 8231 37 6729420 2936732.01 19669 8108 38 6844248 2972992.59 36260 8276 38 6906510 2992718.12 19725 8074 39 7021001 3028854.93 36136 8275 39 7083600 3048416.73 19561 8092 40 7199191 3084584.77 36168 8304 40 7260690 3103795.33 19210 8085 41 7375380 3139675.02 35879 8276 41 7437780 3158848.65 19173 8136 42 7552544 3194214.46 35365 8276 42 7614870 3213532.30 19317 8072 43 7730130 3248964.60 35432 7724 43 7791960 3267930.77 18966 8096 44 7906035 3302653.15 34722 8263 44 7969050 3321900.34 19247 8103 45 8083488 3356590.95 34690 8251 45 8146140 3375484.65 18893 8084 46 8261612 3410429.05 34944 8228 46 8323230 3429043.30 18614 8121 47 8437925 3463440.15 34396 8268 47 8500320 3482173.21 18733 7931 48 8615136 3516342.36 34169 8294 48 8677410 3534982.44 18640 8098 49 8791889 3568641.36 33658 8265 49 8854500 3587280.92 18639 8102 ⚠ Skipped 250 empty values ✔ Successfully finished pretrain
I have chosen to use the default parameters however one might need to change them for their problem.
We can see from the logs that the loss value in the last iteration is 18639, but since the batch_size was 3000 our data must have splitted to 2 batches, (number of texts are 4622) we should also take the previous log entry to account which is loss of 33658, So the average of them would be 26148.5, This number might be intimidating but the only way to check if it actually helps is to try to train a model with it.
If it doesn't then we can resume the training from the model saved on the last epoch. We keep only the last model from the pretraining.
#hide
!mv /content/drive/My\ Drive/Spacy/Pretrained/model49.bin /content/drive/My\ Drive/Spacy/
!mv /content/drive/My\ Drive/Spacy/Pretrained/*.json* /content/drive/My\ Drive/Spacy/#hide
!rm /content/drive/My\ Drive/Spacy/Pretrained/*.bin#hide
!mkdir /content/drive/My\ Drive/Spacy/Pretrained/fifty_iter
!mv /content/drive/My\ Drive/Spacy/model49.bin /content/drive/My\ Drive/Spacy/Pretrained/fifty_iter
!mv /content/drive/My\ Drive/Spacy/*g.json* /content/drive/My\ Drive/Spacy/Pretrained/fifty_iterLet’s train a text classifier with Spacy
Now that we have a pretrained model, We now need to prepare data for training the text classifier. Let’s have a look at the data format that Spacy expects the data to be in.
{
"entities": [(0, 4, "ORG")],
"heads": [1, 1, 1, 5, 5, 2, 7, 5],
"deps": ["nsubj", "ROOT", "prt", "quantmod", "compound", "pobj", "det", "npadvmod"],
"tags": ["PROPN", "VERB", "ADP", "SYM", "NUM", "NUM", "DET", "NOUN"],
"cats": {"BUSINESS": 1.0},
}This format works for training via code, as given in the examples above, There is also another format mentioned here
cats is the only part we need to worry about, this must be where they look for categories/classes.
We have three classes in our dataset - 0 for Neutral - 1 for Positive - -1 for Negative
and they are mutually-exclusive (There can be only one label for a sentence)
We also need to split the training data we have to training and evaluation sets so that we can see how well our model has learnt the problem.
Let’s try to programmatically generate the training data from pandas dataframe
label_map = {1:'POSITIVE', -1:'NEGATIVE', 0:'NEUTRAL'}We need list of tuples of text and the annotation details in a dictionary as mentioned above.
# Adapted from sample data in https://spacy.io/usage/training#training-simple-style
train_json = [
# Get the text from dataframe row
(tweet.original_text,
{'cats':{
label_map[tweet.sentiment_class]:1.0
}
})
for tweet in train_data[['original_text', 'sentiment_class']].itertuples(index=False, name='Tweet')
]train_json[0]("Happy #MothersDay to all you amazing mothers out there! I know it's hard not being able to see your mothers today but it's on all of us to do what we can to protect the most vulnerable members of our society. #BeatCoronaVirus ",
{'cats': {'NEUTRAL': 1.0}})Now we will split the training data
from sklearn.model_selection import train_test_split
# Stratified split with labels
train_split, eval_split = train_test_split(train_json, test_size=0.2,
stratify=train_data['sentiment_class'])len(train_split), len(eval_split)(2588, 647)We should save them as json files to give them as input to the command line train utility in spacy.
import json
with Path(DRIVE_PATH/'train_clas.json').open('w') as f:
json.dump(train_split, f)
with Path(DRIVE_PATH/'eval_clas.json').open('w') as f:
json.dump(eval_split, f)Now should if we have enough data to train the model with train spacy command in CLI, for that I will use Spacy’s debug-data command in CLI.
!python -m spacy debug-data -husage: spacy debug-data [-h] [-tm None] [-b None] [-p tagger,parser,ner] [-IW]
[-V] [-NF]
lang train_path dev_path
Analyze, debug and validate your training and development data, get useful
stats, and find problems like invalid entity annotations, cyclic
dependencies, low data labels and more.
positional arguments:
lang model language
train_path location of JSON-formatted training data
dev_path location of JSON-formatted development data
optional arguments:
-h, --help show this help message and exit
-tm None, --tag-map-path None
Location of JSON-formatted tag map
-b None, --base-model None
name of model to update (optional)
-p tagger,parser,ner, --pipeline tagger,parser,ner
Comma-separated names of pipeline components to train
-IW, --ignore-warnings
Ignore warnings, only show stats and errors
-V, --verbose Print additional information and explanations
-NF, --no-format Don't pretty-print the results%%bash
(python -m spacy debug-data en \
/content/drive/My\ Drive/Spacy/Pretrained/train_clas.json \
/content/drive/My\ Drive/Spacy/Pretrained/eval_clas.json \
-p 'textcat' \
)=========================== Data format validation =========================== ⠙ Loading corpus... ⠹ Loading corpus... ⠸ Loading corpus... ⠼ Loading corpus... ⠴ Loading corpus... ⠦ Loading corpus... ⠧ Loading corpus... ⠇ Loading corpus... ⠏ Loading corpus... ⠙ Loading corpus... ⠹ Loading corpus... ⠸ Loading corpus... ⠼ Loading corpus... ⠴ Loading corpus... ⠦ Loading corpus... ⠧ Loading corpus... ⠇ Loading corpus... ✔ Corpus is loadable =============================== Training stats =============================== Training pipeline: textcat Starting with blank model 'en' 0 training docs 0 evaluation docs ✘ No evaluation docs ✔ No overlap between training and evaluation data ✘ Low number of examples to train from a blank model (0) ============================== Vocab & Vectors ============================== ℹ 0 total words in the data (0 unique) ℹ No word vectors present in the model ============================ Text Classification ============================ ℹ Text Classification: 0 new label(s), 0 existing label(s) ℹ The train data contains only instances with mutually-exclusive classes. ================================== Summary ================================== ✔ 2 checks passed ✘ 2 errors
There must be something I missed now, I asked a question on stackoverflow regarding this, turns out we need to get .jsonl format(again) and use the script provided in the repo to convert to the required json format for training, now I need to change the data generation a little bit to do that.
train_jsonl = [
# Get the text from dataframe row
{'text': tweet.original_text,
'cats': {v: 1.0 if tweet.sentiment_class == k else 0.0
for k, v in label_map.items()},
'meta':{"id": str(tweet.Index)}
}
for tweet in train_data[['original_text', 'sentiment_class']].itertuples(index=True, name='Tweet')
]train_jsonl[0]{'cats': {'NEGATIVE': 0.0, 'NEUTRAL': 1.0, 'POSITIVE': 0.0},
'meta': {'id': '1.24502457848689e+18'},
'text': "Happy #MothersDay to all you amazing mothers out there! I know it's hard not being able to see your mothers today but it's on all of us to do what we can to protect the most vulnerable members of our society. #BeatCoronaVirus "}So instead of a list of tuples now I have a list of dictionaries. We need to split again to have an evaluation set
# Stratified split with labels
train_split, eval_split = train_test_split(train_jsonl, test_size=0.2,
stratify=train_data['sentiment_class'])len(train_split), len(eval_split)(2588, 647)#hide
srsly.write_jsonl(DRIVE_PATH.parent/'train_texts.jsonl', train_split)
srsly.write_jsonl(DRIVE_PATH.parent/'eval_texts.jsonl', eval_split)We still need to convert the jsonl to the required json format, now for that I will use the script named textcatjsonl_to_trainjson.py in this repo. Let’s download the script from the repo.
!wget -O script.py https://raw.githubusercontent.com/explosion/spaCy/master/examples/training/textcat_example_data/textcatjsonl_to_trainjson.py--2020-05-30 07:44:50-- https://raw.githubusercontent.com/explosion/spaCy/master/examples/training/textcat_example_data/textcatjsonl_to_trainjson.py
Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...
Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 1542 (1.5K) [text/plain]
Saving to: ‘script.py’
script.py 0%[ ] 0 --.-KB/s
script.py 100%[===================>] 1.51K --.-KB/s in 0s
2020-05-30 07:44:51 (17.2 MB/s) - ‘script.py’ saved [1542/1542]
%%bash
python script.py -m en /content/drive/My\ Drive/Spacy/train_texts.jsonl /content/drive/My\ Drive/Spacy
python script.py -m en /content/drive/My\ Drive/Spacy/eval_texts.jsonl /content/drive/My\ Drive/SpacyLet’s try to debug again
#hide_input
%%bash
(python -m spacy debug-data en \
/content/drive/My\ Drive/Spacy/train_texts.json \
/content/drive/My\ Drive/Spacy/eval_texts.json \
-p 'textcat' \
)=========================== Data format validation =========================== ⠙ Loading corpus... ⠹ Loading corpus... ⠸ Loading corpus... ⠼ Loading corpus... ⠴ Loading corpus... ⠦ Loading corpus... ⠧ Loading corpus... ⠇ Loading corpus... ⠏ Loading corpus... ⠙ Loading corpus... ⠹ Loading corpus... ⠸ Loading corpus... ⠼ Loading corpus... ⠴ Loading corpus... ⠦ Loading corpus... ⠧ Loading corpus... ⠇ Loading corpus... ⠏ Loading corpus... ⠙ Loading corpus... ⠹ Loading corpus... ⠸ Loading corpus... ⠼ Loading corpus... ⠴ Loading corpus... ⠦ Loading corpus... ⠧ Loading corpus... ⠇ Loading corpus... ⠏ Loading corpus... ⠙ Loading corpus... ⠹ Loading corpus... ⠸ Loading corpus... ⠼ Loading corpus... ⠴ Loading corpus... ⠦ Loading corpus... ⠧ Loading corpus... ⠇ Loading corpus... ⠏ Loading corpus... ⠙ Loading corpus... ⠹ Loading corpus... ⠸ Loading corpus... ✔ Corpus is loadable =============================== Training stats =============================== Training pipeline: textcat Starting with blank model 'en' 2584 training docs 647 evaluation docs ⚠ 5 training examples also in evaluation data ============================== Vocab & Vectors ============================== ℹ 98859 total words in the data (10688 unique) ℹ No word vectors present in the model ============================ Text Classification ============================ ℹ Text Classification: 3 new label(s), 0 existing label(s) ℹ The train data contains only instances with mutually-exclusive classes. ================================== Summary ================================== ✔ 1 check passed ⚠ 1 warning
It worked !, Thanks to the answerer of this question, now we know that our data format is correct. Turns out there is another command to convert our files to spacy’s JSON format which is mentioned here.
The output is pointing out that the evaluation set has some data leakage. I will try to remove that now.
new_eval = [annot for annot in eval_split if all([annot['text'] != t['text'] for t in train_split])]len(new_eval), len(eval_split)(641, 647)We thought there were 5 samples leaking into the training data, it is six here, anyway let’s try to validate the data again.
#hide
srsly.write_jsonl(DRIVE_PATH.parent/'eval_texts.jsonl', new_eval)#hide
!python script.py -m en /content/drive/My\ Drive/Spacy/eval_texts.jsonl /content/drive/My\ Drive/Spacy%%bash
(python -m spacy debug-data en \
/content/drive/My\ Drive/Spacy/train_texts.json \
/content/drive/My\ Drive/Spacy/eval_texts.json \
-p 'textcat' \
)=========================== Data format validation =========================== ⠙ Loading corpus... ⠹ Loading corpus... ⠸ Loading corpus... ⠼ Loading corpus... ⠴ Loading corpus... ⠦ Loading corpus... ⠧ Loading corpus... ⠇ Loading corpus... ⠏ Loading corpus... ⠙ Loading corpus... ⠹ Loading corpus... ⠸ Loading corpus... ⠼ Loading corpus... ⠴ Loading corpus... ⠦ Loading corpus... ⠧ Loading corpus... ⠇ Loading corpus... ⠏ Loading corpus... ⠙ Loading corpus... ⠹ Loading corpus... ⠸ Loading corpus... ⠼ Loading corpus... ⠴ Loading corpus... ⠦ Loading corpus... ⠧ Loading corpus... ⠇ Loading corpus... ⠏ Loading corpus... ⠙ Loading corpus... ⠹ Loading corpus... ⠸ Loading corpus... ⠼ Loading corpus... ⠴ Loading corpus... ⠦ Loading corpus... ⠧ Loading corpus... ⠇ Loading corpus... ⠏ Loading corpus... ⠙ Loading corpus... ⠹ Loading corpus... ⠸ Loading corpus... ⠼ Loading corpus... ✔ Corpus is loadable =============================== Training stats =============================== Training pipeline: textcat Starting with blank model 'en' 2584 training docs 641 evaluation docs ✔ No overlap between training and evaluation data ============================== Vocab & Vectors ============================== ℹ 98859 total words in the data (10688 unique) ℹ No word vectors present in the model ============================ Text Classification ============================ ℹ Text Classification: 3 new label(s), 0 existing label(s) ℹ The train data contains only instances with mutually-exclusive classes. ================================== Summary ================================== ✔ 2 checks passed
We are all set to start training now!
I have made the command to train in CLI, Please refer the comments for details in the order of the arguments given here
%%bash
## Arguement info
# Language of text in which the Model is going to be trained
# Path to store model
# Training data json path
# Evaluation data json path
# Pipeline components that we are going to train
# Number of iterations in total
# Nummber of iterations to wait before improvement in eval accuracy
# Pretrained model to start with
# version
# Augmentation for data(2 params)
# Model Architecture for text classifier (cnn + bow)
(python -m spacy train \
en \
-b en_core_web_sm \
/content/drive/My\ Drive/Spacy/Classifier \
/content/drive/My\ Drive/Spacy/train_texts.json \
/content/drive/My\ Drive/Spacy/train_texts.json \
-p "textcat" \
-n 100 \
-ne 10 \
-t2v /content/drive/My\ Drive/Spacy/Pretrained/fifty_iter/model49.bin \
-V 0.1 \
-nl 0.1 \
-ovl 0.1)Training pipeline: ['textcat'] Starting with base model 'en_core_web_sm' Adding component to base model 'textcat' Counting training words (limit=0) Loaded pretrained tok2vec for: [] Textcat evaluation score: F1-score macro-averaged across the labels 'POSITIVE, NEGATIVE, NEUTRAL' Itn Textcat Loss Textcat Token % CPU WPS --- ------------ ------- ------- ------- 1 26.738 39.853 100.000 177034 2 5.179 65.120 100.000 157933 3 1.483 76.615 100.000 178008 4 0.686 83.266 100.000 177567 5 0.288 86.236 100.000 169033 6 0.151 88.381 100.000 176679 7 0.090 90.099 100.000 166485 8 0.057 91.000 100.000 171279 9 0.135 92.472 100.000 175907 10 0.028 93.237 100.000 171838 11 0.023 94.147 100.000 175174 12 0.022 94.729 100.000 155840 13 0.021 95.248 100.000 161975 14 0.021 95.485 100.000 168029 15 0.019 95.980 100.000 161440 16 0.018 96.226 100.000 167550 17 0.019 96.713 100.000 172607 18 0.017 96.849 100.000 169682 19 0.017 97.026 100.000 167330 20 0.015 97.299 100.000 173145 21 0.016 97.405 100.000 173020 22 0.015 97.526 100.000 165310 23 0.014 97.704 100.000 165994 24 0.013 97.865 100.000 176089 25 0.013 98.106 100.000 172153 26 0.013 98.201 100.000 172878 27 0.013 98.241 100.000 175909 28 0.012 98.320 100.000 170099 29 0.013 98.400 100.000 175274 30 0.012 98.481 100.000 170135 31 0.012 98.521 100.000 164726 32 0.011 98.536 100.000 171204 33 0.011 98.536 100.000 163467 34 0.011 98.576 100.000 150728 35 0.011 98.696 100.000 172780 36 0.010 98.735 100.000 163459 37 0.010 98.695 100.000 162075 38 0.010 98.750 100.000 165827 39 0.010 98.790 100.000 165852 40 0.010 98.830 100.000 174490 41 0.009 98.870 100.000 165485 42 0.010 98.990 100.000 164896 43 0.009 99.045 100.000 172563 44 0.008 99.045 100.000 169908 45 0.009 99.005 100.000 152600 46 0.008 99.084 100.000 166329 47 0.009 99.084 100.000 173841 48 0.008 99.164 100.000 163433 49 0.008 99.203 100.000 162648 50 0.008 99.258 100.000 177108 51 0.009 99.298 100.000 173468 52 0.008 99.298 100.000 169904 53 0.008 99.298 100.000 171979 54 0.008 99.298 100.000 166437 55 0.008 99.298 100.000 170520 56 0.007 99.337 100.000 172712 57 0.007 99.337 100.000 174966 58 0.007 99.392 100.000 173173 59 0.008 99.392 100.000 173910 60 0.007 99.392 100.000 169447 61 0.007 99.431 100.000 161931 62 0.007 99.471 100.000 106123 63 0.007 99.471 100.000 177625 64 0.007 99.511 100.000 172946 65 0.007 99.511 100.000 173579 66 0.007 99.511 100.000 172204 67 0.007 99.550 100.000 172994 68 0.006 99.550 100.000 174403 69 0.007 99.590 100.000 173900 70 0.006 99.630 100.000 169824 71 0.007 99.630 100.000 171172 72 0.006 99.669 100.000 172633 73 0.006 99.669 100.000 159052 74 0.007 99.669 100.000 174377 75 0.007 99.669 100.000 163376 76 0.006 99.669 100.000 174366 77 0.007 99.669 100.000 175517 78 0.007 99.669 100.000 175583 79 0.006 99.669 100.000 174024 80 0.006 99.669 100.000 174381 81 0.006 99.669 100.000 177120 82 0.006 99.708 100.000 175032 83 0.006 99.708 100.000 173298 84 0.007 99.708 100.000 171622 85 0.006 99.709 100.000 163705 86 0.006 99.709 100.000 175330 87 0.006 99.709 100.000 178355 88 0.006 99.709 100.000 170868 89 0.006 99.709 100.000 164401 90 0.005 99.709 100.000 173884 91 0.006 99.709 100.000 159754 92 0.006 99.709 100.000 177335 93 0.006 99.709 100.000 169868 94 0.006 99.709 100.000 168164 95 0.005 99.709 100.000 151894 96 0.006 99.709 100.000 171580 97 0.005 99.709 100.000 169471 98 0.005 99.724 100.000 156458 99 0.005 99.724 100.000 168167 100 0.006 99.724 100.000 172201 ✔ Saved model to output directory /content/drive/My Drive/Spacy/Classifier/model-final ⠙ Creating best model... ⠹ Creating best model... ⠸ Creating best model... ⠼ Creating best model... ⠴ Creating best model... ✔ Created best model /content/drive/My Drive/Spacy/Classifier/model-best
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I also tried to train without the pretrained model (ie)en_core_web_sm, The logs for that are here below. (Uncollapse to view), the results are not very different, the evaluation metrics are off the roof. We need to predict the test data and try to submit to the competition for a better picture of the model.
#collapse
%%bash
## Arguement info
# Language of text in which the Model is going to be trained
# Path to store model
# Training data json path
# Evaluation data json path
# Pipeline components that we are going to train
# Number of iterations in total
# Nummber of iterations to wait before improvement in eval accuracy
# Pretrained model to start with
# version
# Augmentation for data(2 params)
# Model Architecture for text classifier (cnn + bow)
(python -m spacy train \
en \
/content/drive/My\ Drive/Spacy/Classifier_without_using_websm \
/content/drive/My\ Drive/Spacy/train_texts.json \
/content/drive/My\ Drive/Spacy/train_texts.json \
-p "textcat" \
-n 100 \
-ne 10 \
-t2v /content/drive/My\ Drive/Spacy/Pretrained/fifty_iter/model49.bin \
-V 0.1 \
-nl 0.1 \
-ovl 0.1)✔ Created output directory: /content/drive/My Drive/Spacy/Classifier_without_using_websm Training pipeline: ['textcat'] Starting with blank model 'en' Counting training words (limit=0) Loaded pretrained tok2vec for: [] Textcat evaluation score: F1-score macro-averaged across the labels 'POSITIVE, NEGATIVE, NEUTRAL' Itn Textcat Loss Textcat Token % CPU WPS --- ------------ ------- ------- ------- 1 26.755 40.980 100.000 166278 2 5.293 65.846 100.000 172083 3 1.506 76.992 100.000 175595 4 0.695 83.314 100.000 173543 5 0.293 86.284 100.000 172609 6 0.156 88.784 100.000 171486 7 0.091 90.136 100.000 161118 8 0.056 91.761 100.000 156752 9 0.112 92.442 100.000 167948 10 0.028 93.329 100.000 162446 11 0.024 94.144 100.000 165753 12 0.022 95.206 100.000 168336 13 0.021 95.769 100.000 161408 14 0.020 96.150 100.000 162562 15 0.019 96.474 100.000 163309 16 0.018 96.775 100.000 168399 17 0.018 97.140 100.000 169412 18 0.017 97.357 100.000 171364 19 0.017 97.503 100.000 172552 20 0.016 97.584 100.000 167923 21 0.016 97.678 100.000 168228 22 0.015 97.934 100.000 158830 23 0.014 98.055 100.000 170587 24 0.013 98.216 100.000 161772 25 0.014 98.256 100.000 160948 26 0.013 98.296 100.000 163401 27 0.013 98.391 100.000 168392 28 0.012 98.351 100.000 162147 29 0.012 98.391 100.000 171460 30 0.012 98.511 100.000 171279 31 0.012 98.511 100.000 161304 32 0.011 98.511 100.000 171576 33 0.011 98.511 100.000 171248 34 0.011 98.591 100.000 166902 35 0.010 98.710 100.000 164750 36 0.011 98.830 100.000 164097 37 0.011 98.790 100.000 170317 38 0.010 98.790 100.000 163521 39 0.010 98.830 100.000 162378 40 0.009 98.964 100.000 164281 41 0.009 98.964 100.000 173645 42 0.011 99.004 100.000 165681 43 0.009 99.044 100.000 165916 44 0.009 99.044 100.000 168503 45 0.008 99.044 100.000 166608 46 0.008 99.123 100.000 170394 47 0.009 99.084 100.000 171932 48 0.008 99.124 100.000 172888 49 0.009 99.084 100.000 169469 50 0.009 99.084 100.000 167170 51 0.008 99.084 100.000 169762 52 0.008 99.124 100.000 166178 53 0.008 99.124 100.000 161415 54 0.008 99.164 100.000 164241 55 0.008 99.164 100.000 172629 56 0.008 99.164 100.000 164923 57 0.008 99.243 100.000 160153 58 0.007 99.243 100.000 171699 59 0.007 99.283 100.000 165604 60 0.008 99.323 100.000 161672 61 0.007 99.362 100.000 157016 62 0.007 99.417 100.000 171005 63 0.007 99.417 100.000 168709 64 0.007 99.417 100.000 170886 65 0.007 99.417 100.000 164144 66 0.007 99.417 100.000 154789 67 0.007 99.417 100.000 162214 68 0.006 99.457 100.000 164467 69 0.006 99.457 100.000 169052 70 0.006 99.496 100.000 168125 71 0.007 99.496 100.000 164085 72 0.006 99.575 100.000 163078 73 0.006 99.575 100.000 162955 74 0.006 99.575 100.000 166206 75 0.007 99.575 100.000 164477 76 0.006 99.575 100.000 169814 77 0.006 99.575 100.000 162547 78 0.006 99.575 100.000 168980 79 0.007 99.575 100.000 172534 80 0.006 99.575 100.000 161797 81 0.007 99.575 100.000 162510 82 0.006 99.575 100.000 172787 83 0.005 99.535 100.000 159187 84 0.006 99.535 100.000 168200 85 0.005 99.614 100.000 167757 86 0.006 99.614 100.000 158842 87 0.006 99.654 100.000 166849 88 0.005 99.654 100.000 162507 89 0.006 99.654 100.000 167156 90 0.005 99.654 100.000 97872 91 0.006 99.654 100.000 162397 92 0.006 99.708 100.000 168693 93 0.005 99.708 100.000 167645 94 0.005 99.708 100.000 163485 95 0.006 99.708 100.000 171732 96 0.005 99.708 100.000 165686 97 0.005 99.708 100.000 167604 98 0.005 99.708 100.000 166435 99 0.005 99.708 100.000 161645 100 0.005 99.708 100.000 171467 ✔ Saved model to output directory /content/drive/My Drive/Spacy/Classifier_without_using_websm/model-final ⠙ Creating best model... ⠹ Creating best model... ⠸ Creating best model... ✔ Created best model /content/drive/My Drive/Spacy/Classifier_without_using_websm/model-best
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test_data = pd.read_csv(DATA_PATH/'test.csv', index_col=0)
test_data.head()| original_text | lang | retweet_count | original_author | |
|---|---|---|---|---|
| id | ||||
| 1.246628e+18 | 3. Yeah, I once cooked potatoes when I was 3 y... | en | 0 | LToddWood |
| 1.245898e+18 | Happy Mother's Day to all the mums, step-mums,... | en | 0 | iiarushii |
| 1.244717e+18 | I love the people from the UK, however, when I... | en | 0 | andreaanderegg |
| 1.245730e+18 | Happy 81st Birthday Happy Mother’s Day to my m... | en | 1 | TheBookTweeters |
| 1.244636e+18 | Happy Mothers day to all those wonderful mothe... | en | 0 | andreaanderegg |
We will clean the test data of links with regex as well.
test_data['original_text'].replace(
# Regex pattern to match : the text to replace with
{'(https?:\/\/.*|pic.*)[\r\n]*' : ''},
regex=True, inplace=True)test_data.shape(1387, 4)list_of_test_texts = test_data['original_text'].tolist()Let’s load the Spacy model from our training
import spacy
textcat_mod = spacy.load(DRIVE_PATH.parent/'Classifier/model-best')I will try to fasten the prediction by using multithreading as mentioned here
d = textcat_mod(list_of_test_texts[0])
d.cats{'NEGATIVE': 0.020245620980858803,
'NEUTRAL': 0.9727445840835571,
'POSITIVE': 0.007009787950664759}max(d.cats, key=lambda x: d.cats[x])'NEUTRAL'# to facilitate mapping the predictions
label_map = {'POSITIVE':1, 'NEGATIVE':-1, 'NEUTRAL':0}# to gather predictions
preds = []
for doc in textcat_mod.pipe(list_of_test_texts, n_threads=4, batch_size=100):
pred_cls = max(doc.cats, key=lambda x: doc.cats[x])
preds.append(label_map[pred_cls])len(preds), len(list_of_test_texts)(1387, 1387)Let’s form the submission
sub_df = pd.DataFrame(
preds,
index=test_data.index,
columns=['sentiment_class']
)sub_df.shape(1387, 1)sub_df.head()| sentiment_class | |
|---|---|
| id | |
| 1.246628e+18 | 0 |
| 1.245898e+18 | 0 |
| 1.244717e+18 | -1 |
| 1.245730e+18 | -1 |
| 1.244636e+18 | 0 |
sub_df.to_csv(DRIVE_PATH.parent/'submission.csv')The submitted predictions scored a mere 39/100 in weighted f1-score, that’s disappointing. -_-
Let’s analyze the predictions
sub_df['sentiment_class'].value_counts().plot(kind='bar')
sub_df['sentiment_class'].value_counts() 0 847
1 277
-1 263
Name: sentiment_class, dtype: int64This looks very similar to the train data
train_data['sentiment_class'].value_counts() 0 1701
-1 769
1 765
Name: sentiment_class, dtype: int64What would have gone wrong?, I guess what I can do is try another method(traditional). Coming up in another post