adding example 4

docs/source/data_transformations.rst

@@ -15,7 +15,7 @@ Sample transform functions
 ^^^^^^^^^^^^^^^^^^^^^^^^^^
 .. automodule:: utils.tranform_functions
     :members: snips_intent_ner_to_tsv, snli_entailment_to_tsv,create_fragment_detection_tsv,
-        msmarco_answerability_detection_to_tsv, bio_ner_to_tsv, msmarco_query_type_to_tsv, qqp_query_similarity_to_tsv
+        msmarco_answerability_detection_to_tsv, msmarco_query_type_to_tsv, bio_ner_to_tsv, msmarco_query_type_to_tsv, qqp_query_similarity_to_tsv
 
 Your own transform function
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^

docs/source/shared_encoder.rst

@@ -79,5 +79,5 @@ For evaluating the performance on dev and test sets during training, we provide
 
 .. automodule:: utils.eval_metrics
     :members: classification_accuracy, classification_f1_score, seqeval_f1_score, 
-        seqeval_precision, seqeval_recall, snips_f1_score, snips_precision, snips_recall
+        seqeval_precision, seqeval_recall, snips_f1_score, snips_precision, snips_recall, classification_recall_score
 

examples/entailment_detection/entailment_snli.ipynb

@@ -110,7 +110,7 @@
     "!python ../../data_preparation.py \\\n",
     "    --task_file 'tasks_file_snli.yml' \\\n",
     "    --data_dir '../../data' \\\n",
-    "    --max_seq_len 384"
+    "    --max_seq_len 128"
    ]
   },
   {
@@ -135,13 +135,13 @@
     "    --task_file 'tasks_file_snli.yml' \\\n",
     "    --out_dir 'snli_entailment_bert_base' \\\n",
     "    --epochs 3 \\\n",
-    "    --train_batch_size 8 \\\n",
-    "    --eval_batch_size 16 \\\n",
-    "    --grad_accumulation_steps 2 \\\n",
-    "    --log_per_updates 50 \\\n",
+    "    --train_batch_size 64 \\\n",
+    "    --eval_batch_size 64 \\\n",
+    "    --grad_accumulation_steps 1 \\\n",
+    "    --log_per_updates 100 \\\n",
     "    --eval_while_train True \\\n",
     "    --test_while_train True \\\n",
-    "    --max_seq_len 384 \\\n",
+    "    --max_seq_len 128 \\\n",
     "    --silent True "
    ]
   },

examples/query_type_detection/query_type_detection.ipynb

@@ -0,0 +1,97 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!wget https://msmarco.blob.core.windows.net/msmarco/train_v2.1.json.gz -P msmarco_qna_data\n",
+    "!wget https://msmarco.blob.core.windows.net/msmarco/dev_v2.1.json.gz -P msmarco_qna_data\n",
+    "!wget https://msmarco.blob.core.windows.net/msmarco/eval_v2.1_public.json.gz -P msmarco_qna_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!gunzip msmarco_qna_data/train_v2.1.json.gz\n",
+    "!gunzip msmarco_qna_data/dev_v2.1.json.gz\n",
+    "!gunzip msmarco_qna_data/eval_v2.1_public.json.gz"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!python ../../data_transformations.py \\\n",
+    "    --transform_file 'transform_file_querytype.yml'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!python ../../data_preparation.py \\\n",
+    "    --task_file 'tasks_file_querytype.yml' \\\n",
+    "    --data_dir '../../data' \\\n",
+    "    --max_seq_len 60"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!python ../../train.py \\\n",
+    "    --data_dir '../../data/bert-base-uncased_prepared_data' \\\n",
+    "    --task_file 'tasks_file_querytype.yml' \\\n",
+    "    --out_dir 'msmarco_querytype_bert_base' \\\n",
+    "    --epochs 3 \\\n",
+    "    --train_batch_size 64 \\\n",
+    "    --eval_batch_size 64 \\\n",
+    "    --grad_accumulation_steps 1 \\\n",
+    "    --log_per_updates 100 \\\n",
+    "    --eval_while_train True \\\n",
+    "    --test_while_train True \\\n",
+    "    --max_seq_len 60 \\\n",
+    "    --silent True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

examples/query_type_detection/tasks_file_querytype.yml

@@ -0,0 +1,18 @@
+querytype:
+    model_type: BERT
+    config_name: bert-base-uncased
+    dropout_prob: 0.2
+    label_map_or_file:
+    - DESCRIPTION
+    - ENTITY
+    - LOCATION
+    - NUMERIC
+    - PERSON
+    metrics:
+    - classification_accuracy
+    loss_type: CrossEntropyLoss
+    task_type: SingleSenClassification
+    file_names:
+    - querytype_train_v2.1.tsv
+    - querytype_dev_v2.1.tsv
+    - querytype_eval_v2.1_public.tsv

examples/query_type_detection/transform_file_querytype.yml

@@ -0,0 +1,11 @@
+transform1:
+  transform_func: msmarco_query_type_to_tsv
+  transform_params:
+    data_frac : 0.2
+  read_file_names:
+    - train_v2.1.json
+    - dev_v2.1.json
+    - eval_v2.1_public.json
+
+  read_dir: msmarco_qna_data
+  save_dir: ../../data

utils/data_utils.py

@@ -25,7 +25,8 @@
     "seqeval_recall" : seqeval_recall,
     "snips_f1_score" : snips_f1_score,
     "snips_precision" : snips_precision,
-    "snips_recall" : snips_recall
+    "snips_recall" : snips_recall,
+    "classification_recall" : classification_recall
 }
 
 TRANSFORM_FUNCS = {
@@ -37,7 +38,8 @@
     "msmarco_query_type_to_tsv" : msmarco_query_type_to_tsv,
     "imdb_sentiment_data_to_tsv" : imdb_sentiment_data_to_tsv,
     "qqp_query_similarity_to_tsv" : qqp_query_similarity_to_tsv,
-    "msmarco_answerability_detection_to_tsv" : msmarco_answerability_detection_to_tsv
+    "msmarco_answerability_detection_to_tsv" : msmarco_answerability_detection_to_tsv,
+    "clinc_out_of_scope_to_tsv" : clinc_out_of_scope_to_tsv
 }
 
 class ModelType(IntEnum):

utils/eval_metrics.py

@@ -1,5 +1,8 @@
-
+"""
+File for creating metric functions
+"""
 from sklearn.metrics import accuracy_score, f1_score
+from sklearn.metrics import recall_score as class_recall_score
 from seqeval.metrics import f1_score as seq_f1
 from seqeval.metrics import precision_score, recall_score
 
@@ -31,6 +34,19 @@ def classification_f1_score(yTrue, yPred):
     """
     return f1_score(yTrue, yPred, average='micro')
 
+def classification_recall(yTrue, yPred):
+    """
+    Standard recall score from sklearn for classification tasks.
+    It takes a batch of predictions and labels.
+
+    To use this metric, add **classification_f1_score** into list of ``metrics`` in task file.
+
+    Args:
+        yPred (:obj:`list`) : [0, 2, 1, 3]
+        yTrue (:obj:`list`) : [0, 1, 2, 3]
+
+    """
+    return class_recall_score(yTrue, yPred, average='micro')
 
 def seqeval_f1_score(yTrue, yPred):
     """

utils/tranform_functions.py

@@ -3,8 +3,10 @@
 import os
 import re
 import json
+import random
 import pandas as pd
 from tqdm import tqdm
+from collections import defaultdict
 from statistics import median
 from sklearn.model_selection import train_test_split
 SEED = 42
@@ -440,12 +442,12 @@ def msmarco_query_type_to_tsv(dataDir, readFile, wrtDir, transParamDict, isTrain
     #saving
     print('number of samples in final data : ', len(dfKeep))
     print('writing for file {} at {}'.format(readFile, wrtDir))
-    dfKeep.to_csv(os.path.join(wrtDir, 'querytype_{}.tsv'.format(readFile.split('.')[0])), sep='\t',
+    dfKeep.to_csv(os.path.join(wrtDir, 'querytype_{}.tsv'.format(readFile.lower().replace('.json', ''))), sep='\t',
                             index=False, header=False)
     if isTrainFile:
         allClasses = dfKeep['query_type'].unique()
         labelMap = {lab : i for i, lab in enumerate(allClasses)}
-        labelMapPath = os.path.join(wrtDir, 'querytype_{}_label_map.joblib'.format(readFile.split('.')[0]))
+        labelMapPath = os.path.join(wrtDir, 'querytype_{}_label_map.joblib'.format(readFile.lower().replace('.json', '')))
         joblib.dump(labelMap, labelMapPath)
         print('Created label map file at', labelMapPath)
 
@@ -668,4 +670,86 @@ def msmarco_answerability_detection_to_tsv(dataDir, readFile, wrtDir, transParam
     print('Dev file written at: ', os.path.join(wrtDir, 'msmarco_answerability_dev.tsv'))
 
     devDf.to_csv(os.path.join(wrtDir, 'msmarco_answerability_test.tsv'), sep='\t', index=False, header=False)
-    print('Test file written at: ', os.path.join(wrtDir, 'msmarco_answerability_test.tsv'))
+    print('Test file written at: ', os.path.join(wrtDir, 'msmarco_answerability_test.tsv'))
+
+def clinc_out_of_scope_to_tsv(dataDir, readFile, wrtDir, transParamDict, isTrainFile=False):
+
+    """
+
+    For using this transform function, set ``transform_func`` : **clinc_out_of_scope_to_tsv** in transform file.
+
+    Args:
+        dataDir (:obj:`str`) : Path to the directory where the raw data files to be read are present..
+        readFile (:obj:`str`) : This is the file which is currently being read and transformed by the function.
+        wrtDir (:obj:`str`) : Path to the directory where to save the transformed tsv files.
+        transParamDict (:obj:`dict`, defaults to :obj:`None`): Dictionary requiring the following parameters as key-value
+            
+            - ``samples_per_intent_train`` (defaults to 7) : Number of in-scope samples per intent to consider, as this data has imbalance for inscope and outscope
+
+    """
+    transParamDict.setdefault("samples_per_intent_train", 7)
+
+    print("Making data from file {} ...".format(readFile))
+    raw = json.load(open(os.path.join(dataDir, readFile)))
+
+    print('Num of train samples in-scope: ', len(raw['train']))
+    inScopeTrain = defaultdict(list)
+    for sentence, intent in raw['train']:
+        inScopeTrain[intent].append(sentence)
+
+    #sampling 
+    inscopeSampledTrain = []
+    numSamplesPerInt = 7
+    random.seed(SEED)
+    for intent in inScopeTrain:
+        inscopeSampledTrain += random.sample(inScopeTrain[intent], int(transParamDict["samples_per_intent_train"]))
+
+    print('Num of sampled train samples in-scope: ', len(inscopeSampledTrain))
+    #out of scope train
+    outscopeTrain = [sample[0] for sample in raw['oos_train']]
+    print('Num of train out-scope samples: ', len(outscopeTrain))
+
+    #train data
+    allTrain = inscopeSampledTrain + outscopeTrain
+    allTrainLabels = [1]*len(inscopeSampledTrain) + [0]*len(outscopeTrain)
+
+    #writing train data file
+    trainF = open(os.path.join(wrtDir, 'clinc_outofscope_train.tsv'), 'w')
+    for uid, (samp, lab) in enumerate(zip(allTrain, allTrainLabels)):
+        trainF.write("{}\t{}\t{}\n".format(uid, lab, samp))
+    print('Train file written at: ', os.path.join(wrtDir, 'clinc_outofscope_train.tsv'))
+    trainF.close()
+
+    #making dev set
+    inscopeDev = [sample[0] for sample in raw['val']]
+    outscopeDev = [sample[0] for sample in raw['oos_val']]
+    print('Num of val out-scope samples: ', len(outscopeDev))
+    print('Num of val in-scope samples: ', len(inscopeDev))
+
+    #allDev = inscopeDev + outscopeDev
+    allDev = outscopeDev
+    #allDevLabels = [1]*inscopeDev + [0]*outscopeDev
+    allDevLabels = [0]*len(outscopeDev)
+
+    #writing dev data file
+    devF = open(os.path.join(wrtDir, 'clinc_outofscope_dev.tsv'), 'w')
+    for uid, (samp, lab) in enumerate(zip(allDev, allDevLabels)):
+        devF.write("{}\t{}\t{}\n".format(uid, lab, samp))
+    print('Dev file written at: ', os.path.join(wrtDir, 'clinc_outofscope_dev.tsv'))
+    devF.close()
+
+    #making test set 
+    inscopeTest = [sample[0] for sample in raw['test']]
+    outscopeTest = [sample[0] for sample in raw['oos_test']]
+    print('Num of test out-scope samples: ', len(outscopeTest))
+    print('Num of test in-scope samples: ', len(inscopeTest))
+
+    allTest = inscopeTest + outscopeTest
+    allTestLabels = [1]*len(inscopeTest) + [0]*len(outscopeTest)
+
+    #writing test data file
+    testF = open(os.path.join(wrtDir, 'clinc_outofscope_test.tsv'), 'w')
+    for uid, (samp, lab) in enumerate(zip(allTest, allTestLabels)):
+        testF.write("{}\t{}\t{}\n".format(uid, lab, samp))
+    print('Test file written at: ', os.path.join(wrtDir, 'clinc_outofscope_test.tsv'))
+    testF.close()