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README.md
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@@ -56,7 +56,7 @@ The pre-processing operations used to produce the final training dataset were as
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1. Dataset is filtered based on 'medium' value in 'strategy' column (sequence length = 85).
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2. For IKITracs, labels are assigned based on the presence of certain substrings ('_unc' or '_c') based on 'parameter' values which correspond to assessments of 'unconditional' or 'conditional' commitments by human annotaters.
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3. For ClimateWatch, the 'QuestionText' field is searched for the terms 'unconditional' or 'conditional', and labels assigned accordingly.
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4. If 'context_translated' is available and the 'language' is not English, 'context' is replaced with 'context_translated'.
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5. The dataset is "exploded" - i.e., the text samples in the 'context' column, which are lists, are converted into separate rows - and labels are merged to align with the associated samples.
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6. The 'match_onanswer' and 'answerWordcount' are used conditionally to select high quality samples (prefers high % of word matches in 'match_onanswer', but will take lower if there is a high 'answerWordcount')
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7. Data is then augmented using sentence shuffle from the ```albumentations``` library (NLP methods insertion and substitution were also tried, but lowered the performance of the model and were therefore not included in the final training data). This is done to increase the number of training samples available for the Unconditional class from 774 to 1163. The end result is an equal sample per class breakdown of:
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| 56 |
1. Dataset is filtered based on 'medium' value in 'strategy' column (sequence length = 85).
|
| 57 |
2. For IKITracs, labels are assigned based on the presence of certain substrings ('_unc' or '_c') based on 'parameter' values which correspond to assessments of 'unconditional' or 'conditional' commitments by human annotaters.
|
| 58 |
3. For ClimateWatch, the 'QuestionText' field is searched for the terms 'unconditional' or 'conditional', and labels assigned accordingly.
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| 59 |
+
4. If 'context_translated' is available and the 'language' is not English, 'context' is replaced with 'context_translated'. This results in the model being trained on English translations of original text samples.
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| 60 |
5. The dataset is "exploded" - i.e., the text samples in the 'context' column, which are lists, are converted into separate rows - and labels are merged to align with the associated samples.
|
| 61 |
6. The 'match_onanswer' and 'answerWordcount' are used conditionally to select high quality samples (prefers high % of word matches in 'match_onanswer', but will take lower if there is a high 'answerWordcount')
|
| 62 |
7. Data is then augmented using sentence shuffle from the ```albumentations``` library (NLP methods insertion and substitution were also tried, but lowered the performance of the model and were therefore not included in the final training data). This is done to increase the number of training samples available for the Unconditional class from 774 to 1163. The end result is an equal sample per class breakdown of:
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