RiteeshGrandhi/sciclaimeval-shared-task huggingface dataset:
Figure 23: LaGoNN lite performance for all configurations and balance regimes on the Toxic Conversations dataset. The relevant balance is in the title of each panel.; LaGoNN lite performance for all configurations and balance regimes on the Toxic Conversations dataset. The relevant balance is in the title of each panel.
Figure 24: LaGoNN lite performance for all configurations and balance regimes on the Hate Speech Offensive dataset. The relevant balance is in the title of each panel.; LaGoNN lite performance for all configurations and balance regimes on the Hate Speech Offensive dataset. The relevant balance is in the title of each panel.
Figure 25: LaGoNN lite performance for all configurations and balance regimes on the LIAR dataset. The relevant balance is in the title of each panel.; LaGoNN lite performance for all configurations and balance regimes on the LIAR dataset. The relevant balance is in the title of each panel.
Figure 26: LaGoNN exp performance for all configurations and balance regimes on the Insincere Questions dataset. The relevant balance is in the title of each panel.; LaGoNN exp performance for all configurations and balance regimes on the Insincere Questions dataset. The relevant balance is in the title of each panel.
Figure 27: LaGoNN exp performance for all configurations and balance regimes on the Amazon Counterfactual dataset. The relevant balance is in the title of each panel.; LaGoNN exp performance for all configurations and balance regimes on the Amazon Counterfactual dataset. The relevant balance is in the title of each panel.
Figure 28: LaGoNN exp performance for all configurations and balance regimes on the Toxic Conversations dataset. The relevant balance is in the title of each panel.; LaGoNN exp performance for all configurations and balance regimes on the Toxic Conversations dataset. The relevant balance is in the title of each panel.
Figure 29: LaGoNN exp performance for all configurations and balance regimes on the Hate Speech Offensive dataset. The relevant balance is in the title of each panel.; LaGoNN exp performance for all configurations and balance regimes on the Hate Speech Offensive dataset. The relevant balance is in the title of each panel.
Figure 30: LaGoNN exp performance for all configurations and balance regimes on the LIAR dataset. The relevant balance is in the title of each panel.; LaGoNN exp performance for all configurations and balance regimes on the LIAR dataset. The relevant balance is in the title of each panel.
Figure 31: LabDist results for one to five neighbors under the LaGoNN lite fine-tuning strategy on the Insincere Questions dataset. The relevant balance is in the title of each panel.; LabDist results for one to five neighbors under the LaGoNN lite fine-tuning strategy on the Insincere Questions dataset. The relevant balance is in the title of each panel.
Figure 32: LabDist results for one to five neighbors under the LaGoNN lite fine-tuning strategy on the the Amazon Counterfactual dataset. The relevant balance is in the title of each panel.; LabDist results for one to five neighbors under the LaGoNN lite fine-tuning strategy on the the Amazon Counterfactual dataset. The relevant balance is in the title of each panel.
Figure 33: LabDist results for one to five neighbors under the LaGoNN lite fine-tuning strategy on the Toxic Conversations dataset. The relevant balance is in the title of each panel.; LabDist results for one to five neighbors under the LaGoNN lite fine-tuning strategy on the Toxic Conversations dataset. The relevant balance is in the title of each panel.
Figure 34: LabDist results for one to five neighbors under the LaGoNN lite fine-tuning strategy on the Hate Speech Offensive dataset. The relevant balance is in the title of each panel.; LabDist results for one to five neighbors under the LaGoNN lite fine-tuning strategy on the Hate Speech Offensive dataset. The relevant balance is in the title of each panel.
Figure 35: LabDist results for one to five neighbors under the LaGoNN lite fine-tuning strategy on the LIAR dataset. The relevant balance is in the title of each panel.; LabDist results for one to five neighbors under the LaGoNN lite fine-tuning strategy on the LIAR dataset. The relevant balance is in the title of each panel.
Figure 36: DISTANCE results for one to five neighbors under the LaGoNN lite fine-tuning strategy on the the Insincere Questions dataset. The relevant balance is in the title of each panel.; DISTANCE results for one to five neighbors under the LaGoNN lite fine-tuning strategy on the the Insincere Questions dataset. The relevant balance is in the title of each panel.
Figure 37: DISTANCE results for one to five neighbors under the LaGoNN lite fine-tuning strategy on the Amazon Counterfactual dataset. The relevant balance is in the title of each panel.; DISTANCE results for one to five neighbors under the LaGoNN lite fine-tuning strategy on the Amazon Counterfactual dataset. The relevant balance is in the title of each panel.
Figure 38: DISTANCE results for one to five neighbors under the LaGoNN lite fine-tuning strategy on the Toxic Conversations dataset. The relevant balance is in the title of each panel.; DISTANCE results for one to five neighbors under the LaGoNN lite fine-tuning strategy on the Toxic Conversations dataset. The relevant balance is in the title of each panel.
Figure 39: DISTANCE results for one to five neighbors under the LaGoNN lite fine-tuning strategy on the Hate Speech Offensive dataset. The relevant balance is in the title of each panel.; DISTANCE results for one to five neighbors under the LaGoNN lite fine-tuning strategy on the Hate Speech Offensive dataset. The relevant balance is in the title of each panel.
Figure 40: DISTANCE results for one to five neighbors under the LaGoNN lite fine-tuning strategy on the LIAR dataset. The relevant balance is in the title of each panel.; DISTANCE results for one to five neighbors under the LaGoNN lite fine-tuning strategy on the LIAR dataset. The relevant balance is in the title of each panel.
Figure 41: LABEL results for one to five neighbors under the LaGoNN lite fine-tuning strategy on the Insincere Questions dataset. The relevant balance is in the title of each panel.; LABEL results for one to five neighbors under the LaGoNN lite fine-tuning strategy on the Insincere Questions dataset. The relevant balance is in the title of each panel.
Figure 42: LABEL results for one to five neighbors under the LaGoNN lite fine-tuning strategy on the Amazon Counterfactual dataset. The relevant balance is in the title of each panel.; LABEL results for one to five neighbors under the LaGoNN lite fine-tuning strategy on the Amazon Counterfactual dataset. The relevant balance is in the title of each panel.
Figure 43: LABEL results for one to five neighbors under the LaGoNN lite fine-tuning strategy on the Toxic Conversations dataset. The relevant balance is in the title of each panel.; LABEL results for one to five neighbors under the LaGoNN lite fine-tuning strategy on the Toxic Conversations dataset. The relevant balance is in the title of each panel.
Figure 44: LABEL results for one to five neighbors under the LaGoNN lite fine-tuning strategy on the Hate Speech Offensive dataset. The relevant balance is in the title of each panel.; LABEL results for one to five neighbors under the LaGoNN lite fine-tuning strategy on the Hate Speech Offensive dataset. The relevant balance is in the title of each panel.
Figure 45: LABEL results for one to five neighbors under the LaGoNN lite fine-tuning strategy on the LIAR dataset. The relevant balance is in the title of each panel.; LABEL results for one to five neighbors under the LaGoNN lite fine-tuning strategy on the LIAR dataset. The relevant balance is in the title of each panel.
Figure 46: TEXT results for one to five neighbors under the LaGoNN lite fine-tuning strategy on the Insincere Questions dataset. The relevant balance is in the title of each panel.; TEXT results for one to five neighbors under the LaGoNN lite fine-tuning strategy on the Insincere Questions dataset. The relevant balance is in the title of each panel.
Figure 47: TEXT results for one to five neighbors under the LaGoNN lite fine-tuning strategy on the Amazon Counterfactual dataset. The relevant balance is in the title of each panel.; TEXT results for one to five neighbors under the LaGoNN lite fine-tuning strategy on the Amazon Counterfactual dataset. The relevant balance is in the title of each panel.
Figure 48: TEXT results for one to five neighbors under the LaGoNN lite fine-tuning strategy on the Toxic Conversations dataset. The relevant balance is in the title of each panel.; TEXT results for one to five neighbors under the LaGoNN lite fine-tuning strategy on the Toxic Conversations dataset. The relevant balance is in the title of each panel.
Figure 49: TEXT results for one to five neighbors under the LaGoNN lite fine-tuning strategy on the Hate Speech Offensive dataset. The relevant balance is in the title of each panel.; TEXT results for one to five neighbors under the LaGoNN lite fine-tuning strategy on the Hate Speech Offensive dataset. The relevant balance is in the title of each panel.
Figure 50: TEXT results for one to five neighbors under the LaGoNN lite fine-tuning strategy on the LIAR dataset. The relevant balance is in the title of each panel.; TEXT results for one to five neighbors under the LaGoNN lite fine-tuning strategy on the LIAR dataset. The relevant balance is in the title of each panel.
Figure 51: ALL results for one to five neighbors under the LaGoNN lite fine-tuning strategy on the Insincere Questions dataset. The relevant balance is in the title of each panel.; ALL results for one to five neighbors under the LaGoNN lite fine-tuning strategy on the Insincere Questions dataset. The relevant balance is in the title of each panel.
Figure 52: ALL results for one to five neighbors under the LaGoNN lite fine-tuning strategy on the Amazon Counterfactual dataset. The relevant balance is in the title of each panel.; ALL results for one to five neighbors under the LaGoNN lite fine-tuning strategy on the Amazon Counterfactual dataset. The relevant balance is in the title of each panel.
Figure 53: ALL results for one to five neighbors under the LaGoNN lite fine-tuning strategy on the Toxic Conversations dataset. The relevant balance is in the title of each panel.; ALL results for one to five neighbors under the LaGoNN lite fine-tuning strategy on the Toxic Conversations dataset. The relevant balance is in the title of each panel.
Figure 54: ALL results for one to five neighbors under the LaGoNN lite fine-tuning strategy on the Hate Speech Offensive dataset. The relevant balance is in the title of each panel.; ALL results for one to five neighbors under the LaGoNN lite fine-tuning strategy on the Hate Speech Offensive dataset. The relevant balance is in the title of each panel.
Figure 55: ALL results for one to five neighbors under the LaGoNN lite fine-tuning strategy on the LIAR dataset. The relevant balance is in the title of each panel.; ALL results for one to five neighbors under the LaGoNN lite fine-tuning strategy on the LIAR dataset. The relevant balance is in the title of each panel.
Figure 56: LabDist results for one to five neighbors under the LaGoNN lite fine-tuning strategy over all six general classification datasets. Results are for the balanced sampling regime and the measure is average precision for IMDB, macro-F1 elsewhere.; LabDist results for one to five neighbors under the LaGoNN lite fine-tuning strategy over all six general classification datasets. Results are for the balanced sampling regime and the measure is average precision for IMDB, macro-F1 elsewhere.
Figure 57: TEXT results for one to five neighbors under the LaGoNN lite fine-tuning strategy over all six general classification datasets. Results are for the balanced sampling regime and the measure is average precision for IMDB, macro-F1 elsewhere.; TEXT results for one to five neighbors under the LaGoNN lite fine-tuning strategy over all six general classification datasets. Results are for the balanced sampling regime and the measure is average precision for IMDB, macro-F1 elsewhere.
Figure 58: LabDist results for one to five neighbors under the LaGoNN exp fine-tuning strategy over all six general classification datasets. Results are for the balanced sampling regime and the measure is average precision for IMDB, macro-F1 elsewhere.; LabDist results for one to five neighbors under the LaGoNN exp fine-tuning strategy over all six general classification datasets. Results are for the balanced sampling regime and the measure is average precision for IMDB, macro-F1 elsewhere.
Figure 59: TEXT results for one to five neighbors under the LaGoNN exp fine-tuning strategy over all six general classification datasets. Results are for the balanced sampling regime and the measure is average precision for IMDB, macro-F1 elsewhere.; TEXT results for one to five neighbors under the LaGoNN exp fine-tuning strategy over all six general classification datasets. Results are for the balanced sampling regime and the measure is average precision for IMDB, macro-F1 elsewhere.
Figure 60: LaGoNN lite performance when considering different rounding precisions for the Euclidean distance before appending it to a modified instance. We consider all balance regimes on the Insincere Questions dataset and the relevant balance is in the title of each panel.; LaGoNN lite performance when considering different rounding precisions for the Euclidean distance before appending it to a modified instance. We consider all balance regimes on the Insincere Questions dataset and the relevant balance is in the title of each panel.
Figure 61: LaGoNN lite performance when considering different rounding precisions for the Euclidean distance before appending it to a modified instance. We consider all balance regimes on the Amazon Counterfactual dataset and the relevant balance is in the title of each panel.; LaGoNN lite performance when considering different rounding precisions for the Euclidean distance before appending it to a modified instance. We consider all balance regimes on the Amazon Counterfactual dataset and the relevant balance is in the title of each panel.
Figure 62: LaGoNN lite performance when considering different rounding precisions for the Euclidean distance before appending it to a modified instance. We consider all balance regimes on the Toxic Conversations dataset and the relevant balance is in the title of each panel.; LaGoNN lite performance when considering different rounding precisions for the Euclidean distance before appending it to a modified instance. We consider all balance regimes on the Toxic Conversations dataset and the relevant balance is in the title of each panel.
Figure 63: LaGoNN lite performance when considering different rounding precisions for the Euclidean distance before appending it to a modified instance. We consider all balance regimes on the Hate Speech Offensive dataset and the relevant balance is in the title of each panel.; LaGoNN lite performance when considering different rounding precisions for the Euclidean distance before appending it to a modified instance. We consider all balance regimes on the Hate Speech Offensive dataset and the relevant balance is in the title of each panel.
Figure 64: LaGoNN lite performance when considering different rounding precisions for the Euclidean distance before appending it to a modified instance. We consider all balance regimes on the LIAR dataset and the relevant balance is in the title of each panel.; LaGoNN lite performance when considering different rounding precisions for the Euclidean distance before appending it to a modified instance. We consider all balance regimes on the LIAR dataset and the relevant balance is in the title of each panel.
Figure 65: LaGoNN lite performance where we compare the LabDist against LABEL , LabRound , and rounding the distance to the nearest whole number.
We consider all balance regimes on the Insincere Questions dataset and the relevant balance is in the title of each panel.; LaGoNN lite performance where we compare the LabDist against LABEL , LabRound , and rounding the distance to the nearest whole number.
We consider all balance regimes on the Insincere Questions dataset and the relevant balance is in the title of each panel.
Figure 66: LaGoNN lite performance where we compare the LabDist against LABEL , LabRound , and rounding the distance to the nearest whole number.
We consider all balance regimes on the Amazon Counterfactual dataset and the relevant balance is in the title of each panel.; LaGoNN lite performance where we compare the LabDist against LABEL , LabRound , and rounding the distance to the nearest whole number.
We consider all balance regimes on the Amazon Counterfactual dataset and the relevant balance is in the title of each panel.
Figure 67: LaGoNN lite performance where we compare the LabDist against LABEL , LabRound , and rounding the distance to the nearest whole number.
We consider all balance regimes on the Toxic Conversations dataset and the relevant balance is in the title of each panel.; LaGoNN lite performance where we compare the LabDist against LABEL , LabRound , and rounding the distance to the nearest whole number.
We consider all balance regimes on the Toxic Conversations dataset and the relevant balance is in the title of each panel.
Figure 68: LaGoNN lite performance where we compare the LabDist against LABEL , LabRound , and rounding the distance to the nearest whole number. We consider all balance regimes on the Hate Speech Offensive dataset and the relevant balance is in the title of each panel.; LaGoNN lite performance where we compare the LabDist against LABEL , LabRound , and rounding the distance to the nearest whole number. We consider all balance regimes on the Hate Speech Offensive dataset and the relevant balance is in the title of each panel.
Figure 69: LaGoNN lite performance where we compare the LabDist against LABEL , LabRound , and rounding the distance to the nearest whole number.
We consider all balance regimes on the LIAR dataset and the relevant balance is in the title of each panel.; LaGoNN lite performance where we compare the LabDist against LABEL , LabRound , and rounding the distance to the nearest whole number.
We consider all balance regimes on the LIAR dataset and the relevant balance is in the title of each panel.
Toy training and test data and different LaGoNN configurations considering the first training example. Text is in quotation marks and the integer label is in parenthesis. In brackets are the gold label or distance from the NN or both. Train and Test Modified are altered instances that are input into the final embedding model for training and inference, respectively. The input format is " original text [SEP] [(NN gold) (label distance)] NN training instance text ". See Appendix A.10 for examples of LaGoNN ALL modified text.
Speed comparison between LaGoNN LabDist with one neighbor and comparable methods. Time includes training on 1,000 examples and inference on 51,000 examples.