Add new SentenceTransformer model.

README.md

@@ -52,18 +52,18 @@ Relevant Argument Properties (WRAP) into the embedding space.
 
 ## Class Semantics
 
-The model, to some degree, captures the semantics of the critical components of an argument, as defined by the [Cambridge Dictionary](https://dictionary.cambridge.org).
+WRAPresentations, to some degree, captures the semantics of the critical components of an argument (inference and information), as defined by the[Cambridge Dictionary](https://dictionary.cambridge.org).
 It encodes *inference* as *a guess that one makes or an opinion formed based on available information*, and it also leverages the definition of
 *information* as *facts or details about a person, company, product, etc.*.
 
 Consequently, it has also learned the semantics of:
 
 * *Statement*, which refers to unique cases where only the *inference* is presented as *something that someone says or writes officially, or an
-action done to express an opinion* (see ex. 1).
+action done to express an opinion*.
 * *Reason*, which represents a full argument where the *inference* is based on direct *information* mentioned in the tweet, such as a
-source-reference or quotation, and thus reveals the author’s motivation *to try to understand and to make judgments based on practical facts* (see ex. 2).
-* *Notification*, which refers to a tweet that limits itself to providing *information*, such as media channels promoting their latest articles (see ex. 3).
-* *None*, a tweet that provides neither *inference* nor *information* (see ex. 4).
+source-reference or quotation, and thus reveals the author’s motivation *to try to understand and to make judgments based on practical facts*.
+* *Notification*, which refers to a tweet that limits itself to providing *information*, such as media channels promoting their latest articles.
+* *None*, a tweet that provides neither *inference* nor *information*.
 
 In its entirety, WRAPresentations encodes the following hierarchy for tweets:
 
@@ -199,16 +199,16 @@ WRAPresentation model using the
 | Model               | Accuracy | Precision | Recall | F1     | Support |
 |---------------------|----------|-----------|--------|--------|---------|
 | vinai/bertweet-base | 60.62%   | 50.08%    | 99.89% | 66.71% | 86,142  |
-| WRAPresentations    | 71.56%   | 65.70%    | 83.20% | 73.42% | 86,142  |
+| WRAPresentations    | 72.25%   | 65.45%    | 88.21% | 75.14% | 86,142  |
 
 An evaluation was conducted on previously unseen data from the holdout topic abortion, resulting in the model achieving a sophisticated macro
-F1-score of 71.56%. The recall, which stands at 87.54%, indicates the model's ability to capture subtle tweet patterns and class-specific features for
-Reason, Statement, Notification, and None. Despite having a lower precision of 65.70%, the model's primary focus is on prioritizing recall to capture
+F1-score of 75.14%. The recall, which stands at 88.21%, indicates the model's ability to capture subtle tweet patterns and class-specific features for
+Reason, Statement, Notification, and None. Despite having a lower precision of 65.45%, the model's primary focus is on prioritizing recall to capture
 relevant instances. Fine-tuning precision can be addressed in a subsequent classification phase, when using this model
 for `AutoModelForSequenceClassification`. In contrast, the baseline model (*vinai/bertweet-base*) achieved an exceptional recall of 99.89%, but it 
 comes with a precision trade-off (50.08%), possibly indicating overfitting. However, WRAPresentations demonstrated its ability to effectively 
 distinguish between tweets of the argument framework, capturing intra-class semantics while discerning inter-class semantics.
-This is indicated by its better F1 score of 71.56%, showcasing a superior balance between recall and precision.
+This is indicated by its better F1 score of 75.14%, showcasing a superior balance between recall and precision.
 As a result, WRAPresentations proves to be more suitable for argument mining on Twitter, as it achieves a more reliable performance in identifying 
 relevant instances in the data.
 

binary_classification_evaluation_baseline-heldout_results.csv

@@ -1,2 +1,2 @@
 epoch,steps,cossim_accuracy,cossim_accuracy_threshold,cossim_f1,cossim_precision,cossim_recall,cossim_f1_threshold,cossim_ap,manhattan_accuracy,manhattan_accuracy_threshold,manhattan_f1,manhattan_precision,manhattan_recall,manhattan_f1_threshold,manhattan_ap,euclidean_accuracy,euclidean_accuracy_threshold,euclidean_f1,euclidean_precision,euclidean_recall,euclidean_f1_threshold,euclidean_ap,dot_accuracy,dot_accuracy_threshold,dot_f1,dot_precision,dot_recall,dot_f1_threshold,dot_ap
--1,-1,0.606235997012696,0.7620757818222046,0.6671155668611901,0.5007862812640408,0.9988797610156833,0.2958066463470459,0.632370163297349,0.6153286034353995,72.18704223632812,0.6850335895837661,0.5444897060442585,0.9233756534727409,87.33160400390625,0.6237689103378412,0.6188946975354742,3.8260855674743652,0.6802268387246109,0.5552144341583223,0.8778939507094847,4.539368152618408,0.6216050521813652,0.5073562359970127,15.957334518432617,0.6667997405060133,0.5006744604316546,0.9979088872292756,9.347124099731445,0.5036378545091198
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binary_classification_evaluation_fine-tune-heldout_results.csv

@@ -1,2 +1,2 @@
 epoch,steps,cossim_accuracy,cossim_accuracy_threshold,cossim_f1,cossim_precision,cossim_recall,cossim_f1_threshold,cossim_ap,manhattan_accuracy,manhattan_accuracy_threshold,manhattan_f1,manhattan_precision,manhattan_recall,manhattan_f1_threshold,manhattan_ap,euclidean_accuracy,euclidean_accuracy_threshold,euclidean_f1,euclidean_precision,euclidean_recall,euclidean_f1_threshold,euclidean_ap,dot_accuracy,dot_accuracy_threshold,dot_f1,dot_precision,dot_recall,dot_f1_threshold,dot_ap
--1,-1,0.7155713218820015,0.5635744333267212,0.7342164228285225,0.6569760584974643,0.8320388349514564,0.4611433148384094,0.7701340554755384,0.7225914861837192,166.821533203125,0.7399493229612176,0.651601716835792,0.8560119492158327,203.3678741455078,0.774587176530623,0.7169716206123973,8.298559188842773,0.7285230528783538,0.6809284207109235,0.7832710978342047,9.337143898010254,0.7708316235108152,0.7108849887976102,57.948116302490234,0.7276492693110648,0.6582263181749509,0.8134428678117999,41.91051483154297,0.7366228479917187
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eval/binary_classification_evaluation_fine-tune-test_results.csv

@@ -1,31 +1,31 @@
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