fix loading errors; update readme

README.md

@@ -68,60 +68,49 @@ The text in the dataset is in English.
 A data point comprises a question with a `title` field containing the overview of the question and a `question` that elaborates on the title.  The answers are sentence tokenized and contain relevance labels, labels for inclusion in the final summary, and cluster labels. We include cluster summaries, overall summaries, and additional metadata. 
 
 An example from the AnswerSumm test set looks as follows:
+```json
+{
+  "example_id": 9_24,
+  "annotator_id": [1], 
+  "question": {
+    "author": "gaming.stackexchange.com/users/11/Jeffrey",
+    "forum": "gaming.stackexchange.com",
+    "link": "gaming.stackexchange.com/questions/1",
+    "question": "Now that the Engineer update has come, there will be lots of Engineers building up everywhere.  How should this best be handled?",
+    "question_tags": "\<team-fortress-2\>",
+    "title": "What is a good strategy to deal with lots of engineers turtling on the other team?"
+  }, 
+  "answers": [
+    {  
+      "answer_details": {
+        "author": "gaming.stackexchange.com/users/44/Corv1nus", 
+        "score": 49 
+      }
+      "sents": [
+        "text": "Lots of medics with lots of ubers on high-damage-dealing classes." 
+        "label": [0],
+        "label_summ": [0],
+        "cluster_id": [[-1]],
+      ]
+      ...
+    },
+    ...
+  ]
+  "summaries": [
+     [
+      "Demomen usually work best against a sentry farm. Heavies or pyros can also be effective. Medics should be in the frontline to absorb the shock. Build a teleporter to help your team through.",
+      "Demomen are best against a sentry farm. Heavies or pyros can also be effective. The medic should lead the uber combo. ..."
+     ]
+  ]
+  "cluster_summaries":[
+    "Demomen are best against a sentry farm.",
+    "Heavies or pyros can also be effective.",
+    ...
+  ]
+}
 
-{ </br>
-> "example_id": 9_24, </br>
-"annotator_id": [1], </br>
-"question": </br>
-> > {</br>
-    "author": "gaming.stackexchange.com/users/11/Jeffrey",</br>
-   "forum": "gaming.stackexchange.com",</br>
-    "link": "gaming.stackexchange.com/questions/1",</br>
-  "question": "Now that the Engineer update has come, there will be lots of Engineers building up everywhere.  How should this best be handled?",</br>
-    "question_tags": "\<team-fortress-2\>",</br>
-    "title": "What is a good strategy to deal with lots of engineers turtling on the other team?"</br>
-},</br>
-
->"answers":
-[</br>
-> >{</br>
-        "answer_details": { </br>
-            "author": "gaming.stackexchange.com/users/44/Corv1nus", </br>
-            "score": 49 </br>
-        }, </br>
-        "sents": [ 
-> > > {
-> > > "cluster_id": [[-1]],</br>
-> > >             "label": [0],</br>
-> > >              "label_summ": [0],</br>
->  > >         "text": "Lots of medics with lots of ubers on high-damage-dealing classes." </br>
-> > },</br>
-
-> > > ... </br> 
-> >  >   ] </br>
-<!-- > >  } </br> -->
-
-> > ... </br>
-> > ], </br>
-
-> "summaries": 
-[ </br>
-> >    [</br>
-        "Demomen usually work best against a sentry farm. Heavies or pyros can also be effective. Medics should be in the frontline to absorb the shock. Build a teleporter to help your team through.",</br>
-        "Demomen are best against a sentry farm. Heavies or pyros can also be effective. The medic should lead the uber combo. If possible, back the uber team(s) up with as much raw firepower as you can muster. Go with corner-edging soldiers and sneaky demos.  The Machina can help damage Engies that hide behind their sentry, but for unattended sentries, the Sydney Sleeper charges up very fast.  The best way you can help your team is by building up a teleporter, so that they can get to and take down the other team's Sentry.    "
-    </br>]
-], </br>
-
-> "cluster_summaries":
-[
-    [
-        "Demomen are best against a sentry farm.",
-        "Heavies or pyros can also be effective.",
-        ...
-    ]
-]
+```
 
-}
             
 
 ### Data Fields
@@ -136,16 +125,18 @@ An example from the AnswerSumm test set looks as follows:
 - answers: list of sentence-tokenizer answers
     - answer_details: dictionary consisting of link to answer author's user page (author) and community-assigned score (score)
     - sents: sentences that compose the answer
-            - text: the sentence text
-            - label: a list (to generalize to multi-annotator scenarios) of whether the sentence is labeled as relevant or not for answering the question. 
-             - label_summ: a list of whether the sentence was used to write the first annotator-created summary (that is the first summary in `summaries`)
-             - cluster_id: a list of lists (potentially multiple annotators and a sentence can be in potentially multiple clusters) of the clusters a sentence belongs to. -1 implies no cluster. This label can be used to aggregate sentences into clusters across answers. 
--summaries: list of list of summaries. Each annotator wrote two summaries. The first in the list is the summary in which the instructor was told to mark sentences relevant for inclusion in the summary and then closely use the words of these sentences, while for the second summary the annotator was asked to paraphrase and condense the cluster summaries but was not asked to reduce abstraction.
+      - text: the sentence text
+      - label: a list (to generalize to multi-annotator scenarios) of whether the sentence is labeled as relevant or not for answering the question. 
+      - label_summ: a list of whether the sentence was used to write the first annotator-created summary (that is the first summary in `summaries`)
+      - cluster_id: a list of lists (potentially multiple annotators and a sentence can be in potentially multiple clusters) of the clusters a sentence belongs to. -1 implies no cluster. This label can be used to aggregate sentences into clusters across answers. 
+
+- summaries: list of list of summaries. Each annotator wrote two summaries. The first in the list is the summary in which the instructor was told to mark sentences relevant for inclusion in the summary and then closely use the words of these sentences, while for the second summary the annotator was asked to paraphrase and condense the cluster summaries but was not asked to reduce abstraction.
+
 - annotator_id: a list of the ids of the annotator(s) who completed all tasks related to that thread.
--mismatch_info: a dict of any issues in processing the excel files on which annotations were completed. 
-    - rel_sent_not_in_cluster: list of booleans indicating whether there are sentences that are labeled as relevant but were not included in a cluster. 
-    - cluster_sents_not_matched: list of sentences that were found in a cluster but which our processing script didn't automatically match to sentences in the source answers. If cluster summarization is of interest to the user you may want to process these examples separately using clusters_orig. 
-    - clusters_orig: for cases in which cluster_sents_not_matched is not empty this contains the original clusters which were separated by a blank line in the Excel annotation sheet. 
+
+- mismatch_info: a dict of any issues in processing the excel files on which annotations were completed. 
+  - rel_sent_not_in_cluster: list of booleans indicating whether there are sentences that are labeled as relevant but were not included in a cluster. 
+  - cluster_sents_not_matched: list of sentences that were found in a cluster but which our processing script didn't automatically match to sentences in the source answers. If cluster summarization is of interest to the user you may want to process these examples separately using clusters_orig. 
 
 ### Data Splits
 

test.jsonl

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+size 8755413

train.jsonl

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+size 24817274

validation.jsonl

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-size 4460214
+oid sha256:ffa9fdd0d1cc612042816af9b9171c0dad6d6e99de23501316df8e5007e8b076
+size 4428855