Merge pull request #560 from EleutherAI/dataset-metric-log

Dataset metric log [WIP]

lm_eval/tasks/pile/pile_pubmed-abstracts.yaml

@@ -19,4 +19,4 @@ metric_list:
     higher_is_better: false
   - metric: bits_per_byte
     aggregation: bits_per_byte
-    higher_is_better: false
\ No newline at end of file
+    higher_is_better: false

lm_eval/tasks/pile/pile_pubmed-central.yaml

@@ -19,4 +19,4 @@ metric_list:
     higher_is_better: false
   - metric: bits_per_byte
     aggregation: bits_per_byte
-    higher_is_better: false
\ No newline at end of file
+    higher_is_better: false

lm_eval/tasks/pile/pile_stackexchange.yaml

@@ -19,4 +19,4 @@ metric_list:
     higher_is_better: false
   - metric: bits_per_byte
     aggregation: bits_per_byte
-    higher_is_better: false
\ No newline at end of file
+    higher_is_better: false

lm_eval/tasks/pile/pile_ubuntu-irc.yaml

@@ -19,4 +19,4 @@ metric_list:
     higher_is_better: false
   - metric: bits_per_byte
     aggregation: bits_per_byte
-    higher_is_better: false
\ No newline at end of file
+    higher_is_better: false

lm_eval/tasks/pile/pile_uspto.yaml

@@ -19,4 +19,4 @@ metric_list:
     higher_is_better: false
   - metric: bits_per_byte
     aggregation: bits_per_byte
-    higher_is_better: false
\ No newline at end of file
+    higher_is_better: false

lm_eval/tasks/pile/pile_wikipedia.yaml

@@ -19,4 +19,4 @@ metric_list:
     higher_is_better: false
   - metric: bits_per_byte
     aggregation: bits_per_byte
-    higher_is_better: false
\ No newline at end of file
+    higher_is_better: false

lm_eval/tasks/pile/pile_youtubesubtitles.yaml

@@ -19,4 +19,4 @@ metric_list:
     higher_is_better: false
   - metric: bits_per_byte
     aggregation: bits_per_byte
-    higher_is_better: false
\ No newline at end of file
+    higher_is_better: false

lm_eval/tasks/piqa/piqa.yaml

-group: 
+group:
   - piqa_yaml_grp
 task: piqa_yaml
 dataset_path: piqa
@@ -9,7 +9,7 @@ validation_split: validation
 test_split: null
 template_aliases: "{% set question = goal %}{% set answer_choices = [sol1, sol2] %}{% set gold = label %}" # set the list of possible answer choices, and set what this doc's gold label idx is
 doc_to_text: "Question: {{question}}\nAnswer:"
-doc_to_target: "{{gold}}" # this will be cast to an int. 
+doc_to_target: "{{gold}}" # this will be cast to an int.
 metric_list:
   - metric: acc
     aggregation: mean

lm_eval/tasks/sciq/sciq.yaml

-group: 
+group:
   - sciq_yaml_grp
 task: sciq_yaml
 dataset_path: sciq
@@ -9,7 +9,7 @@ validation_split: validation
 test_split: test
 template_aliases: "{% set answer_choices = [distractor1, distractor2, distractor3, correct_answer] %}{% set gold = 3 %}" # set the list of possible answer choices, and set what this doc's gold label idx is
 doc_to_text: "{{support.lstrip()}}\nQuestion: {{question}}\nAnswer:"
-doc_to_target: "{{gold}}" # this will be cast to an int. 
+doc_to_target: "{{gold}}" # this will be cast to an int.
 metric_list:
   - metric: acc
     aggregation: mean
@@ -19,4 +19,4 @@ metric_list:
     higher_is_better: true
   - metric: acc_mutual_info
     aggregation: mean
-    higher_is_better: true
\ No newline at end of file
+    higher_is_better: true

lm_eval/tasks/super_glue/boolq/default.yaml

+group:
+  - super-glue-lm-eval-v1
+task: "default"
+dataset_path: super_glue
+dataset_name: boolq
+output_type: multiple_choice
+training_split: train
+validation_split: validation
+doc_to_text: "{{passage}}\nQuestion: {{question}}\nAnswer:"
+doc_to_target: "{{label}}" # this will be cast to an int.
+template_aliases: "{% set answer_choices = ['no', 'yes'] %}"

lm_eval/tasks/super_glue/cb/aggregate.py

+import sklearn
+import numpy as np
+
+
+def cb_multi_fi(items):
+    preds, golds = zip(*items)
+    preds = np.array(preds)
+    golds = np.array(golds)
+    f11 = sklearn.metrics.f1_score(y_true=golds == 0, y_pred=preds == 0)
+    f12 = sklearn.metrics.f1_score(y_true=golds == 1, y_pred=preds == 1)
+    f13 = sklearn.metrics.f1_score(y_true=golds == 2, y_pred=preds == 2)
+    avg_f1 = np.mean([f11, f12, f13])
+    return avg_f1

lm_eval/tasks/super_glue/cb/default.yaml

+group:
+  - super-glue-lm-eval-v1
+task: "default"
+dataset_path: super_glue
+dataset_name: cb
+output_type: multiple_choice
+training_split: train
+validation_split: validation
+doc_to_text: "{{premise}}\nQuestion: {{hypothesis}}. True, False, or Neither?\nAnswer:"
+doc_to_target: "{{label}}" # this will be cast to an int.
+template_aliases: "{% set answer_choices = ['True', 'False', 'Neither'] %}"
+metric_list:
+  - metric: acc
+  - metric: f1
+    aggregation: !function "aggregate.cb_multi_fi"

lm_eval/tasks/triviaqa.py

@@ -10,11 +10,12 @@ high quality distant supervision for answering the questions.
 Homepage: https://nlp.cs.washington.edu/triviaqa/
 """
 import inspect
+
 # import lm_eval.datasets.triviaqa.triviaqa
 import string
 from lm_eval.api.task import Task
 from lm_eval.api.instance import Instance
-from lm_eval.api.register import register_task
+from lm_eval.api.registry import register_task
 from lm_eval.api.metrics import mean
 
 _CITATION = """
@@ -29,10 +30,11 @@ _CITATION = """
 }
 """
 
+
 @register_task("triviaqa")
 class TriviaQA(Task):
     VERSION = 1
-    DATASET_PATH = "trivia_qa" #inspect.getfile(lm_eval.datasets.triviaqa.triviaqa)
+    DATASET_PATH = "trivia_qa"  # inspect.getfile(lm_eval.datasets.triviaqa.triviaqa)
     DATASET_NAME = "unfiltered.nocontext"
 
     OUTPUT_TYPE = "greedy_until"
@@ -90,18 +92,29 @@ class TriviaQA(Task):
         continuation = Instance(
             request_type=self.OUTPUT_TYPE,
             doc=doc,
-            arguments=(ctx, {
-                "until": ["\n", ".", ","], 
-                "do_sample": False, 
-            }),
+            arguments=(
+                ctx,
+                {
+                    "until": ["\n", ".", ","],
+                    "do_sample": False,
+                },
+            ),
             idx=0,
             **kwargs,
         )
         return continuation
 
     def process_results(self, doc, results):
-        continuation = results[0].strip().lower().translate(str.maketrans('', '', string.punctuation))
-        list_of_candidates = [alias.lower().translate(str.maketrans('', '', string.punctuation)) for alias in self._remove_prefixes(doc["answer"]["aliases"])]
+        continuation = (
+            results[0]
+            .strip()
+            .lower()
+            .translate(str.maketrans("", "", string.punctuation))
+        )
+        list_of_candidates = [
+            alias.lower().translate(str.maketrans("", "", string.punctuation))
+            for alias in self._remove_prefixes(doc["answer"]["aliases"])
+        ]
         return {"em": float(continuation in list_of_candidates)}
 
     def aggregation(self):
@@ -110,4 +123,4 @@ class TriviaQA(Task):
         }
 
     def higher_is_better(self):
-        return {"em": True}
\ No newline at end of file
+        return {"em": True}

lm_eval/tasks/wikitext.py

+"""
+Pointer Sentinel Mixture Models
+https://arxiv.org/pdf/1609.07843.pdf
+
+The WikiText language modeling dataset is a collection of over 100 million tokens
+extracted from the set of verified Good and Featured articles on Wikipedia.
+
+NOTE: This `Task` is based on WikiText-2.
+
+Homepage: https://www.salesforce.com/products/einstein/ai-research/the-wikitext-dependency-language-modeling-dataset/
+"""
+import re
+
+from lm_eval.api.task import PerplexityTask
+
+from lm_eval.api.registry import register_task, register_group
+
+_CITATION = """
+@misc{merity2016pointer,
+    title={Pointer Sentinel Mixture Models},
+    author={Stephen Merity and Caiming Xiong and James Bradbury and Richard Socher},
+    year={2016},
+    eprint={1609.07843},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+"""
+
+
+def wikitext_detokenizer(string):
+    # contractions
+    string = string.replace("s '", "s'")
+    string = re.sub(r"/' [0-9]/", r"/'[0-9]/", string)
+    # number separators
+    string = string.replace(" @-@ ", "-")
+    string = string.replace(" @,@ ", ",")
+    string = string.replace(" @.@ ", ".")
+    # punctuation
+    string = string.replace(" : ", ": ")
+    string = string.replace(" ; ", "; ")
+    string = string.replace(" . ", ". ")
+    string = string.replace(" ! ", "! ")
+    string = string.replace(" ? ", "? ")
+    string = string.replace(" , ", ", ")
+    # double brackets
+    string = re.sub(r"\(\s*([^\)]*?)\s*\)", r"(\1)", string)
+    string = re.sub(r"\[\s*([^\]]*?)\s*\]", r"[\1]", string)
+    string = re.sub(r"{\s*([^}]*?)\s*}", r"{\1}", string)
+    string = re.sub(r"\"\s*([^\"]*?)\s*\"", r'"\1"', string)
+    string = re.sub(r"'\s*([^']*?)\s*'", r"'\1'", string)
+    # miscellaneous
+    string = string.replace("= = = =", "====")
+    string = string.replace("= = =", "===")
+    string = string.replace("= =", "==")
+    string = string.replace(" " + chr(176) + " ", chr(176))
+    string = string.replace(" \n", "\n")
+    string = string.replace("\n ", "\n")
+    string = string.replace(" N ", " 1 ")
+    string = string.replace(" 's", "'s")
+
+    return string
+
+
+@register_task("wikitext")
+class WikiText(PerplexityTask):
+    VERSION = "2.0"
+    DATASET_PATH = "EleutherAI/wikitext_document_level"
+    DATASET_NAME = "wikitext-2-raw-v1"
+
+    def has_training_docs(self):
+        return True
+
+    def has_validation_docs(self):
+        return True
+
+    def has_test_docs(self):
+        return True
+
+    def training_docs(self):
+        return map(self._process_doc, self.dataset["train"])
+
+    def validation_docs(self):
+        return map(self._process_doc, self.dataset["validation"])
+
+    def test_docs(self):
+        return map(self._process_doc, self.dataset["test"])
+
+    def _process_doc(self, doc):
+        return doc["page"]
+
+    def doc_to_target(self, doc):
+        return wikitext_detokenizer(doc)
+
+    def should_decontaminate(self):
+        return True
+
+    def count_words(self, doc):
+        # count number of words in *original doc before detokenization*
+        return len(re.split(r"\s+", doc))

lm_eval/tasks/wikitext/README.md

@@ -33,4 +33,4 @@ Homepage: https://www.salesforce.com/products/einstein/ai-research/the-wikitext-
 - [x] Is in Eval-harness v1.0 ?
 - [x] Has been checked for regression from v1.0?
 - [ ] Has been checked for equivalence with original paper methodology?
-- [ ] "Main" checked variant clearly denoted?
\ No newline at end of file
+- [ ] "Main" checked variant clearly denoted?

lm_eval/tasks/wikitext/preprocess_wikitext.py

 import re
 
+
 def wikitext_detokenizer(doc):
     string = doc["page"]
     # contractions

lm_eval/tasks/wikitext/wikitext.yaml

 group:
   - wikitext_group
-task: wikitext_yaml
+task: default
 dataset_path: EleutherAI/wikitext_document_level
 dataset_name: wikitext-2-raw-v1
 output_type: loglikelihood_rolling
@@ -14,11 +14,5 @@ should_decontaminate: true
 doc_to_decontamination_query: "{{page}}"
 metric_list:
   - metric: word_perplexity
-    aggregation: weighted_perplexity
-    higher_is_better: false
   - metric: byte_perplexity
-    aggregation: weighted_perplexity
-    higher_is_better: false
   - metric: bits_per_byte
-    aggregation: bits_per_byte
-    higher_is_better: false
\ No newline at end of file

lm_eval/utils.py

@@ -158,7 +158,15 @@ def make_table(result_dict):
     md_writer = MarkdownTableWriter()
     latex_writer = LatexTableWriter()
     md_writer.headers = ["Task", "Version", "Filter", "Metric", "Value", "", "Stderr"]
-    latex_writer.headers = ["Task", "Version", "Filter", "Metric", "Value", "", "Stderr"]
+    latex_writer.headers = [
+        "Task",
+        "Version",
+        "Filter",
+        "Metric",
+        "Value",
+        "",
+        "Stderr",
+    ]
 
     values = []
 
@@ -166,7 +174,7 @@ def make_table(result_dict):
         version = result_dict["versions"][k]
         for (mf), v in dic.items():
             m, _, f = mf.partition(",")
-            print(m,f)
+            print(m, f)
             if m.endswith("_stderr"):
                 continue
 

main.py

@@ -19,7 +19,9 @@ class MultiChoice:
         for value in values.split(","):
             if len(fnmatch.filter(self.choices, value)) == 0:
                 eval_logger.warning("{} is not in task list.".format(value))
-                # eval_logger.info(f"{choices} is this")
+                eval_logger.info(f"Available tasks to choose:")
+                for choice in self.choices:
+                    eval_logger.info(f"    {choice}")
 
         return True