Merge pull request #2 from cjlovering/master

Pulling eval harness updates

lm_eval/evaluator.py

@@ -2,19 +2,32 @@ import collections
 import itertools
 import pathlib
 import random
+
 import lm_eval.metrics
 import lm_eval.models
 import lm_eval.tasks
 import lm_eval.base
+import promptsource
 import numpy as np
+
+from promptsource.templates import DatasetTemplates
 from lm_eval.utils import positional_deprecated, run_task_tests
 
 
 @positional_deprecated
-def simple_evaluate(model, model_args=None, tasks=[],
-                    num_fewshot=0, batch_size=None, device=None,
-                    no_cache=False, limit=None, bootstrap_iters=100000,
-                    description_dict=None, check_integrity=False):
+def simple_evaluate(
+    model,
+    model_args=None,
+    tasks=[],
+    num_fewshot=0,
+    batch_size=None,
+    device=None,
+    no_cache=False,
+    limit=None,
+    bootstrap_iters=100000,
+    description_dict=None,
+    check_integrity=False,
+):
     """Instantiate and evaluate a model on a list of tasks.
 
     :param model: Union[str, LM]
@@ -49,20 +62,28 @@ def simple_evaluate(model, model_args=None, tasks=[],
     assert tasks != [], "No tasks specified"
 
     if isinstance(model, str):
-        if model_args is None: model_args = ""
-        lm = lm_eval.models.get_model(model).create_from_arg_string(model_args, {
-            'batch_size': batch_size, 'device': device
-        })
+        if model_args is None:
+            model_args = ""
+        lm = lm_eval.models.get_model(model).create_from_arg_string(
+            model_args, {"batch_size": batch_size, "device": device}
+        )
     else:
         assert isinstance(model, lm_eval.base.LM)
         lm = model
 
+    # TODO: Hard-code turning off cache while testing. Remove once testing is completed.
+    no_cache = True
     if not no_cache:
         lm = lm_eval.base.CachingLM(
-            lm, 'lm_cache/' + model + '_' + model_args.replace('=', '-').replace(',', '_').replace('/', '-') + '.db'
+            lm,
+            "lm_cache/"
+            + model
+            + "_"
+            + model_args.replace("=", "-").replace(",", "_").replace("/", "-")
+            + ".db",
         )
 
-    task_dict = lm_eval.tasks.get_task_dict(tasks)
+    task_dict = lm_eval.tasks.get_task_dict_promptsource(tasks)
 
     if check_integrity:
         run_task_tests(task_list=tasks)
@@ -72,7 +93,7 @@ def simple_evaluate(model, model_args=None, tasks=[],
         task_dict=task_dict,
         num_fewshot=num_fewshot,
         limit=limit,
-        description_dict=description_dict
+        description_dict=description_dict,
     )
 
     # add info about the model and few shot config
@@ -85,14 +106,22 @@ def simple_evaluate(model, model_args=None, tasks=[],
         "no_cache": no_cache,
         "limit": limit,
         "bootstrap_iters": bootstrap_iters,
-        "description_dict": description_dict
+        "description_dict": description_dict,
     }
 
     return results
 
 
 @positional_deprecated
-def evaluate(lm, task_dict, provide_description=None, num_fewshot=0, limit=None, bootstrap_iters=100000, description_dict=None):
+def evaluate(
+    lm,
+    task_dict,
+    provide_description=None,
+    num_fewshot=0,
+    limit=None,
+    bootstrap_iters=100000,
+    description_dict=None,
+):
     """Instantiate and evaluate a model on a list of tasks.
 
     :param lm: obj
@@ -118,12 +147,14 @@ def evaluate(lm, task_dict, provide_description=None, num_fewshot=0, limit=None,
     assert not provide_description  # not implemented.
     if provide_description is not None:
         # nudge people to not specify it at all
-        print("WARNING: provide_description is deprecated and will be removed in a future version in favor of description_dict")
+        print(
+            "WARNING: provide_description is deprecated and will be removed in a future version in favor of description_dict"
+        )
 
     task_dict_items = [
         (name, task)
         for name, task in task_dict.items()
-        if(task.has_validation_docs() or task.has_test_docs())
+        if (task.has_validation_docs() or task.has_test_docs())
     ]
 
     results = collections.defaultdict(dict)
@@ -141,8 +172,12 @@ def evaluate(lm, task_dict, provide_description=None, num_fewshot=0, limit=None,
     docs = {}
 
     # get lists of each type of request
-    for task_name, task in task_dict_items:
-        versions[task_name] = task.VERSION
+    for task_prompt_name, task in task_dict_items:
+        # if task.is_generation_task():
+        #     print(f"WARNING: Skipping generation prompt {task.prompt.name}.")
+        #     continue
+
+        versions[task_prompt_name] = task.VERSION
         # default to test doc, fall back to val doc if validation unavailable
         # TODO: the test-fallback-to-val system isn't final, we should revisit it at some point
         if task.has_test_docs():
@@ -158,15 +193,19 @@ def evaluate(lm, task_dict, provide_description=None, num_fewshot=0, limit=None,
         rnd.seed(42)
         rnd.shuffle(task_docs)
 
-        description = description_dict[task_name] if description_dict and task_name in description_dict else ""
+        description = (
+            description_dict[task_prompt_name]
+            if description_dict and task_prompt_name in description_dict
+            else ""
+        )
 
         for doc_id, doc in enumerate(itertools.islice(task_docs, 0, limit)):
-            docs[(task_name, doc_id)] = doc
+            if task.invalid_doc_for_prompt(doc):
+                continue
+
+            docs[(task_prompt_name, doc_id)] = doc
             ctx = task.fewshot_context(
-                doc=doc,
-                num_fewshot=num_fewshot,
-                rnd=rnd,
-                description=description
+                doc=doc, num_fewshot=num_fewshot, rnd=rnd, description=description
             )
             reqs = task.construct_requests(doc, ctx)
             if not isinstance(reqs, (list, tuple)):
@@ -175,7 +214,9 @@ def evaluate(lm, task_dict, provide_description=None, num_fewshot=0, limit=None,
                 requests[req.request_type].append(req)
                 # i: index in requests for a single task instance
                 # doc_id: unique id that we can get back to a doc using `docs`
-                requests_origin[req.request_type].append((i, task_name, doc, doc_id))
+                requests_origin[req.request_type].append(
+                    (i, task_prompt_name, doc, doc_id)
+                )
 
     # all responses for each (task, doc)
     process_res_queue = collections.defaultdict(list)
@@ -189,43 +230,49 @@ def evaluate(lm, task_dict, provide_description=None, num_fewshot=0, limit=None,
 
         print("Running", reqtype, "requests")
         resps = getattr(lm, reqtype)([req.args for req in reqs])
-        resps = [x if req.index is None else x[req.index] for x, req in zip(resps, reqs)]
+        resps = [
+            x if req.index is None else x[req.index] for x, req in zip(resps, reqs)
+        ]
 
-        for resp, (i, task_name, doc, doc_id) in zip(resps, requests_origin[reqtype]):
-            process_res_queue[(task_name, doc_id)].append((i, resp))
+        for resp, (i, task_prompt_name, doc, doc_id) in zip(
+            resps, requests_origin[reqtype]
+        ):
+            process_res_queue[(task_prompt_name, doc_id)].append((i, resp))
 
     vals = collections.defaultdict(list)
 
     # unpack results and sort back in order and return control to Task
-    for (task_name, doc_id), requests in process_res_queue.items():
+    for (task_prompt_name, doc_id), requests in process_res_queue.items():
         requests.sort(key=lambda x: x[0])
         requests = [x[1] for x in requests]
 
-        task = task_dict[task_name]
-        doc = docs[(task_name, doc_id)]
+        task = task_dict[task_prompt_name]
+        doc = docs[(task_prompt_name, doc_id)]
 
         metrics = task.process_results(doc, requests)
         for metric, value in metrics.items():
-            vals[(task_name, metric)].append(value)
+            vals[(task_prompt_name, metric)].append(value)
 
     # aggregate results
-    for (task_name, metric), items in vals.items():
-        task = task_dict[task_name]
-        results[task_name][metric] = task.aggregation()[metric](items)
+    for (task_prompt_name, metric), items in vals.items():
+        task_name, prompt_name = task_prompt_name.split("+")
+        results[task_prompt_name]["task_name"] = task_name
+        results[task_prompt_name]["prompt_name"] = prompt_name
+        task = task_dict[task_prompt_name]
+        results[task_prompt_name][metric] = task.aggregation()[metric](items)
 
         # hotfix: bleu, chrf, ter seem to be really expensive to bootstrap
         # so we run them less iterations. still looking for a cleaner way to do this
         stderr = lm_eval.metrics.stderr_for_metric(
             metric=task.aggregation()[metric],
-            bootstrap_iters=min(bootstrap_iters, 1000) if metric in ["bleu", "chrf", "ter"] else bootstrap_iters,
+            bootstrap_iters=min(bootstrap_iters, 1000)
+            if metric in ["bleu", "chrf", "ter"]
+            else bootstrap_iters,
         )
         if stderr is not None:
-            results[task_name][metric + "_stderr"] = stderr(items)
+            results[task_prompt_name][metric + "_stderr"] = stderr(items)
 
-    return {
-        "results": dict(results),
-        "versions": dict(versions)
-    }
+    return {"results": dict(results), "versions": dict(versions)}
 
 
 def make_table(result_dict):
@@ -234,22 +281,50 @@ def make_table(result_dict):
 
     md_writer = MarkdownTableWriter()
     latex_writer = LatexTableWriter()
-    md_writer.headers = ["Task", "Version", "Metric", "Value", "", "Stderr"]
-    latex_writer.headers = ["Task", "Version", "Metric", "Value", "", "Stderr"]
+    md_writer.headers = ["Task", "Prompt", "Version", "Metric", "Value", "", "Stderr"]
+    latex_writer.headers = [
+        "Task",
+        "Prompt",
+        "Version",
+        "Metric",
+        "Value",
+        "",
+        "Stderr",
+    ]
 
     values = []
-
     for k, dic in result_dict["results"].items():
         version = result_dict["versions"][k]
         for m, v in dic.items():
             if m.endswith("_stderr"):
                 continue
-
+            if "_name" in m:
+                continue
             if m + "_stderr" in dic:
                 se = dic[m + "_stderr"]
-                values.append([k, version, m, '%.4f' % v, '±', '%.4f' % se])
+                values.append(
+                    [
+                        dic["task_name"],
+                        dic["prompt_name"],
+                        version,
+                        m,
+                        "%.4f" % v,
+                        "±",
+                        "%.4f" % se,
+                    ]
+                )
             else:
-                values.append([k, version, m, '%.4f' % v, '', ''])
+                values.append(
+                    [
+                        dic["task_name"],
+                        dic["prompt_name"],
+                        version,
+                        m,
+                        "%.4f" % v,
+                        "",
+                        "",
+                    ]
+                )
             k = ""
             version = ""
     md_writer.value_matrix = values

lm_eval/metrics.py

+import typing
 import math
 from collections.abc import Iterable
 
 import numpy as np
 import sacrebleu
+from rouge_score import rouge_scorer
 import sklearn.metrics
 import random
 
@@ -184,6 +186,74 @@ def _sacreformat(refs, preds):
 
     return refs, preds
 
+
+def rouge(
+    refs: typing.List[str],
+    pred: str,
+    rouge_types: typing.List[str] = ["rouge1", "rouge2", "rougeL", "rougeLsum"]
+):
+    """ ROUGE with multi-reference support
+
+    Implementation based on GEM-metrics:
+    https://github.com/GEM-benchmark/GEM-metrics/blob/431a8174bd6b3637e8d6118bfad2983e39e99733/gem_metrics/rouge.py
+
+    :param refs:
+        A `list` of reference `str`s.
+    :param pred:
+        A single prediction `str`s.
+    """
+
+    # Add newlines between sentences to correctly compute `rougeLsum`.
+    if "rougeLsum" in rouge_types:
+        # TODO: Adapt this to handle languages that do not support sentence endings by `.`.
+        # See GEM-metrics implementation with lang specific `nltk` tokenizers to
+        # split sentences.
+        pred = pred.replace(".", ".\n")
+        refs = [ref.replace(".", ".\n") for ref in refs]
+
+    scorer = rouge_scorer.RougeScorer(rouge_types=rouge_types, use_stemmer=True)
+    # ROUGE multi-ref jackknifing
+    if len(refs) > 1:
+        cur_scores = [scorer.score(ref, pred) for ref in refs]
+
+        # get best score for all leave-one-out sets
+        best_scores = []
+        for leave in range(len(refs)):
+            cur_scores_leave_one = [
+                cur_scores[s] for s in range(len(refs)) if s != leave
+            ]
+            best_scores.append(
+                {
+                    rouge_type: max(
+                        [s[rouge_type] for s in cur_scores_leave_one],
+                        key=lambda s: s.fmeasure,
+                    )
+                    for rouge_type in rouge_types
+                }
+            )
+        # average the leave-one-out bests to produce the final score
+        score = {
+            rouge_type: rouge_scorer.scoring.Score(
+                np.mean([b[rouge_type].precision for b in best_scores]),
+                np.mean([b[rouge_type].recall for b in best_scores]),
+                np.mean([b[rouge_type].fmeasure for b in best_scores]),
+            )
+            for rouge_type in rouge_types
+        }
+    else:
+        score = scorer.score(refs[0], pred)
+    # convert the named tuples to plain nested dicts
+    score = {
+        rouge_type: {
+            "precision": score[rouge_type].precision,
+            "recall": score[rouge_type].recall,
+            "fmeasure": score[rouge_type].fmeasure,
+        }
+        for rouge_type in rouge_types
+    }
+    return score
+
+
 # stderr stuff
 
 class _bootstrap_internal:

lm_eval/models/__init__.py

 from . import gpt2
+from . import gptj
 from . import gpt3
+from . import t5
+from . import t0
 from . import dummy
 
 MODEL_REGISTRY = {
     "hf": gpt2.HFLM,
     "gpt2": gpt2.GPT2LM,
+    "gptj": gptj.GPTJLM,
     "gpt3": gpt3.GPT3LM,
+    "t5": t5.T5LM,
+    "mt5": t5.T5LM,
+    "t0": t0.T0LM,
     "dummy": dummy.DummyLM,
 }
 

lm_eval/models/gpt2.py

@@ -4,8 +4,15 @@ from lm_eval.base import BaseLM
 
 
 class HFLM(BaseLM):
-
-    def __init__(self, device='cuda', pretrained='gpt2', revision='main', subfolder=None, tokenizer=None, batch_size=1):
+    def __init__(
+        self,
+        device="cuda",
+        pretrained="gpt2",
+        revision="main",
+        subfolder=None,
+        tokenizer=None,
+        batch_size=1,
+    ):
         super().__init__()
 
         assert isinstance(device, str)
@@ -15,28 +22,47 @@ class HFLM(BaseLM):
         if device:
             self._device = torch.device(device)
         else:
-            self._device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
+            self._device = (
+                torch.device("cuda")
+                if torch.cuda.is_available()
+                else torch.device("cpu")
+            )
 
         # TODO: update this to be less of a hack once subfolder is fixed in HF
         self.gpt2 = transformers.AutoModelForCausalLM.from_pretrained(
-            pretrained, revision=revision + ("/" + subfolder if subfolder is not None else "")
+            pretrained,
+            revision=revision + ("/" + subfolder if subfolder is not None else ""),
         ).to(self.device)
         self.gpt2.eval()
 
         # pretrained tokenizer for neo is broken for now so just hard-coding this to gpt2
         self.tokenizer = transformers.AutoTokenizer.from_pretrained(
-            pretrained if tokenizer is None else tokenizer, revision=revision, subfolder=subfolder)
+            pretrained if tokenizer is None else tokenizer,
+            revision=revision,
+            subfolder=subfolder,
+        )
 
-        assert isinstance(self.tokenizer, (
-            transformers.GPT2Tokenizer, transformers.GPT2TokenizerFast,
-            transformers.T5Tokenizer, transformers.T5TokenizerFast,
-        )), "this tokenizer has not been checked for compatibility yet!"
+        assert isinstance(
+            self.tokenizer,
+            (
+                transformers.GPT2Tokenizer,
+                transformers.GPT2TokenizerFast,
+                transformers.T5Tokenizer,
+                transformers.T5TokenizerFast,
+            ),
+        ), "this tokenizer has not been checked for compatibility yet!"
 
         self.vocab_size = self.tokenizer.vocab_size
 
-        if isinstance(self.tokenizer, (transformers.GPT2Tokenizer, transformers.GPT2TokenizerFast)):
-            assert self.tokenizer.encode('hello\n\nhello') == [31373, 198, 198, 31373], \
-                self.tokenizer.encode('hello\n\nhello')
+        if isinstance(
+            self.tokenizer, (transformers.GPT2Tokenizer, transformers.GPT2TokenizerFast)
+        ):
+            assert self.tokenizer.encode("hello\n\nhello") == [
+                31373,
+                198,
+                198,
+                31373,
+            ], self.tokenizer.encode("hello\n\nhello")
 
         # multithreading and batching
         self.batch_size_per_gpu = batch_size  # todo: adaptive batch size
@@ -90,14 +116,42 @@ class HFLM(BaseLM):
         with torch.no_grad():
             return self.gpt2(inps)[0][:, :, :50257]
 
-    def _model_generate(self, context, max_length, eos_token_id):
+    def _get_stopping_criteria(self, stopping_criteria_ids):
+        class MultitokenEOSCriteria(transformers.StoppingCriteria):
+            def __init__(self, eos_seq_id: torch.LongTensor, tokenizer):
+                self.eos_seq = tokenizer.decode(eos_seq_id)
+                self.eos_seq_id = eos_seq_id
+                self.eos_seq_len = len(eos_seq_id) + 1
+                self.tokenizer = tokenizer
+
+            def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
+                last_token_id = input_ids[0, -self.eos_seq_len:]
+                last_tokens = self.tokenizer.decode(last_token_id)
+                is_stopped = self.eos_seq in last_tokens
+                return is_stopped
+        
+        class EOSCriteria(transformers.StoppingCriteria):
+            def __init__(self, eos_token_id: torch.LongTensor):
+                self.eos_token_id = eos_token_id
+
+            def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
+                return input_ids[0,-1] == self.eos_token_id
+         
+        return transformers.StoppingCriteriaList([
+            MultitokenEOSCriteria(stopping_criteria_ids, self.tokenizer),
+            EOSCriteria(self.tokenizer.eos_token)
+        ])
+
+    def _model_generate(self, context, max_length, stopping_criteria_ids):
+        stopping_criteria = self._get_stopping_criteria(stopping_criteria_ids)
         return self.gpt2.generate(
             context, 
             max_length=max_length, 
-            eos_token_id=eos_token_id,
-            do_sample=False
+            stopping_criteria=stopping_criteria,
+            do_sample=False,
         )
         
 
+
 # for backwards compatibility
 GPT2LM = HFLM

lm_eval/models/gptj.py

+import transformers
+import torch
+from lm_eval.base import BaseLM
+
+
+class GPTJLM(BaseLM):
+    def __init__(
+        self,
+        device="cuda",
+        batch_size=1,
+    ):
+        super().__init__()
+
+        assert isinstance(device, str)
+        assert isinstance(batch_size, int)
+
+        if device:
+            self._device = torch.device(device)
+        else:
+            self._device = (
+                torch.device("cuda")
+                if torch.cuda.is_available()
+                else torch.device("cpu")
+            )
+
+        pretrained = "EleutherAI/gpt-j-6B"
+        self.gptj = transformers.AutoModelForCausalLM.from_pretrained(pretrained).to(self.device)
+        self.gptj.eval()
+
+        # pretrained tokenizer for neo is broken for now so just hard-coding this to gptj
+        self.tokenizer = transformers.AutoTokenizer.from_pretrained(pretrained)
+        self.vocab_size = self.tokenizer.vocab_size
+
+        # multithreading and batching
+        self.batch_size_per_gpu = batch_size  # todo: adaptive batch size
+
+        # TODO: fix multi-gpu
+        # gpus = torch.cuda.device_count()
+        # if gpus > 1:
+        #     self.gptj = nn.DataParallel(self.gptj)
+
+    @property
+    def eot_token_id(self):
+        # we use EOT because end of *text* is more accurate for what we're doing than end of *sentence*
+        return self.tokenizer.eos_token_id
+
+    @property
+    def max_length(self):
+        try:
+            return self.gptj.config.n_ctx
+        except AttributeError:
+            # gptneoconfig doesn't have n_ctx apparently
+            return self.gptj.config.max_position_embeddings
+
+    @property
+    def max_gen_toks(self):
+        return 256
+
+    @property
+    def batch_size(self):
+        # TODO: fix multi-gpu
+        return self.batch_size_per_gpu  # * gpus
+
+    @property
+    def device(self):
+        # TODO: fix multi-gpu
+        return self._device
+
+    def tok_encode(self, string: str):
+        return self.tokenizer.encode(string, add_special_tokens=False)
+
+    def tok_decode(self, tokens):
+        return self.tokenizer.decode(tokens)
+
+    def _model_call(self, inps):
+        """
+        inps: a torch tensor of shape [batch, sequence]
+        the size of sequence may vary from call to call
+
+        returns: a torch tensor of shape [batch, sequence, vocab] with the
+        logits returned from the model
+        """
+        with torch.no_grad():
+            return self.gptj(inps)[0][:, :, :50257]
+
+    def _get_stopping_criteria(self, stopping_criteria_ids):
+        class MultitokenEOSCriteria(transformers.StoppingCriteria):
+            def __init__(self, eos_seq_id: torch.LongTensor, tokenizer):
+                self.eos_seq = tokenizer.decode(eos_seq_id)
+                self.eos_seq_id = eos_seq_id
+                self.eos_seq_len = len(eos_seq_id) + 1
+                self.tokenizer = tokenizer
+
+            def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
+                last_token_id = input_ids[0, -self.eos_seq_len:]
+                last_tokens = self.tokenizer.decode(last_token_id)
+                is_stopped = self.eos_seq in last_tokens
+                return is_stopped
+        
+        class EOSCriteria(transformers.StoppingCriteria):
+            def __init__(self, eos_token_id: torch.LongTensor):
+                self.eos_token_id = eos_token_id
+
+            def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
+                return input_ids[0,-1] == self.eos_token_id
+         
+        return transformers.StoppingCriteriaList([
+            MultitokenEOSCriteria(stopping_criteria_ids, self.tokenizer),
+            EOSCriteria(self.tokenizer.eos_token)
+        ])
+
+    def _model_generate(self, context, max_length, stopping_criteria_ids):
+        stopping_criteria = self._get_stopping_criteria(stopping_criteria_ids)
+        return self.gptj.generate(
+            context, 
+            max_length=max_length, 
+            stopping_criteria=stopping_criteria,
+            do_sample=False,
+        )

lm_eval/models/t0.py

+import transformers
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from lm_eval.base import LM
+from lm_eval import utils
+from tqdm import tqdm
+import numpy as np
+import math
+
+class T0LM(LM):
+    MAX_GEN_TOKS = 256
+    MAX_INP_LENGTH = 512
+    VOCAB_SIZE = 32100
+    EOT_TOKEN_ID = 1
+
+    def __init__(self, device='cuda', parallelize=False, pretrained='t0', batch_size=1):
+        super().__init__()
+        if device:
+            self.device = torch.device(device)
+        else:
+            self.device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
+        print(pretrained)
+        self.t0 = transformers.AutoModelForSeq2SeqLM.from_pretrained(pretrained)
+        self.t0.eval()
+
+        if parallelize == "True":
+            print(parallelize)
+            self.t0.parallelize()
+            self.device = torch.device('cuda:0')
+        else:
+            self.t0.to(self.device)
+
+        self.tokenizer = transformers.AutoTokenizer.from_pretrained(pretrained)
+        self.max_length = self.MAX_INP_LENGTH
+
+        self.batch_size = int(batch_size)
+
+    @classmethod
+    def create_from_arg_string(cls, arg_string, additional_config={}):
+        args = utils.simple_parse_args_string(arg_string)
+        args2 = {k: v for k, v in additional_config.items() if v is not None}
+        return cls(**args, **args2)
+
+    def loglikelihood(self, requests):
+        res = []
+        for chunk in tqdm(utils.chunks(requests, self.batch_size), total=math.ceil(len(requests)/self.batch_size)):
+
+            inputs, targets = zip(*chunk)
+
+            inputs_tok = self.tokenizer(
+                list(inputs),
+                max_length=self.max_length,
+                padding=True,
+                # truncation=True,
+                add_special_tokens=False,
+                return_tensors="pt"
+                ).to(self.device)
+
+            for key in inputs_tok:
+                inputs_tok[key] = inputs_tok[key][:, -(self.max_length - 1) :]
+
+            targets_tok = self.tokenizer(
+                list(targets),
+                max_length=self.MAX_GEN_TOKS,
+                padding=True,
+                # truncation=True,
+                add_special_tokens=False,
+                return_tensors="pt"
+                ).to(self.device)
+
+            for key in targets_tok:
+                targets_tok[key] = targets_tok[key][:, -(self.max_length - 1) :]
+
+            with torch.no_grad():
+                outputs = self.t0(
+                    **inputs_tok,
+                    labels=targets_tok["input_ids"]
+                    )
+
+            log_softmaxes = F.log_softmax(outputs.logits, dim=-1)
+            
+            output_iterator = zip(
+                chunk,
+                log_softmaxes,
+                targets_tok["input_ids"],
+                targets_tok["attention_mask"],
+            )
+            for cache_key, log_softmax, target_tok, target_mask in output_iterator:
+                length = target_mask.sum()
+                log_softmax = log_softmax[:length]
+                target_tok = target_tok[:length]
+                greedy_tokens = log_softmax.argmax(dim=-1)
+                max_equal = (greedy_tokens == target_tok).all()
+                target_logits = torch.gather(
+                    log_softmax, 1, target_tok.unsqueeze(-1)
+                ).squeeze(-1)
+                answer = (float(target_logits.sum()), bool(max_equal))
+            
+                if cache_key is not None:
+                    self.cache_hook.add_partial("loglikelihood", cache_key, answer)
+
+                res.append(answer)
+
+        return res
+    
+    def loglikelihood_rolling(self, requests):
+        raise NotImplementedError
+
+    def _get_stopping_criteria(self, stopping_criteria_ids):
+        class MultitokenEOSCriteria(transformers.StoppingCriteria):
+            def __init__(self, eos_seq_id: torch.LongTensor, tokenizer):
+                self.eos_seq = tokenizer.decode(eos_seq_id)
+                self.eos_seq_id = eos_seq_id
+                self.eos_seq_len = len(eos_seq_id) + 1
+                self.tokenizer = tokenizer
+
+            def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
+                last_token_id = input_ids[0, -self.eos_seq_len:]
+                last_tokens = self.tokenizer.decode(last_token_id)
+                is_stopped = self.eos_seq in last_tokens
+                return is_stopped
+        
+        class EOSCriteria(transformers.StoppingCriteria):
+            def __init__(self, eos_token_id: torch.LongTensor):
+                self.eos_token_id = eos_token_id
+
+            def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
+                return input_ids[0,-1] == self.eos_token_id
+         
+        return transformers.StoppingCriteriaList([
+            MultitokenEOSCriteria(stopping_criteria_ids, self.tokenizer),
+            EOSCriteria(self.tokenizer.eos_token)
+        ])
+
+    def greedy_until(self, requests):
+        
+        res = []
+
+        for context, until in tqdm(requests):
+            if isinstance(until, str): until = [until]
+
+            context_enc = self.tokenizer(context, return_tensors="pt").to(self.device).input_ids
+
+            stopping_criteria_ids = self.tokenizer.encode(until[0])
+            stopping_criteria = self._get_stopping_criteria(stopping_criteria_ids)
+
+            cont = self.t0.generate(
+                context_enc,
+                max_length=self.MAX_GEN_TOKS,
+                stopping_criteria=stopping_criteria,
+                do_sample=False
+            )
+
+            s = self.tokenizer.decode(cont[0].tolist())
+            
+            self.cache_hook.add_partial("greedy_until", (context, until), s)
+            
+            res.append(s)
+        
+        return res
\ No newline at end of file

lm_eval/models/t5.py

+import transformers
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from lm_eval.base import LM
+from lm_eval import utils
+from tqdm import tqdm
+import numpy as np
+import math
+
+class T5LM(LM):
+    MAX_GEN_TOKS = 256
+    MAX_INP_LENGTH = 512
+    VOCAB_SIZE = 32128
+    EOT_TOKEN_ID = 1
+
+    def __init__(self, device='cuda', parallelize=False, pretrained='t5', batch_size=1):
+        super().__init__()
+        if device:
+            self.device = torch.device(device)
+        else:
+            self.device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
+        print(pretrained)
+        self.t5 = transformers.AutoModelForSeq2SeqLM.from_pretrained(pretrained)
+        self.t5.eval()
+
+        if parallelize == "True":
+            print(parallelize)
+            self.t5.parallelize()
+            self.device = torch.device('cuda:0')
+        else:
+            self.t5.to(self.device)
+
+        self.tokenizer = transformers.T5TokenizerFast.from_pretrained(pretrained)
+        self.max_length = self.MAX_INP_LENGTH
+
+        self.batch_size = int(batch_size)
+
+    @classmethod
+    def create_from_arg_string(cls, arg_string, additional_config={}):
+        args = utils.simple_parse_args_string(arg_string)
+        args2 = {k: v for k, v in additional_config.items() if v is not None}
+        return cls(**args, **args2)
+
+    def loglikelihood(self, requests):
+        res = []
+        for chunk in tqdm(utils.chunks(requests, self.batch_size), total=math.ceil(len(requests)/self.batch_size)):
+
+            inputs, targets = zip(*chunk)
+
+            inputs_tok = self.tokenizer(
+                list(inputs),
+                max_length=self.max_length,
+                padding=True,
+                # truncation=True,
+                add_special_tokens=False,
+                return_tensors="pt"
+                ).to(self.device)
+
+            for key in inputs_tok:
+                inputs_tok[key] = inputs_tok[key][:, -(self.max_length - 1) :]
+
+            targets_tok = self.tokenizer(
+                list(targets),
+                max_length=self.MAX_GEN_TOKS,
+                padding=True,
+                # truncation=True,
+                add_special_tokens=False,
+                return_tensors="pt"
+                ).to(self.device)
+
+            for key in targets_tok:
+                targets_tok[key] = targets_tok[key][:, -(self.max_length - 1) :]
+
+            with torch.no_grad():
+                outputs = self.t5(
+                    **inputs_tok,
+                    labels=targets_tok["input_ids"]
+                    )
+
+            log_softmaxes = F.log_softmax(outputs.logits, dim=-1)
+            
+            output_iterator = zip(
+                chunk,
+                log_softmaxes,
+                targets_tok["input_ids"],
+                targets_tok["attention_mask"],
+            )
+            for cache_key, log_softmax, target_tok, target_mask in output_iterator:
+                length = target_mask.sum()
+                log_softmax = log_softmax[:length]
+                target_tok = target_tok[:length]
+                greedy_tokens = log_softmax.argmax(dim=-1)
+                max_equal = (greedy_tokens == target_tok).all()
+                target_logits = torch.gather(
+                    log_softmax, 1, target_tok.unsqueeze(-1)
+                ).squeeze(-1)
+                answer = (float(target_logits.sum()), bool(max_equal))
+            
+                if cache_key is not None:
+                    self.cache_hook.add_partial("loglikelihood", cache_key, answer)
+
+                res.append(answer)
+
+        return res
+    
+    def loglikelihood_rolling(self, requests):
+        raise NotImplementedError
+
+    def _get_stopping_criteria(self, stopping_criteria_ids):
+        class MultitokenEOSCriteria(transformers.StoppingCriteria):
+            def __init__(self, eos_seq_id: torch.LongTensor, tokenizer):
+                self.eos_seq = tokenizer.decode(eos_seq_id)
+                self.eos_seq_id = eos_seq_id
+                self.eos_seq_len = len(eos_seq_id) + 1
+                self.tokenizer = tokenizer
+
+            def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
+                last_token_id = input_ids[0, -self.eos_seq_len:]
+                last_tokens = self.tokenizer.decode(last_token_id)
+                is_stopped = self.eos_seq in last_tokens
+                return is_stopped
+        
+        class EOSCriteria(transformers.StoppingCriteria):
+            def __init__(self, eos_token_id: torch.LongTensor):
+                self.eos_token_id = eos_token_id
+
+            def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
+                return input_ids[0,-1] == self.eos_token_id
+         
+        return transformers.StoppingCriteriaList([
+            MultitokenEOSCriteria(stopping_criteria_ids, self.tokenizer),
+            EOSCriteria(self.tokenizer.eos_token)
+        ])
+
+    def greedy_until(self, requests):
+        
+        res = []
+
+        for context, until in tqdm(requests):
+            if isinstance(until, str): until = [until]
+
+            context_enc = self.tokenizer(context, return_tensors="pt").to(self.device).input_ids
+
+            stopping_criteria_ids = self.tokenizer.encode(until[0])
+            stopping_criteria = self._get_stopping_criteria(stopping_criteria_ids)
+
+            cont = self.t5.generate(
+                context_enc,
+                max_length=self.MAX_GEN_TOKS,
+                stopping_criteria=stopping_criteria,
+                do_sample=False
+            )
+
+            s = self.tokenizer.decode(cont[0].tolist())
+            
+            self.cache_hook.add_partial("greedy_until", (context, until), s)
+            
+            res.append(s)
+        
+        return res
\ No newline at end of file

lm_eval/tasks/__init__.py

+from promptsource.templates import DatasetTemplates
 from pprint import pprint
 from typing import List, Union
 
@@ -51,6 +52,9 @@ from . import blimp
 from . import asdiv
 from . import gsm8k
 from . import storycloze
+from . import hans
+
+# from . import e2e_nlg_cleaned
 
 ########################################
 # Translation tasks
@@ -58,8 +62,8 @@ from . import storycloze
 
 # 6 total
 gpt3_translation_benchmarks = {
-    "wmt14": ['en-fr', 'fr-en'],  # French
-    "wmt16": ['en-ro', 'ro-en', 'de-en', 'en-de'],  # German, Romanian
+    "wmt14": ["en-fr", "fr-en"],  # French
+    "wmt16": ["en-ro", "ro-en", "de-en", "en-de"],  # German, Romanian
 }
 
 
@@ -67,7 +71,7 @@ gpt3_translation_benchmarks = {
 selected_translation_benchmarks = {
     **gpt3_translation_benchmarks,
     "wmt20": sacrebleu.get_langpairs_for_testset("wmt20"),
-    "iwslt17": ['en-ar', 'ar-en']  # Arabic
+    "iwslt17": ["en-ar", "ar-en"],  # Arabic
 }
 
 # 319 total
@@ -91,7 +95,7 @@ TASK_REGISTRY = {
     "rte": glue.RTE,
     "qnli": glue.QNLI,
     "qqp": glue.QQP,
-    #"stsb": glue.STSB, # not implemented yet
+    # "stsb": glue.STSB, # not implemented yet
     "sst": glue.SST,
     "wnli": glue.WNLI,
     # SuperGLUE
@@ -102,34 +106,27 @@ TASK_REGISTRY = {
     "record": superglue.ReCoRD,
     "wic": superglue.WordsInContext,
     "wsc": superglue.SGWinogradSchemaChallenge,
-    
     # Order by benchmark/genre?
     "coqa": coqa.CoQA,
     "drop": drop.DROP,
     "lambada": lambada.LAMBADA,
     "lambada_cloze": lambada_cloze.LAMBADA_cloze,
-    
     # multilingual lambada
     **lambada_multilingual.construct_tasks(),
-
     "wikitext": wikitext.WikiText,
     # "cbt-cn": cbt.CBTCN, # disabled pending context length fix
     # "cbt-ne": cbt.CBTNE, # disabled pending context length fix
-
     "piqa": piqa.PiQA,
     "prost": prost.PROST,
     "mc_taco": mc_taco.MCTACO,
-
     # Science related
-    "pubmedqa" : pubmedqa.Pubmed_QA,
-    "sciq" : sciq.SciQ,
-
+    "pubmedqa": pubmedqa.Pubmed_QA,
+    "sciq": sciq.SciQ,
+    # "e2e_nlg_cleaned": e2e_nlg_cleaned.E2E_NLG_Cleaned,
     "qasper": qasper.QASPER,
-
-    "qa4mre_2011" : qa4mre.QA4MRE_2011,
-    "qa4mre_2012" : qa4mre.QA4MRE_2012,
-    "qa4mre_2013" : qa4mre.QA4MRE_2013,
-
+    "qa4mre_2011": qa4mre.QA4MRE_2011,
+    "qa4mre_2012": qa4mre.QA4MRE_2012,
+    "qa4mre_2013": qa4mre.QA4MRE_2013,
     "triviaqa": triviaqa.TriviaQA,
     "arc_easy": arc.ARCEasy,
     "arc_challenge": arc.ARCChallenge,
@@ -150,21 +147,18 @@ TASK_REGISTRY = {
     "anli_r1": anli.ANLIRound1,
     "anli_r2": anli.ANLIRound2,
     "anli_r3": anli.ANLIRound3,
-
+    "hans": hans.HANS,
     "ethics_cm": hendrycks_ethics.EthicsCM,
     "ethics_deontology": hendrycks_ethics.EthicsDeontology,
     "ethics_justice": hendrycks_ethics.EthicsJustice,
     "ethics_utilitarianism_original": hendrycks_ethics.EthicsUtilitarianismOriginal,
     "ethics_utilitarianism": hendrycks_ethics.EthicsUtilitarianism,
     "ethics_virtue": hendrycks_ethics.EthicsVirtue,
-
     "truthfulqa_mc": truthfulqa.TruthfulQAMultipleChoice,
     "truthfulqa_gen": truthfulqa.TruthfulQAGeneration,
-
     # dialogue
     "mutual": mutual.MuTual,
     "mutual_plus": mutual.MuTualPlus,
-
     # math
     "math_algebra": hendrycks_math.MathAlgebra,
     "math_counting_and_prob": hendrycks_math.MathCountingAndProbability,
@@ -175,7 +169,6 @@ TASK_REGISTRY = {
     "math_precalc": hendrycks_math.MathPrecalculus,
     "math_asdiv": asdiv.Asdiv,
     "gsm8k": gsm8k.GradeSchoolMath8K,
-
     # arithmetic
     "arithmetic_2da": arithmetic.Arithmetic2DPlus,
     "arithmetic_2ds": arithmetic.Arithmetic2DMinus,
@@ -189,22 +182,18 @@ TASK_REGISTRY = {
     "arithmetic_1dc": arithmetic.Arithmetic1DComposite,
     # TODO Perhaps make these groups of tasks
     #   e.g. anli, arithmetic, openai_translations, harness_translations
-
     # hendrycksTest (57 tasks)
     **hendrycks_test.create_all_tasks(),
-
     # e.g. wmt14-fr-en
     **translation.create_tasks_from_benchmarks(gpt3_translation_benchmarks),
     # chef's selection, mostly wmt20
     **translation.create_tasks_from_benchmarks(selected_translation_benchmarks),
-
     # Word Scrambling and Manipulation Tasks
     "anagrams1": unscramble.Anagrams1,
     "anagrams2": unscramble.Anagrams2,
     "cycle_letters": unscramble.CycleLetters,
     "random_insertion": unscramble.RandomInsertion,
     "reversed_words": unscramble.ReversedWords,
-
     # Pile
     "pile_arxiv": pile.PileArxiv,
     "pile_books3": pile.PileBooks3,
@@ -228,7 +217,6 @@ TASK_REGISTRY = {
     "pile_ubuntu-irc": pile.PileUbuntuIrc,
     "pile_wikipedia": pile.PileWikipedia,
     "pile_youtubesubtitles": pile.PileYoutubeSubtitles,
-    
     # BLiMP
     "blimp_adjunct_island": blimp.BlimpAdjunctIsland,
     "blimp_anaphor_gender_agreement": blimp.BlimpAnaphorGenderAgreement,
@@ -297,7 +285,6 @@ TASK_REGISTRY = {
     "blimp_wh_vs_that_no_gap_long_distance": blimp.BlimpWhVsThatNoGapLongDistance,
     "blimp_wh_vs_that_with_gap": blimp.BlimpWhVsThatWithGap,
     "blimp_wh_vs_that_with_gap_long_distance": blimp.BlimpWhVsThatWithGapLongDistance,
-
     # Requires manual download of data.
     # "storycloze_2016": storycloze.StoryCloze2016,
     # "storycloze_2018": storycloze.StoryCloze2018,
@@ -323,17 +310,49 @@ def get_task_name_from_object(task_object):
             return name
 
     # this gives a mechanism for non-registered tasks to have a custom name anyways when reporting
-    return task_object.EVAL_HARNESS_NAME if hasattr(task_object, "EVAL_HARNESS_NAME") else type(task_object).__name__
+    return (
+        task_object.EVAL_HARNESS_NAME
+        if hasattr(task_object, "EVAL_HARNESS_NAME")
+        else type(task_object).__name__
+    )
 
 
 def get_task_dict(task_name_list: List[Union[str, lm_eval.base.Task]]):
     task_name_dict = {
         task_name: get_task(task_name)()
-        for task_name in task_name_list if isinstance(task_name, str)
+        for task_name in task_name_list
+        if isinstance(task_name, str)
     }
     task_name_from_object_dict = {
         get_task_name_from_object(task_object): task_object
-        for task_object in task_name_list if not isinstance(task_object, str)
+        for task_object in task_name_list
+        if not isinstance(task_object, str)
     }
     assert set(task_name_dict.keys()).isdisjoint(set(task_name_from_object_dict.keys()))
     return {**task_name_dict, **task_name_from_object_dict}
+
+
+def get_task_dict_promptsource(task_name_list: List[str]):
+    """Loads a task instance for each prompt written for that task."""
+    task_name_dict = {}
+
+    for task_name in task_name_list:
+        assert isinstance(task_name, str)
+
+        # Static version of the Task Use this to get HF dataset path / name.
+        static_task_obj = get_task(task_name)
+        # Create the proper task name arg for DatasetTemplates.
+        sub_task = (
+            f"/{static_task_obj.DATASET_NAME}" if static_task_obj.DATASET_NAME else ""
+        )
+        ps_task_name = f"{static_task_obj.DATASET_PATH}{sub_task}"
+
+        task_prompts = DatasetTemplates(ps_task_name)
+        for prompt_name in task_prompts.all_template_names:
+            prompt = task_prompts[prompt_name]
+            # NOTE: We choose a sep that can be easily split.
+            task_name_dict[f"{task_name}+{prompt_name}"] = get_task(task_name)(
+                prompt=prompt
+            )
+
+    return task_name_dict

lm_eval/tasks/anli.py

@@ -10,7 +10,7 @@ provided explanations.
 Homepage: "https://github.com/facebookresearch/anli"
 """
 import numpy as np
-from lm_eval.base import rf, Task
+from lm_eval.base import rf, PromptSourceTask
 from lm_eval.metrics import mean
 
 
@@ -30,7 +30,7 @@ _CITATION = """
 """
 
 
-class ANLIBase(Task):
+class ANLIBase(PromptSourceTask):
     VERSION = 0
     DATASET_PATH = "anli"
     DATASET_NAME = None
@@ -59,51 +59,6 @@ class ANLIBase(Task):
         if self.has_test_docs():
             return self.dataset["test_r" + str(self.SPLIT)]
 
-    def doc_to_text(self, doc):
-        # OA does this a bit weirdly: they prepend "anli 1:  anli 1:  " to the beginning
-        # of the prompt (yes, repeating it!). also, " True, False, or Neither?" is directly 
-        # appended onto the question, with no "Answer:" or even a newline. Do we *really* 
-        # want to do it exactly as OA did?
-        return doc['premise'] + '\nQuestion: ' + doc['hypothesis'] + ' True, False, or Neither?\nAnswer:'
-
-    def doc_to_target(self, doc):
-        # True = entailment
-        # False = contradiction
-        # Neither = neutral
-        return " " + ["True", "Neither", "False"][doc['label']]
-
-    def construct_requests(self, doc, ctx):
-        """ Uses RequestFactory to construct Requests and returns an iterable of 
-        Requests which will be sent to the LM.
-
-        :param doc:
-            The document as returned from training_docs, validation_docs, or test_docs.
-        :param ctx: str
-            The context string, generated by fewshot_context. This includes the natural 
-            language description, as well as the few shot examples, and the question
-            part of the document for `doc`. 
-        """
-        ll_true, _ = rf.loglikelihood(ctx, " True") 
-        ll_neither, _ = rf.loglikelihood(ctx, " Neither") 
-        ll_false, _ = rf.loglikelihood(ctx, " False") 
-        return ll_true, ll_neither, ll_false
-    
-    def process_results(self, doc, results):
-        """Take a single document and the LM results and evaluates, returning a 
-        dict where keys are the names of submetrics and values are the values of 
-        the metric for that one document
-
-        :param doc:
-            The document as returned from training_docs, validation_docs, or test_docs.
-        :param results:
-            The results of the requests created in construct_requests.
-        """
-        gold = doc["label"]
-        pred = np.argmax(results)
-        return {
-            "acc": pred == gold
-        }
-
     def aggregation(self):
         """
         :returns: {str: [float] -> float}

lm_eval/tasks/arithmetic.py

@@ -58,10 +58,11 @@ class Arithmetic(Task):
 
     def construct_requests(self, doc, ctx):
         ll, is_prediction = rf.loglikelihood(ctx, doc["completion"])
-        return is_prediction
+        return ll, is_prediction
 
     def process_results(self, doc, results):
-        is_prediction, = results
+        print(results)
+        results = results
         return {
             "acc": is_prediction
         }

lm_eval/tasks/coqa.py

@@ -12,7 +12,7 @@ Homepage: https://stanfordnlp.github.io/coqa/
 import inspect
 import transformers.data.metrics.squad_metrics as squad_metrics
 import lm_eval.datasets.coqa.coqa
-from lm_eval.base import Task, rf, mean
+from lm_eval.base import PromptSourceTask, Task, rf, mean
 from itertools import zip_longest
 
 
@@ -28,9 +28,9 @@ _CITATION = """
 """
 
 
-class CoQA(Task):
+class CoQA(PromptSourceTask):
     VERSION = 1
-    DATASET_PATH = inspect.getfile(lm_eval.datasets.coqa.coqa)
+    DATASET_PATH = "coqa"
     DATASET_NAME = None
 
     def has_training_docs(self):
@@ -51,44 +51,21 @@ class CoQA(Task):
     def test_docs(self):
         pass
 
-    def doc_to_text(self, doc):
-        # Given a passage p, the conversation history {q1, a1, . . . qi−1, ai−1} 
-        # and a question qi, the task is to predict the answer ai
-        doc_text = doc["story"] + '\n\n'
-        for (q, a) in zip_longest(doc["questions"]["input_text"], doc["answers"]["input_text"][:-1]):   # omit target answer ai
-            question = f"Q: {q}\n\n"
-            answer = f"A: {a}\n\n" if a is not None else "A:"
-            doc_text += question + answer
-        return doc_text
-        
-    @classmethod
-    def get_answers(cls, doc, turn_id):
-        # Returns unique answers and valid alternatives (Some questions in CoQA have multiple valid answers).
-        answers = []
-        answer_forturn = doc["answers"]["input_text"][turn_id - 1]
-        answers.append(answer_forturn)
-        
-        additional_answers = doc.get("additional_answers")
-        if additional_answers:
-            for key in additional_answers:
-                additional_answer_for_turn = additional_answers[key]["input_text"][turn_id - 1]
-                if additional_answer_for_turn.lower() not in map(str.lower, answers):
-                    answers.append(additional_answer_for_turn)
-        return answers
-    
-    @classmethod
-    def get_answer_choice(self, raw_text):
-        # Function maps answers to CoQA answer categories
-        # ~ 1/5 of the CoQA answers are Yes/No 
-        # ~ 2/3 of the CoQA answers are span-based
-        # (answers overlap with the passage ignoring punctuation and case mismatch)
-        if raw_text == "unknown":
-            return '0'
-        if squad_metrics.normalize_answer(raw_text) == "yes":
-            return '1'
-        if squad_metrics.normalize_answer(raw_text) == "no":
-            return '2'
-        return '3' # Not a yes/no question
+    # @classmethod
+    # def get_answers(cls, doc, turn_id):
+    #     # Returns unique answers and valid alternatives (Some questions in CoQA have multiple valid answers).
+    #     answers = []
+    #     answer_forturn = doc["answers"]["input_text"][turn_id - 1]
+    #     answers.append(answer_forturn)
+    #     additional_answers = doc.get("additional_answers")
+    #     if additional_answers:
+    #         for key in additional_answers:
+    #             additional_answer_for_turn = additional_answers[key]["input_text"][
+    #                 turn_id - 1
+    #             ]
+    #             if additional_answer_for_turn.lower() not in map(str.lower, answers):
+    #                 answers.append(additional_answer_for_turn)
+    #     return answers
 
     @staticmethod
     def compute_scores(gold_list, pred):
@@ -98,36 +75,36 @@ class CoQA(Task):
         em_sum = 0.0
         if len(gold_list) > 1:
             for i in range(len(gold_list)):
-                gold_answers = gold_list[0:i] + gold_list[i + 1:]
+                gold_answers = gold_list[0:i] + gold_list[i + 1 :]
                 # predictions compared against (n) golds and take maximum
-                em_sum += max(squad_metrics.compute_exact(a, pred) for a in gold_answers)
+                em_sum += max(
+                    squad_metrics.compute_exact(a, pred) for a in gold_answers
+                )
                 f1_sum += max(squad_metrics.compute_f1(a, pred) for a in gold_answers)
         else:
             em_sum += max(squad_metrics.compute_exact(a, pred) for a in gold_list)
             f1_sum += max(squad_metrics.compute_f1(a, pred) for a in gold_list)
 
-        return {'em': em_sum / max(1, len(gold_list)), 'f1': f1_sum / max(1, len(gold_list))}
+        return {
+            "em": em_sum / max(1, len(gold_list)),
+            "f1": f1_sum / max(1, len(gold_list)),
+        }
 
-    def doc_to_target(self, doc, turnid=None):
-        # Default to prediction of last turn.
-        if turnid is None:
-            turnid = len(doc["questions"]["input_text"])
-        raw_text = doc['answers']["input_text"][turnid - 1]
-        return " " + raw_text
+    def stopping_criteria(self):
+        return "\n\n"
 
-    def construct_requests(self, doc, ctx):
-        """ Uses RequestFactory to construct Requests and returns an iterable of 
-        Requests which will be sent to the LM.
+    # def construct_requests(self, doc, ctx):
+    #     """Uses RequestFactory to construct Requests and returns an iterable of
+    #     Requests which will be sent to the LM.
 
-        :param doc:
-            The document as returned from training_docs, validation_docs, or test_docs.
-        :param ctx: str
-            The context string, generated by fewshot_context. This includes the natural 
-            language description, as well as the few shot examples, and the question
-            part of the document for `doc`. 
-        """
-        cont_request = rf.greedy_until(ctx, ['\nQ:'])
-        return cont_request
+    #     :param doc:
+    #         The document as returned from training_docs, validation_docs, or test_docs.
+    #     :param ctx: str
+    #         The context string, generated by fewshot_context. This includes the natural
+    #         language description, as well as the few shot examples, and the question
+    #         part of the document for `doc`.
+    #     """
+    #     return cont_request
 
     def process_results(self, doc, results):
         """Take a single document and the LM results and evaluates, returning a
@@ -139,15 +116,25 @@ class CoQA(Task):
         :param results:
             The results of the requests created in construct_requests.
         """
-        turn_id = len(doc["questions"]["input_text"])
-        gold_list = self.get_answers(doc, turn_id)
-        pred = results[0].strip().split('\n')[0]
-
-        scores = self.compute_scores(gold_list, pred)
+        target = self.doc_to_target(doc).strip()
+        pred = results[0].strip().split("\n")[0]
+        print("*" * 80)
+        print(f"DOC: {doc}")
+        #        print(f"PS: {self.prompt.apply(doc)}")
+        print(f"TEXT: {self.doc_to_text(doc)}")
+        print(f"TARGET: {target} END TARGET")
+        print(f"PRED: {pred} END PRED")
+        print("*" * 80)
+
+        # turn_id = len(doc["questions"]["input_text"])
+        # gold_list = self.get_answers(doc, turn_id)
+
+        # TODO: Add HF metrics mapped from promptsource metadata.
+        scores = self.compute_scores([target], pred)
 
         return {
-            "f1": scores['f1'],
-            "em": scores['em'],
+            "f1": scores["f1"],
+            "em": scores["em"],
         }
 
     def higher_is_better(self):

lm_eval/tasks/drop.py

@@ -18,7 +18,7 @@ import re
 import string
 import lm_eval.datasets.drop.drop
 from scipy.optimize import linear_sum_assignment
-from lm_eval.base import Task, rf
+from lm_eval.base import PromptSourceTask, rf
 from lm_eval.metrics import mean
 
 
@@ -37,9 +37,9 @@ _CITATION = """
 _ARTICLES = re.compile(r"\b(a|an|the)\b", re.UNICODE)
 
 
-class DROP(Task):
+class DROP(PromptSourceTask):
     VERSION = 1
-    DATASET_PATH = inspect.getfile(lm_eval.datasets.drop.drop)
+    DATASET_PATH = "drop"  # inspect.getfile(lm_eval.datasets.drop.drop)
     DATASET_NAME = None
 
     def has_training_docs(self):
@@ -52,46 +52,13 @@ class DROP(Task):
         return False
 
     def training_docs(self):
-        if self._training_docs is None:
-            self._training_docs = list(map(self._process_doc, self.dataset["train"]))
-        return self._training_docs
+        # if self._training_docs is None:
+        #     self._training_docs = list()
+        # return self._training_docs
+        return self.dataset["train"]
 
     def validation_docs(self):
-        return map(self._process_doc, self.dataset["validation"])
-
-    def _process_doc(self, doc):
-        return {
-            "id": doc["query_id"],
-            "passage": doc["passage"],
-            "question": doc["question"],
-            "answers": self.get_answers(doc),
-        }
-
-    @classmethod
-    def get_answers(cls, qa):
-        def _flatten_validated_answers(validated_answers):
-            """ Flattens a dict of lists of validated answers.
-            {"number": ['1', '8'], ...}
-            -> [{"number": ['1'], ...}, {"number": ['8'], ...}]
-            """
-            vas = []
-            for i in range(len(validated_answers["number"])):
-                vas.append({
-                    "number": validated_answers["number"][i],
-                    "date": validated_answers["date"][i],
-                    "spans": validated_answers["spans"][i],
-                })
-            return vas
-        answers = []
-        answers_set = set()
-        candidates = [qa["answer"]] + _flatten_validated_answers(qa["validated_answers"])
-        for candidate in candidates:
-            answer = cls.parse_answer(candidate)
-            if answer in answers_set:
-                continue
-            answers_set.add(answer)
-            answers.append(answer)
-        return answers
+        return self.dataset["validation"]
 
     @classmethod
     def parse_answer(cls, answer):
@@ -100,29 +67,33 @@ class DROP(Task):
             return (str(answer["number"]),)
         if answer["spans"] != []:
             return tuple(answer["spans"])
-        return (" ".join([answer["date"]["day"],
-                          answer["date"]["month"],
-                          answer["date"]["year"]]).strip(),)
+        return (
+            " ".join(
+                [answer["date"]["day"], answer["date"]["month"], answer["date"]["year"]]
+            ).strip(),
+        )
 
-    def doc_to_text(self, doc):
-        return f"Passage: {doc['passage']}\nQuestion: {doc['question']}\nAnswer:"
+    # def doc_to_text(self, doc):
+    #     return f"Passage: {doc['passage']}\nQuestion: {doc['question']}\nAnswer:"
 
-    def doc_to_target(self, doc):
-        return " " + ", ".join(doc["answers"][0])
+    # def doc_to_target(self, doc):
+    #     return " " + ", ".join(doc["answers"][0])
 
-    def construct_requests(self, doc, ctx):
-        """Uses RequestFactory to construct Requests and returns an iterable of
-        Requests which will be sent to the LM.
+    # def construct_requests(self, doc, ctx):
+    #     """Uses RequestFactory to construct Requests and returns an iterable of
+    #     Requests which will be sent to the LM.
 
-        :param doc:
-            The document as returned from training_docs, validation_docs, or test_docs.
-        :param ctx: str
-            The context string, generated by fewshot_context. This includes the natural
-            language description, as well as the few shot examples, and the question
-            part of the document for `doc`.
-        """
-        conts = [rf.greedy_until(ctx, ["."])]
-        return conts
+    #     :param doc:
+    #         The document as returned from training_docs, validation_docs, or test_docs.
+    #     :param ctx: str
+    #         The context string, generated by fewshot_context. This includes the natural
+    #         language description, as well as the few shot examples, and the question
+    #         part of the document for `doc`.
+    #     """
+    #     conts = [rf.greedy_until(ctx, ["."])]
+    #     return conts
+    def stopping_criteria(self):
+        return "."
 
     def process_results(self, doc, results):
         """Take a single document and the LM results and evaluates, returning a
@@ -134,7 +105,21 @@ class DROP(Task):
         :param results:
             The results of the requests created in construct_requests.
         """
-        preds, golds = results, doc["answers"]
+
+        pred = results[0].strip()
+        target = self.doc_to_target(doc).strip()
+
+        print("*" * 80)
+        print(f"DOC: {doc}")
+        print(f"PS: {self.prompt.apply(doc)}")
+        print(f"TEXT: {self.doc_to_text(doc)}")
+        print(f"TARGET: {target} END TARGET")
+        print(f"PRED: {pred} END PRED")
+        print("*" * 80)
+
+        preds = [pred]
+        golds = [target]
+
         max_em = 0
         max_f1 = 0
         for gold_answer in golds:
@@ -142,10 +127,7 @@ class DROP(Task):
             if gold_answer[0].strip():
                 max_em = max(max_em, exact_match)
                 max_f1 = max(max_f1, f1_score)
-        return {
-            "em": max_em,
-            "f1": max_f1
-        }
+        return {"em": max_em, "f1": max_f1}
 
     def get_metrics(self, predicted, gold):
         """
@@ -158,7 +140,9 @@ class DROP(Task):
         predicted_bags = self._answer_to_bags(predicted)
         gold_bags = self._answer_to_bags(gold)
 
-        if set(predicted_bags[0]) == set(gold_bags[0]) and len(predicted_bags[0]) == len(gold_bags[0]):
+        if set(predicted_bags[0]) == set(gold_bags[0]) and len(
+            predicted_bags[0]
+        ) == len(gold_bags[0]):
             exact_match = 1.0
         else:
             exact_match = 0.0
@@ -190,7 +174,9 @@ class DROP(Task):
         for gold_index, gold_item in enumerate(gold):
             for pred_index, pred_item in enumerate(predicted):
                 if self._match_numbers_if_present(gold_item, pred_item):
-                    scores[gold_index, pred_index] = self._compute_f1(pred_item, gold_item)
+                    scores[gold_index, pred_index] = self._compute_f1(
+                        pred_item, gold_item
+                    )
         row_ind, col_ind = linear_sum_assignment(-scores)
 
         max_scores = np.zeros([max(len(gold), len(predicted))])
@@ -256,7 +242,11 @@ class DROP(Task):
 
     def _normalize(self, answer):
         tokens = [
-            self._white_space_fix(self._remove_articles(self._fix_number(self._remove_punc(token.lower()))))
+            self._white_space_fix(
+                self._remove_articles(
+                    self._fix_number(self._remove_punc(token.lower()))
+                )
+            )
             for token in self._tokenize(answer)
         ]
         tokens = [token for token in tokens if token.strip()]
@@ -269,10 +259,7 @@ class DROP(Task):
             A dictionary where keys are the names of submetrics and values are
             functions that aggregate a list of metrics
         """
-        return {
-            "em": mean,
-            "f1": mean
-        }
+        return {"em": mean, "f1": mean}
 
     def higher_is_better(self):
         """
@@ -280,7 +267,4 @@ class DROP(Task):
             A dictionary where keys are the names of submetrics and values are
             whether a higher value of the submetric is better
         """
-        return {
-            "em": True,
-            "f1": True
-        }
+        return {"em": True, "f1": True}

lm_eval/tasks/glue.py

@@ -14,7 +14,7 @@ respect to a wide range of linguistic phenomena found in natural language.
 Homepage: https://gluebenchmark.com/
 """
 import numpy as np
-from lm_eval.base import rf, Task
+from lm_eval.base import PromptSourceTask, rf, Task
 from lm_eval.metrics import mean, matthews_corrcoef, f1_score, yesno
 from lm_eval.utils import general_detokenize
 
@@ -45,7 +45,7 @@ _CITATION = """
 # Single-Sentence Tasks
 
 
-class CoLA(Task):
+class CoLA(PromptSourceTask):
     VERSION = 0
     DATASET_PATH = "glue"
     DATASET_NAME = "cola"
@@ -67,37 +67,20 @@ class CoLA(Task):
     def validation_docs(self):
         return self.dataset["validation"]
 
-    def doc_to_text(self, doc):
-        return "{}\nQuestion: Does this sentence make sense?\nAnswer:".format(doc["sentence"])
+    # def process_results(self, doc, results):
+    #     answer_choices_list = self.prompt.get_answer_choices_list(doc)
+    #     pred = np.argmax(results)
+    #     target = answer_choices_list.index(self.doc_to_target(doc).strip())
+    #     return {"mcc": (target, pred)}
 
-    def doc_to_target(self, doc):
-        return " {}".format({1: "yes", 0: "no"}[doc["label"]])
+    # def higher_is_better(self):
+    #     return {"mcc": True}
 
-    def construct_requests(self, doc, ctx):
-        ll_true, _ = rf.loglikelihood(ctx, " yes")
-        ll_false, _ = rf.loglikelihood(ctx, " no")
-        return ll_true, ll_false
+    # def aggregation(self):
+    #     return {"mcc": matthews_corrcoef}
 
-    def process_results(self, doc, results):
-        ll_true, ll_false = results
-        pred = ll_true > ll_false
-        gold = doc["label"]
-        return {
-            "mcc": (gold, pred)
-        }
 
-    def higher_is_better(self):
-        return {
-            "mcc": True
-        }
-
-    def aggregation(self):
-        return {
-            "mcc": matthews_corrcoef
-        }
-
-
-class SST(Task):
+class SST(PromptSourceTask):
     VERSION = 0
     DATASET_PATH = "glue"
     DATASET_NAME = "sst2"
@@ -119,42 +102,11 @@ class SST(Task):
     def validation_docs(self):
         return self.dataset["validation"]
 
-    def doc_to_text(self, doc):
-        return "{}\nQuestion: Is this sentence positive or negative?\nAnswer:".format(
-            general_detokenize(doc["sentence"]),
-        )
-
-    def doc_to_target(self, doc):
-        return " {}".format({1: "positive", 0: "negative"}[doc["label"]])
-
-    def construct_requests(self, doc, ctx):
-        ll_positive, _ = rf.loglikelihood(ctx, " positive")
-        ll_negative, _ = rf.loglikelihood(ctx, " negative")
-        return ll_positive, ll_negative
-
-    def process_results(self, doc, results):
-        ll_positive, ll_negative = results
-        pred = ll_positive > ll_negative
-        gold = doc["label"]
-        return {
-            "acc": pred == gold
-        }
-
-    def higher_is_better(self):
-        return {
-            "acc": True
-        }
-
-    def aggregation(self):
-        return {
-            "acc": mean
-        }
-
 
 # Inference Tasks
 
 
-class MNLI(Task):
+class MNLI(PromptSourceTask):
     VERSION = 0
     DATASET_PATH = "glue"
     DATASET_NAME = "mnli"
@@ -181,41 +133,6 @@ class MNLI(Task):
         if self.has_test_docs():
             return self.dataset["test_matched"]
 
-    def doc_to_text(self, doc):
-        return "{}\nQuestion: {} True, False or Neither?\nAnswer:".format(
-            doc["premise"],
-            doc["hypothesis"].strip() + ('' if doc["hypothesis"].strip().endswith('.') else '.'),
-        )
-
-    def doc_to_target(self, doc):
-        # True = entailment
-        # False = contradiction
-        # Neither = neutral
-        return " {}".format({0: "True", 1: "Neither", 2: "False"}[doc["label"]])
-
-    def construct_requests(self, doc, ctx):
-        ll_true, _ = rf.loglikelihood(ctx, " True")
-        ll_neither, _ = rf.loglikelihood(ctx, " Neither")
-        ll_false, _ = rf.loglikelihood(ctx, " False")
-        return ll_true, ll_neither, ll_false
-
-    def process_results(self, doc, results):
-        gold = doc["label"]
-        pred = np.argmax(results)
-        return {
-            "acc": pred == gold
-        }
-
-    def higher_is_better(self):
-        return {
-            "acc": True
-        }
-
-    def aggregation(self):
-        return {
-            "acc": mean
-        }
-
 
 class MNLIMismatched(MNLI):
     VERSION = 0
@@ -229,7 +146,7 @@ class MNLIMismatched(MNLI):
             return self.dataset["test_mismatched"]
 
 
-class QNLI(Task):
+class QNLI(PromptSourceTask):
     VERSION = 0
     DATASET_PATH = "glue"
     DATASET_NAME = "qnli"
@@ -251,42 +168,8 @@ class QNLI(Task):
     def validation_docs(self):
         return self.dataset["validation"]
 
-    def doc_to_text(self, doc):
-        return "{}\n{}\nQuestion: Does this response answer the question?\nAnswer:".format(
-            doc["question"],
-            doc["sentence"],
-        )
-
-    def doc_to_target(self, doc):
-        # True = entailment
-        # False = not entailment
-        return " {}".format({0: "yes", 1: "no"}[doc["label"]])
-
-    def construct_requests(self, doc, ctx):
-        ll_yes, _ = rf.loglikelihood(ctx, " yes")
-        ll_no, _ = rf.loglikelihood(ctx, " no")
-        return ll_yes, ll_no
-
-    def process_results(self, doc, results):
-        ll_yes, ll_no = results
-        pred = ll_no > ll_yes
-        gold = doc["label"]
-        return {
-            "acc": pred == gold
-        }
 
-    def higher_is_better(self):
-        return {
-            "acc": True
-        }
-
-    def aggregation(self):
-        return {
-            "acc": mean
-        }
-
-
-class WNLI(Task):
+class WNLI(PromptSourceTask):
     VERSION = 1
     DATASET_PATH = "glue"
     DATASET_NAME = "wnli"
@@ -301,49 +184,13 @@ class WNLI(Task):
         return False
 
     def training_docs(self):
-        if self._training_docs is None:
-            self._training_docs = list(self.dataset["train"])
-        return self._training_docs
+        return self.dataset["train"]
 
     def validation_docs(self):
         return self.dataset["validation"]
 
-    def doc_to_text(self, doc):
-        return "{}\nQuestion: {} True or False?\nAnswer:".format(
-            doc["sentence1"],
-            doc["sentence2"],
-        )
-
-    def doc_to_target(self, doc):
-        # True = entailment
-        # False = not_entailment
-        return " {}".format({0: "False", 1: "True"}[doc["label"]])
-
-    def construct_requests(self, doc, ctx):
-        ll_true, _ = rf.loglikelihood(ctx, " True")
-        ll_false, _ = rf.loglikelihood(ctx, " False")
-        return ll_true, ll_false
-
-    def process_results(self, doc, results):
-        ll_true, ll_false = results
-        pred = ll_true > ll_false
-        gold = doc["label"]
-        return {
-            "acc": pred == gold
-        }
-
-    def higher_is_better(self):
-        return {
-            "acc": True
-        }
-
-    def aggregation(self):
-        return {
-            "acc": mean
-        }
-
 
-class RTE(Task):
+class RTE(PromptSourceTask):
     VERSION = 0
     DATASET_PATH = "glue"
     DATASET_NAME = "rte"
@@ -365,45 +212,17 @@ class RTE(Task):
     def validation_docs(self):
         return self.dataset["validation"]
 
-    def doc_to_text(self, doc):
-        return "{}\nQuestion: {} True or False?\nAnswer:".format(
-            doc["sentence1"],
-            doc["sentence2"],
-        )
-
-    def doc_to_target(self, doc):
-        # 0 = entailment
-        # 1 = not_entailment
-        return " {}".format({0: "True", 1: "False"}[doc["label"]])
-
-    def construct_requests(self, doc, ctx):
-        ll_true, _ = rf.loglikelihood(ctx, " True")
-        ll_false, _ = rf.loglikelihood(ctx, " False")
-        return ll_true, ll_false
-
-    def process_results(self, doc, results):
-        ll_true, ll_false = results
-        pred = ll_false > ll_true
-        gold = doc["label"]
-        return {
-            "acc": pred == gold
-        }
-
     def higher_is_better(self):
-        return {
-            "acc": True
-        }
+        return {"acc": True}
 
     def aggregation(self):
-        return {
-            "acc": mean
-        }
+        return {"acc": mean}
 
 
 # Similarity and Paraphrase Tasks
 
 
-class MRPC(Task):
+class MRPC(PromptSourceTask):
     VERSION = 0
     DATASET_PATH = "glue"
     DATASET_NAME = "mrpc"
@@ -417,6 +236,9 @@ class MRPC(Task):
     def has_test_docs(self):
         return False
 
+    def stopping_criteria(self):
+        return "\n"
+
     def training_docs(self):
         if self._training_docs is None:
             self._training_docs = list(self.dataset["train"])
@@ -425,43 +247,8 @@ class MRPC(Task):
     def validation_docs(self):
         return self.dataset["validation"]
 
-    def doc_to_text(self, doc):
-        return "Sentence 1: {}\nSentence 2: {}\nQuestion: Do both sentences mean the same thing?\nAnswer:".format(
-            general_detokenize(doc["sentence1"]),
-            general_detokenize(doc["sentence2"]),
-        )
-
-    def doc_to_target(self, doc):
-        return " {}".format(yesno(doc["label"]))
-
-    def construct_requests(self, doc, ctx):
-        ll_yes, _ = rf.loglikelihood(ctx, " yes")
-        ll_no, _ = rf.loglikelihood(ctx, " no")
-        return ll_yes, ll_no
-
-    def process_results(self, doc, results):
-        ll_yes, ll_no = results
-        gold = doc["label"]
-        pred = ll_yes > ll_no
-        return {
-            "acc": pred == gold,
-            "f1": (gold, pred),
-        }
-
-    def higher_is_better(self):
-        return {
-            "acc": True,
-            "f1": True
-        }
-
-    def aggregation(self):
-        return {
-            "acc": mean,
-            "f1": f1_score
-        }
-
 
-class QQP(Task):
+class QQP(PromptSourceTask):
     VERSION = 0
     DATASET_PATH = "glue"
     DATASET_NAME = "qqp"
@@ -483,41 +270,6 @@ class QQP(Task):
     def validation_docs(self):
         return self.dataset["validation"]
 
-    def doc_to_text(self, doc):
-        return "Question 1: {}\nQuestion 2: {}\nQuestion: Do both questions ask the same thing?\nAnswer:".format(
-            doc["question1"],
-            doc["question2"],
-        )
-
-    def doc_to_target(self, doc):
-        return " {}".format(yesno(doc["label"]))
-
-    def construct_requests(self, doc, ctx):
-        ll_yes, _ = rf.loglikelihood(ctx, " yes")
-        ll_no, _ = rf.loglikelihood(ctx, " no")
-        return ll_yes, ll_no
-
-    def process_results(self, doc, results):
-        ll_yes, ll_no = results
-        gold = doc["label"]
-        pred = ll_yes > ll_no
-        return {
-            "acc": pred == gold,
-            "f1": (gold, pred),
-        }
-
-    def higher_is_better(self):
-        return {
-            "acc": True,
-            "f1": True
-        }
-
-    def aggregation(self):
-        return {
-            "acc": mean,
-            "f1": f1_score
-        }
-
 
 class STSB(Task):
     VERSION = 0
@@ -554,7 +306,7 @@ class STSB(Task):
         return " {}".format(doc["label"])
 
     def construct_requests(self, doc, ctx):
-        """ Uses RequestFactory to construct Requests and returns an iterable of 
+        """Uses RequestFactory to construct Requests and returns an iterable of
         Requests which will be sent to the LM.
 
         :param doc:
@@ -565,7 +317,7 @@ class STSB(Task):
             part of the document for `doc`.
         """
         # TODO: implement evaluation.
-        raise NotImplementedError('Evaluation not implemented')
+        raise NotImplementedError("Evaluation not implemented")
 
     def process_results(self, doc, results):
         """Take a single document and the LM results and evaluates, returning a
@@ -578,7 +330,7 @@ class STSB(Task):
             The results of the requests created in construct_requests.
         """
         # TODO: implement evaluation.
-        raise NotImplementedError('Evaluation not implemented')
+        raise NotImplementedError("Evaluation not implemented")
 
     def aggregation(self):
         """
@@ -587,7 +339,7 @@ class STSB(Task):
             functions that aggregate a list of metrics
         """
         # TODO: implement evaluation.
-        raise NotImplementedError('Evaluation not implemented')
+        raise NotImplementedError("Evaluation not implemented")
 
     def higher_is_better(self):
         """
@@ -596,4 +348,4 @@ class STSB(Task):
             whether a higher value of the submetric is better
         """
         # TODO: implement evaluation.
-        raise NotImplementedError('Evaluation not implemented')
+        raise NotImplementedError("Evaluation not implemented")

lm_eval/tasks/hans.py

+"""
+Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference
+https://arxiv.org/abs/1902.01007
+
+A controlled evaluation set called HANS (Heuristic Analysis for NLI Systems),
+which contains many examples where the heuristics fail.
+
+Homepage: https://github.com/tommccoy1/hans
+"""
+from lm_eval.base import PromptSourceTask
+
+
+_CITATION = """
+@inproceedings{mccoy-etal-2019-right,
+    title = "Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference",
+    author = "McCoy, Tom  and
+      Pavlick, Ellie  and
+      Linzen, Tal",
+    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
+    month = jul,
+    year = "2019",
+    address = "Florence, Italy",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/P19-1334",
+    doi = "10.18653/v1/P19-1334",
+    pages = "3428--3448",
+    abstract = "A machine learning system can score well on a given test set by relying on heuristics that are effective for frequent example types but break down in more challenging cases. We study this issue within natural language inference (NLI), the task of determining whether one sentence entails another. We hypothesize that statistical NLI models may adopt three fallible syntactic heuristics: the lexical overlap heuristic, the subsequence heuristic, and the constituent heuristic. To determine whether models have adopted these heuristics, we introduce a controlled evaluation set called HANS (Heuristic Analysis for NLI Systems), which contains many examples where the heuristics fail. We find that models trained on MNLI, including BERT, a state-of-the-art model, perform very poorly on HANS, suggesting that they have indeed adopted these heuristics. We conclude that there is substantial room for improvement in NLI systems, and that the HANS dataset can motivate and measure progress in this area.",
+}
+"""
+
+
+class HANS(PromptSourceTask):
+    VERSION = 0
+    DATASET_PATH = "hans"
+    DATASET_NAME = None
+
+    def has_training_docs(self):
+        return True
+
+    def has_validation_docs(self):
+        return True
+
+    def has_test_docs(self):
+        return False
+
+    def training_docs(self):
+        if self.has_training_docs():
+            # We cache training documents in `self._training_docs` for faster
+            # few-shot processing. If the data is too large to fit in memory,
+            # return the training data as a generator instead of a list.
+            if self._training_docs is None:
+                self._training_docs = list(self.dataset["train"])
+            return self._training_docs
+
+    def validation_docs(self):
+        if self.has_validation_docs():
+            return self.dataset["validation"]
+
+    def test_docs(self):
+        if self.has_test_docs():
+            return self.dataset["test"]

lm_eval/tasks/race.py

@@ -12,7 +12,7 @@ Homepage: https://www.cs.cmu.edu/~glai1/data/race/
 import collections
 import datasets
 import numpy as np
-from lm_eval.base import rf, Task
+from lm_eval.base import PromptSourceTask, rf
 from lm_eval.metrics import mean
 
 
@@ -34,13 +34,13 @@ class each:
         return list(map(self.f, other))
 
 
-class RACE(Task):
+class RACE(PromptSourceTask):
     VERSION = 1
     DATASET_PATH = "race"
     DATASET_NAME = "high"
 
     cache = {}
-    letter_to_num = {'A': 0, 'B': 1, 'C': 2, 'D': 3}
+    letter_to_num = {"A": 0, "B": 1, "C": 2, "D": 3}
 
     def has_training_docs(self):
         return True
@@ -51,79 +51,88 @@ class RACE(Task):
     def has_test_docs(self):
         return True
 
-    def _collate_data(self, set):
-        if set in self.cache:
-            return self.cache[set]
-        # One big issue with HF's implementation of this dataset: it makes a
-        # separate document for each question; meanwhile, in the GPT3 paper it
-        # is shown that one document is made per passage.
-
-        r = collections.defaultdict(list)
-        for item in datasets.load_dataset(path=self.DATASET_PATH, name=self.DATASET_NAME)[set]:
-            r[item['article']].append(item)
-        
-        res = list(r.values() >> each(lambda x: {
-            'article': x[0]['article'],
-            'problems': x >> each(lambda y: {
-                'question': y['question'],
-                'answer': y['answer'],
-                'options': y['options'],
-            })
-        }))
-
-        self.cache[set] = res
-        return res
+    # def _collate_data(self, set):
+    #     if set in self.cache:
+    #         return self.cache[set]
+    #     # One big issue with HF's implementation of this dataset: it makes a
+    #     # separate document for each question; meanwhile, in the GPT3 paper it
+    #     # is shown that one document is made per passage.
+
+    #     r = collections.defaultdict(list)
+    #     for item in datasets.load_dataset(
+    #         path=self.DATASET_PATH, name=self.DATASET_NAME
+    #     )[set]:
+    #         r[item["article"]].append(item)
+
+    #     res = list(
+    #         r.values()
+    #         >> each(
+    #             lambda x: {
+    #                 "article": x[0]["article"],
+    #                 "problems": x
+    #                 >> each(
+    #                     lambda y: {
+    #                         "question": y["question"],
+    #                         "answer": y["answer"],
+    #                         "options": y["options"],
+    #                     }
+    #                 ),
+    #             }
+    #         )
+    #     )
+
+    #     self.cache[set] = res
+    #     return res
 
     def training_docs(self):
-        return self._collate_data("train")
+        return self.dataset["train"]
 
     def validation_docs(self):
-        return self._collate_data("validation")
+        return self.dataset["validation"]
 
     def test_docs(self):
-        return self._collate_data("test")
+        return self.dataset["test"]
 
     @classmethod
     def get_answer_option(cls, problem):
-        answer = cls.letter_to_num[problem['answer']]
-        return problem['options'][answer]
+        answer = cls.letter_to_num[problem["answer"]]
+        return problem["options"][answer]
 
     @classmethod
     def last_problem(cls, doc):
-        return doc['problems'][-1]
-
-    def doc_to_text(self, doc):
-        text = 'Article: ' + doc['article'] + '\n\n'
-        for problem in doc['problems'][:-1]:
-            if problem['question'][-6:] == '  _  .':
-                text += problem['question'][-5:] + self.get_answer_option(problem) + '\n'
-            else:
-                question = 'Question: ' + problem['question'] + '\n'
-                answer = 'Answer: ' + self.get_answer_option(problem) + '\n'
-                text += question + answer
-        text += self.last_problem(doc)['question']
-        return text
-
-    def doc_to_target(self, doc):
-        return " " + self.get_answer_option(self.last_problem(doc))
-
-    def construct_requests(self, doc, ctx):
-        """ Uses RequestFactory to construct Requests and returns an iterable of 
-        Requests which will be sent to the LM.
-
-        :param doc:
-            The document as returned from training_docs, validation_docs, or test_docs.
-        :param ctx: str
-            The context string, generated by fewshot_context. This includes the natural 
-            language description, as well as the few shot examples, and the question
-            part of the document for `doc`. 
-        """
-        problem = self.last_problem(doc)
-        ll_choices = [
-            rf.loglikelihood(ctx, " " + problem['options'][i])[0]
-            for i in range(4)
-        ]
-        return ll_choices
+        return doc["problems"][-1]
+
+    # def doc_to_text(self, doc):
+    #     text = 'Article: ' + doc['article'] + '\n\n'
+    #     for problem in doc['problems'][:-1]:
+    #         if problem['question'][-6:] == '  _  .':
+    #             text += problem['question'][-5:] + self.get_answer_option(problem) + '\n'
+    #         else:
+    #             question = 'Question: ' + problem['question'] + '\n'
+    #             answer = 'Answer: ' + self.get_answer_option(problem) + '\n'
+    #             text += question + answer
+    #     text += self.last_problem(doc)['question']
+    #     return text
+
+    # def doc_to_target(self, doc):
+    #     return " " + self.get_answer_option(self.last_problem(doc))
+
+    # def construct_requests(self, doc, ctx):
+    #     """Uses RequestFactory to construct Requests and returns an iterable of
+    #     Requests which will be sent to the LM.
+
+    #     :param doc:
+    #         The document as returned from training_docs, validation_docs, or test_docs.
+    #     :param ctx: str
+    #         The context string, generated by fewshot_context. This includes the natural
+    #         language description, as well as the few shot examples, and the question
+    #         part of the document for `doc`.
+    #     """
+    #     problem = self.last_problem(doc)
+    #     ll_choices = [
+    #         rf.loglikelihood(ctx, " " + problem["options"][i])[0] for i in range(4)
+    #     ]
+    #     return ll_choices
 
     def process_results(self, doc, results):
         """Take a single document and the LM results and evaluates, returning a
@@ -135,11 +144,11 @@ class RACE(Task):
         :param results:
             The results of the requests created in construct_requests.
         """
-        gold = self.letter_to_num[self.last_problem(doc)['answer']]
+        #
+        gold = self.letter_to_num[self.doc_to_target(doc)]
+        # gold = self.letter_to_num[self.last_problem(doc)["answer"]]
         pred = np.argmax(results)
-        return {
-            "acc": int(pred == gold)
-        }
+        return {"acc": int(pred == gold)}
 
     def aggregation(self):
         """
@@ -147,9 +156,7 @@ class RACE(Task):
             A dictionary where keys are the names of submetrics and values are
             functions that aggregate a list of metrics
         """
-        return {
-            "acc": mean
-        }
+        return {"acc": mean}
 
     def higher_is_better(self):
         """
@@ -157,6 +164,4 @@ class RACE(Task):
             A dictionary where keys are the names of submetrics and values are
             whether a higher value of the submetric is better
         """
-        return {
-            "acc": True
-        }
+        return {"acc": True}

lm_eval/tasks/superglue.py

@@ -12,7 +12,7 @@ TODO: WSC requires free-form generation.
 import numpy as np
 import sklearn
 import transformers.data.metrics.squad_metrics as squad_metrics
-from lm_eval.base import rf, Task
+from lm_eval.base import rf, PromptSourceTask
 from lm_eval.metrics import mean, acc_all, metric_max_over_ground_truths, yesno
 from lm_eval.utils import general_detokenize
 
@@ -32,7 +32,7 @@ _CITATION = """
 """
 
 
-class BoolQ(Task):
+class BoolQ(PromptSourceTask):
     VERSION = 1
     DATASET_PATH = "super_glue"
     DATASET_NAME = "boolq"
@@ -54,41 +54,8 @@ class BoolQ(Task):
     def validation_docs(self):
         return self.dataset["validation"]
 
-    def doc_to_text(self, doc):
-        return f"{doc['passage']}\nQuestion: {doc['question']}?\nAnswer:"
 
-    def doc_to_target(self, doc):
-        return " " + yesno(doc['label']) 
-
-    def construct_requests(self, doc, ctx):
-
-        ll_yes, _ = rf.loglikelihood(ctx, ' yes')
-        ll_no, _ = rf.loglikelihood(ctx, ' no')
-
-        return ll_yes, ll_no
-
-    def process_results(self, doc, results):
-        ll_yes, ll_no = results
-        gold = doc["label"]
-
-        acc = 1. if (ll_yes > ll_no) == gold else 0.
-
-        return {
-            "acc": acc
-        }
-    
-    def higher_is_better(self):
-        return {
-            "acc": True
-        }
-    
-    def aggregation(self):
-        return {
-            "acc": mean
-        }
-
-
-class CommitmentBank(Task):
+class CommitmentBank(PromptSourceTask):
     VERSION = 1
     DATASET_PATH = "super_glue"
     DATASET_NAME = "cb"
@@ -110,40 +77,15 @@ class CommitmentBank(Task):
     def validation_docs(self):
         return self.dataset["validation"]
 
-    def doc_to_text(self, doc):
-        return "{}\nQuestion: {}. True, False or Neither?\nAnswer:".format(
-            doc["premise"],
-            doc["hypothesis"],
-        )
-
-    def doc_to_target(self, doc):
-        # True = entailment
-        # False = contradiction
-        # Neither = neutral
-        return " {}".format({0: "True", 1: "False", 2: "Neither"}[doc["label"]])
-
-    def construct_requests(self, doc, ctx):
-        ll_true, _ = rf.loglikelihood(ctx, ' True')
-        ll_false, _ = rf.loglikelihood(ctx, ' False')
-        ll_neither, _ = rf.loglikelihood(ctx, ' Neither')
-
-        return ll_true, ll_false, ll_neither
-
     def process_results(self, doc, results):
         gold = doc["label"]
         pred = np.argmax(results)
-        acc = 1. if pred == gold else 0.
+        acc = 1.0 if pred == gold else 0.0
 
-        return {
-            "acc": acc,
-            "f1": (pred, gold)
-        }
+        return {"acc": acc, "f1": (pred, gold)}
 
     def higher_is_better(self):
-        return {
-            "acc": True,
-            "f1": True
-        }
+        return {"acc": True, "f1": True}
 
     @classmethod
     def cb_multi_fi(cls, items):
@@ -163,7 +105,7 @@ class CommitmentBank(Task):
         }
 
 
-class Copa(Task):
+class Copa(PromptSourceTask):
     VERSION = 0
     DATASET_PATH = "super_glue"
     DATASET_NAME = "copa"
@@ -185,53 +127,25 @@ class Copa(Task):
     def validation_docs(self):
         return self.dataset["validation"]
 
-    def doc_to_text(self, doc):
-        # Drop the period
-        connector = {
-            "cause": "because",
-            "effect": "therefore",
-        }[doc["question"]]
-        return doc["premise"].strip()[:-1] + f" {connector}"
-
-    def doc_to_target(self, doc):
-        correct_choice = doc["choice1"] if doc["label"] == 0 else doc["choice2"]
-        # Connect the sentences
-        return " " + self.convert_choice(correct_choice)
-
-    def construct_requests(self, doc, ctx):
-        choice1 = " " + self.convert_choice(doc["choice1"])
-        choice2 = " " + self.convert_choice(doc["choice2"])
-        
-        ll_choice1, _ = rf.loglikelihood(ctx, choice1)
-        ll_choice2, _ = rf.loglikelihood(ctx, choice2)
-
-        return ll_choice1, ll_choice2
-
     def process_results(self, doc, results):
         gold = doc["label"]
         pred = np.argmax(results)
-        acc = 1. if pred == gold else 0.
+        acc = 1.0 if pred == gold else 0.0
 
-        return {
-            "acc": acc
-        }
+        return {"acc": acc}
 
     def higher_is_better(self):
-        return {
-            "acc": True
-        }
+        return {"acc": True}
 
     def aggregation(self):
-        return {
-            "acc": mean
-        }
+        return {"acc": mean}
 
     @staticmethod
     def convert_choice(choice):
         return choice[0].lower() + choice[1:]
 
 
-class MultiRC(Task):
+class MultiRC(PromptSourceTask):
     VERSION = 1
     DATASET_PATH = "super_glue"
     DATASET_NAME = "multirc"
@@ -253,45 +167,19 @@ class MultiRC(Task):
     def validation_docs(self):
         return self.dataset["validation"]
 
-    def doc_to_text(self, doc):
-        return f"{doc['paragraph']}\nQuestion: {doc['question']}\nAnswer:"
-
-    def doc_to_target(self, doc):
-        return " " + self.format_answer(answer=doc["answer"], label=doc["label"])
-
-    @staticmethod
-    def format_answer(answer, label):
-        label_str = "yes" if label else "no"
-        return f"{answer}\nIs the answer correct? {label_str}"
-
-    def construct_requests(self, doc, ctx):
-        true_choice = self.format_answer(answer=doc["answer"], label=True)
-        false_choice = self.format_answer(answer=doc["answer"], label=False)
-        
-        ll_true_choice, _ = rf.loglikelihood(ctx, f' {true_choice}')
-        ll_false_choice, _ = rf.loglikelihood(ctx, f' {false_choice}')
-
-        return ll_true_choice, ll_false_choice
-
     def process_results(self, doc, results):
         ll_true_choice, ll_false_choice = results
         pred = ll_true_choice > ll_false_choice
-        return {
-            "acc": (pred, doc)
-        }
+        return {"acc": (pred, doc)}
 
     def higher_is_better(self):
-        return {
-            "acc": True
-        }
+        return {"acc": True}
 
     def aggregation(self):
-        return {
-            "acc": acc_all
-        }
+        return {"acc": acc_all}
 
 
-class ReCoRD(Task):
+class ReCoRD(PromptSourceTask):
     VERSION = 0
     DATASET_PATH = "super_glue"
     DATASET_NAME = "record"
@@ -311,56 +199,31 @@ class ReCoRD(Task):
         if self._training_docs is None:
             self._training_docs = []
             for doc in self.dataset["train"]:
-                self._training_docs.append(self._process_doc(doc))
+                self._training_docs.append(doc)
         return self._training_docs
 
     def validation_docs(self):
         # See: training_docs
         for doc in self.dataset["validation"]:
-            yield self._process_doc(doc)
-
-    @classmethod
-    def _process_doc(cls, doc):
-        return {
-            "passage": doc["passage"],
-            "query": doc["query"],
-            "entities": sorted(list(set(doc["entities"]))),
-            "answers": sorted(list(set(doc["answers"]))),
-        }
-
-    def doc_to_text(self, doc):
-        initial_text, *highlights = doc["passage"].strip().split("\n@highlight\n")
-        text = initial_text + "\n\n"
-        for highlight in highlights:
-            text += f"  - {highlight}.\n"
-        return text
-
-    @classmethod
-    def format_answer(cls, query, entity):
-        return f'  - {query}'.replace("@placeholder", entity)
-
-    def doc_to_target(self, doc):
-        # We only output the first correct entity in a doc
-        return self.format_answer(query=doc["query"], entity=doc["answers"][0])
-
-    def construct_requests(self, doc, ctx):
-        requests = [
-            rf.loglikelihood(ctx, self.format_answer(query=doc["query"], entity=entity))
-            for entity in doc["entities"]
-        ]
-        return requests
+            yield doc
 
     def process_results(self, doc, results):
         # ReCoRD's evaluation is actually deceptively simple:
         # - Pick the maximum likelihood prediction entity
         # - Evaluate the accuracy and token F1 PER EXAMPLE
         # - Average over all examples
+
+        # TODO (jon-tow): Look at result
         max_idx = np.argmax(np.array([result[0] for result in results]))
 
         prediction = doc["entities"][max_idx]
         gold_label_set = doc["answers"]
-        f1 = metric_max_over_ground_truths(squad_metrics.compute_f1, prediction, gold_label_set)
-        em = metric_max_over_ground_truths(squad_metrics.compute_exact, prediction, gold_label_set)
+        f1 = metric_max_over_ground_truths(
+            squad_metrics.compute_f1, prediction, gold_label_set
+        )
+        em = metric_max_over_ground_truths(
+            squad_metrics.compute_exact, prediction, gold_label_set
+        )
 
         return {
             "f1": f1,
@@ -380,7 +243,7 @@ class ReCoRD(Task):
         }
 
 
-class WordsInContext(Task):
+class WordsInContext(PromptSourceTask):
     VERSION = 0
     DATASET_PATH = "super_glue"
     DATASET_NAME = "wic"
@@ -402,50 +265,19 @@ class WordsInContext(Task):
     def validation_docs(self):
         return self.dataset["validation"]
 
-    def doc_to_text(self, doc):
-        return "Sentence 1: {}\nSentence 2: {}\nQuestion: Is the word '{}' used in the same way in the" \
-               " two sentences above?\nAnswer:".format(
-                    doc["sentence1"],
-                    doc["sentence2"],
-                    doc["sentence1"][doc["start1"]:doc["end1"]],
-                )
-
-    def doc_to_target(self, doc):
-        return " {}".format({0: "no", 1: "yes"}[doc["label"]])
-
-    def construct_requests(self, doc, ctx):
-        ll_yes, _ = rf.loglikelihood(ctx, ' yes')
-        ll_no, _ = rf.loglikelihood(ctx, ' no')
-
-        return ll_yes, ll_no
-
-    def process_results(self, doc, results):
-        ll_yes, ll_no = results
-        gold = doc["label"]
-
-        acc = 1. if (ll_yes > ll_no) == gold else 0.
-
-        return {
-            "acc": acc
-        }
-
     def higher_is_better(self):
-        return {
-            "acc": True
-        }
+        return {"acc": True}
 
     def aggregation(self):
-        return {
-            "acc": mean
-        }
+        return {"acc": mean}
 
 
-class SGWinogradSchemaChallenge(Task):
+class SGWinogradSchemaChallenge(PromptSourceTask):
     VERSION = 0
     # Note: This implementation differs from Fig G.32 because this is the SuperGLUE,
     #       binary version of the task.
     DATASET_PATH = "super_glue"
-    DATASET_NAME = "wsc"
+    DATASET_NAME = "wsc.fixed"
 
     def has_training_docs(self):
         return True
@@ -461,56 +293,15 @@ class SGWinogradSchemaChallenge(Task):
             if self._training_docs is None:
                 # GPT-3 Paper's format only uses positive examples for fewshot "training"
                 self._training_docs = [
-                    doc for doc in
-                    self.dataset["train"]
-                    if doc["label"]
+                    doc for doc in self.dataset["train"] if doc["label"]
                 ]
             return self._training_docs
 
     def validation_docs(self):
         return self.dataset["validation"]
 
-    def doc_to_text(self, doc):
-        raw_passage = doc["text"]
-        # NOTE: HuggingFace span indices are word-based not character-based.
-        pre = " ".join(raw_passage.split()[:doc["span2_index"]])
-        post = raw_passage[len(pre) + len(doc["span2_text"]) + 1:]
-        passage = general_detokenize(pre + " *{}*".format(doc['span2_text']) + post)
-        noun = doc["span1_text"]
-        pronoun = doc["span2_text"]
-        text = (
-            f"Passage: {passage}\n"
-            + f"Question: In the passage above, does the pronoun \"*{pronoun}*\" refer to \"*{noun}*\"?\n"
-            + "Answer:"
-        )
-        return text
-
-    def doc_to_target(self, doc):
-        return " " + yesno(doc['label'])
-
-    def construct_requests(self, doc, ctx):
-
-        ll_yes, _ = rf.loglikelihood(ctx, ' yes')
-        ll_no, _ = rf.loglikelihood(ctx, ' no')
-
-        return ll_yes, ll_no
-
-    def process_results(self, doc, results):
-        ll_yes, ll_no = results
-        gold = doc["label"]
-
-        acc = 1. if (ll_yes > ll_no) == gold else 0.
-
-        return {
-            "acc": acc
-        }
-
     def higher_is_better(self):
-        return {
-            "acc": True
-        }
+        return {"acc": True}
 
     def aggregation(self):
-        return {
-            "acc": mean
-        }
+        return {"acc": mean}

lm_eval/utils.py

@@ -146,6 +146,19 @@ class Reorderer:
         
         return res
 
+
+def flatten(d, parent_key='', sep='_'):
+    # From: https://stackoverflow.com/a/6027615
+    items = []
+    for k, v in d.items():
+        new_key = parent_key + sep + k if parent_key else k
+        if isinstance(v, collections.MutableMapping):
+            items.extend(flatten(v, new_key, sep=sep).items())
+        else:
+            items.append((new_key, v))
+    return dict(items)
+
+
 def positional_deprecated(fn):
     """
     A decorator to nudge users into passing only keyword args (`kwargs`) to the 

scripts/write_out.py

@@ -30,7 +30,7 @@ def main():
         task_names = tasks.ALL_TASKS
     else:
         task_names = args.tasks.split(",")
-    task_dict = tasks.get_task_dict(task_names)
+    task_dict = tasks.get_task_dict_promptsource(task_names)
 
     description_dict = {}
     if args.description_dict_path:

setup.py

@@ -18,8 +18,12 @@ setuptools.setup(
         "License :: OSI Approved :: MIT License",
         "Operating System :: OS Independent",
     ],
-    python_requires='>=3.6',
+    python_requires=">=3.6",
     install_requires=[
+        "promptsource @ git+https://github.com/bigscience-workshop/promptsource@eval-hackathon",
+        "wrapt",
+        "nltk",
+        "jinja2",
         "black",
         "datasets==2.0.0",
         "click>=7.1",
@@ -42,9 +46,9 @@ setuptools.setup(
         "openai==0.6.4",
         "jieba==0.42.1",
         "nagisa==0.2.7",
-        "bleurt@https://github.com/google-research/bleurt/archive/b610120347ef22b494b6d69b4316e303f5932516.zip#egg=bleurt"
+        "bleurt@https://github.com/google-research/bleurt/archive/b610120347ef22b494b6d69b4316e303f5932516.zip#egg=bleurt",
     ],
     dependency_links=[
         "https://github.com/google-research/bleurt/archive/b610120347ef22b494b6d69b4316e303f5932516.zip#egg=bleurt",
-    ]
+    ],
 )