Merge remote-tracking branch 'upstream/master' into results

README.md

@@ -10,9 +10,11 @@ This project provides a unified framework to test generative language models on
 Features:
 
 - 200+ tasks implemented. See the [task-table](./docs/task_table.md) for a complete list.
-- Support for the Hugging Face `transformers` library, GPT-NeoX, Megatron-DeepSpeed, and the OpenAI API, with flexible tokenization-agnostic interface.
+- Support for models loaded via [transformers](https://github.com/huggingface/transformers/), [GPT-NeoX](https://github.com/EleutherAI/gpt-neox), and [Megatron-DeepSpeed](https://github.com/microsoft/Megatron-DeepSpeed/), with a flexible tokenization-agnostic interface.
+- Support for commercial APIs including [OpenAI](https://openai.com), [goose.ai](https://goose.ai), and [TextSynth](https://textsynth.com/).
 - Support for evaluation on adapters (e.g. LoRa) supported in [HuggingFace's PEFT library](https://github.com/huggingface/peft).
-- Task versioning to ensure reproducibility.
+- Evaluating with publicly available prompts ensures reproducibility and comparability between papers.
+- Task versioning to ensure reproducibility when tasks are updated.
 
 ## Install
 
@@ -34,14 +36,16 @@ pip install -e ".[multilingual]"
 
 > **Note**: When reporting results from eval harness, please include the task versions (shown in `results["versions"]`) for reproducibility. This allows bug fixes to tasks while also ensuring that previously reported scores are reproducible. See the [Task Versioning](#task-versioning) section for more info.
 
-To evaluate a model hosted on the [HuggingFace Hub](https://huggingface.co/models) (e.g. GPT-J-6B) on tasks with names matching the pattern `lambada_*` and `hellaswag` you can use the following command:
+### Hugging Face `transformers`
+
+To evaluate a model hosted on the [HuggingFace Hub](https://huggingface.co/models) (e.g. GPT-J-6B) on `hellaswag` you can use the following command:
 
 
 ```bash
 python main.py \
     --model hf-causal \
     --model_args pretrained=EleutherAI/gpt-j-6B \
-    --tasks lambada_*,hellaswag \
+    --tasks hellaswag \
     --device cuda:0
 ```
 
@@ -59,16 +63,9 @@ To evaluate models that are loaded via `AutoSeq2SeqLM` in Huggingface, you inste
 
 > **Warning**: Choosing the wrong model may result in erroneous outputs despite not erroring.
 
-To use with [PEFT](https://github.com/huggingface/peft), take the call you would run to evaluate the base model and add `,peft=PATH` to the `model_args` argument as shown below:
-```bash
-python main.py \
-    --model hf-causal-experimental \
-    --model_args pretrained=EleutherAI/gpt-j-6b,peft=nomic-ai/gpt4all-j-lora \
-    --tasks openbookqa,arc_easy,winogrande,hellaswag,arc_challenge,piqa,boolq \
-    --device cuda:0
-```
+### Commercial APIs
 
-Our library also supports the OpenAI API:
+Our library also supports language models served via the OpenAI API:
 
 ```bash
 export OPENAI_API_SECRET_KEY=YOUR_KEY_HERE
@@ -90,7 +87,9 @@ python main.py \
     --check_integrity
 ```
 
-To evaluate mesh-transformer-jax models that are not available on HF, please invoke eval harness through [this script](https://github.com/kingoflolz/mesh-transformer-jax/blob/master/eval_harness.py).
+### Other Frameworks
+
+A number of other libraries contain scripts for calling the eval harness through their library. These include [GPT-NeoX](https://github.com/EleutherAI/gpt-neox/blob/main/eval_tasks/eval_adapter.py), [Megatron-DeepSpeed](https://github.com/microsoft/Megatron-DeepSpeed/blob/main/examples/MoE/readme_evalharness.md), and [mesh-transformer-jax](https://github.com/kingoflolz/mesh-transformer-jax/blob/master/eval_harness.py).
 
 💡 **Tip**: You can inspect what the LM inputs look like by running the following command:
 
@@ -104,6 +103,22 @@ python write_out.py \
 
 This will write out one text file for each task.
 
+## Advanced Usage
+
+For models loaded with the HuggingFace  `transformers` library, any arguments provided via `--model_args` get passed to the relevant constructor directly. This means that anything you can do with `AutoModel` can be done with our library. For example, you can pass a local path via `pretrained=` or use models finetuned with [PEFT](https://github.com/huggingface/peft) by taking the call you would run to evaluate the base model and add `,peft=PATH` to the `model_args` argument:
+```bash
+python main.py \
+    --model hf-causal-experimental \
+    --model_args pretrained=EleutherAI/gpt-j-6b,peft=nomic-ai/gpt4all-j-lora \
+    --tasks openbookqa,arc_easy,winogrande,hellaswag,arc_challenge,piqa,boolq \
+    --device cuda:0
+```
+
+
+We support wildcards in task names, for example you can run all of the machine-translated lambada tasks via `--task lambada_openai_mt_*`.
+
+We currently only support one prompt per task, which we strive to make the "standard" as defined by the benchmark's authors. If you would like to study how varying prompts causes changes in the evaluation score, check out the [BigScience fork](https://github.com/bigscience-workshop/lm-evaluation-harness) of this repo. We are currently working on upstreaming this capability to `main`.
+
 ## Implementing new tasks
 
 To implement a new task in the eval harness, see [this guide](./docs/task_guide.md).

ignore.txt

 ROUGE
 rouge
 nin
+maka
+mor
+te

lm_eval/base.py

@@ -190,14 +190,19 @@ class BaseLM(LM):
 
         # automatic batch size detection for vectorization
         adaptive_batch_size = None
-        if self.batch_size == 'auto': 
+        if self.batch_size == "auto":
             # using rolling window with maximum context
-            print('Passed argument batch_size = auto. Detecting largest batch size')
-            @find_executable_batch_size(starting_batch_size=512) # if OOM, then halves batch_size and tries again
+            print("Passed argument batch_size = auto. Detecting largest batch size")
+
+            @find_executable_batch_size(
+                starting_batch_size=512
+            )  # if OOM, then halves batch_size and tries again
             def forward_batch(batch_size):
-                test_batch = torch.ones((batch_size, self.max_length), device=self.device).long()
+                test_batch = torch.ones(
+                    (batch_size, self.max_length), device=self.device
+                ).long()
                 for _ in range(5):
-                    out = F.log_softmax(self._model_call(test_batch), dim = -1).cpu()
+                    _ = F.log_softmax(self._model_call(test_batch), dim=-1).cpu()
                 return batch_size
 
             batch_size = forward_batch()
@@ -223,7 +228,9 @@ class BaseLM(LM):
             # TODO: extract out this call so it only gets called once and also somehow figure out partial caching for
             # that
             string_nll = self._loglikelihood_tokens(
-                rolling_token_windows, disable_tqdm=True, override_bs = adaptive_batch_size
+                rolling_token_windows,
+                disable_tqdm=True,
+                override_bs=adaptive_batch_size,
             )
 
             # discard is_greedy
@@ -234,7 +241,7 @@ class BaseLM(LM):
 
         return loglikelihoods
 
-    def _loglikelihood_tokens(self, requests, disable_tqdm=False, override_bs = None):
+    def _loglikelihood_tokens(self, requests, disable_tqdm=False, override_bs=None):
         # TODO: implement some kind of efficient-request-middleware that lumps together requests with the same context
         res = []
 
@@ -249,11 +256,11 @@ class BaseLM(LM):
             toks = x[1] + x[2]
             return -len(toks), tuple(toks)
 
-        
         re_ord = utils.Reorderer(requests, _collate)
 
         # automatic (variable) batch size detection for vectorization
         # pull longest context sample from request
+        if len(re_ord.get_reordered()) > 0:
             _, context_enc, continuation_enc = re_ord.get_reordered()[0]
             max_context = len((context_enc + continuation_enc)[-(self.max_length + 1) :][:-1])
             if (self.batch_size == 'auto'):
@@ -273,9 +280,12 @@ class BaseLM(LM):
 
                 else:
                     adaptive_batch_size = override_bs
+        else:
+            adaptive_batch_size = 0 if override_bs is None else override_bs
 
         for chunk in utils.chunks(
-            tqdm(re_ord.get_reordered(), disable=disable_tqdm), self.batch_size if self.batch_size != "auto" else adaptive_batch_size
+            tqdm(re_ord.get_reordered(), disable=disable_tqdm),
+            self.batch_size if self.batch_size != "auto" else adaptive_batch_size,
         ):
             inps = []
             cont_toks_list = []
@@ -382,7 +392,7 @@ class BaseLM(LM):
         re_ord = utils.Reorderer(requests, _collate)
 
         for context, request_args in tqdm(re_ord.get_reordered()):
-            until = request_args['until']
+            until = request_args["until"]
             if isinstance(until, str):
                 until = [until]
 
@@ -396,7 +406,7 @@ class BaseLM(LM):
             ).to(self.device)
 
             max_gen_tokens = min(
-                self.max_gen_toks, request_args.get('max_length', self.max_gen_toks)
+                self.max_gen_toks, request_args.get("max_length", self.max_gen_toks)
             )
             cont = self._model_generate(
                 context_enc, context_enc.shape[1] + max_gen_tokens, primary_until

lm_eval/models/gpt2.py

@@ -3,6 +3,7 @@ import transformers
 from typing import Optional
 from lm_eval.base import BaseLM
 
+
 class HFLM(BaseLM):
     def __init__(
         self,
@@ -20,9 +21,11 @@ class HFLM(BaseLM):
 
         assert isinstance(device, str)
         assert isinstance(pretrained, str)
-        assert isinstance(batch_size, (int,str))
+        assert isinstance(batch_size, (int, str))
 
-        device_list = set(["cuda", "cpu"] + [f'cuda:{i}' for i in range(torch.cuda.device_count())])
+        device_list = set(
+            ["cuda", "cpu"] + [f"cuda:{i}" for i in range(torch.cuda.device_count())]
+        )
         if device and device in device_list:
             self._device = torch.device(device)
             print(f"Using device '{device}'")
@@ -66,7 +69,7 @@ class HFLM(BaseLM):
             ], self.tokenizer.encode("hello\n\nhello")
 
         # setup for automatic batch size detection
-        if batch_size == 'auto': 
+        if batch_size == "auto":
             self.batch_size_per_gpu = batch_size
         else:
             self.batch_size_per_gpu = int(batch_size)
@@ -116,9 +119,10 @@ class HFLM(BaseLM):
             return self.gpt2(inps)[0]
 
     def _model_generate(self, context, max_length, eos_token_id):
-        generation_kwargs = {'do_sample': False, 'max_length': max_length}
+        generation_kwargs = {"do_sample": False, "max_length": max_length}
         if eos_token_id is not None:
             generation_kwargs['eos_token_id'] = eos_token_id
+            generation_kwargs['pad_token_id'] = eos_token_id # setting eos_token_id as pad token
         return self.gpt2.generate(context, **generation_kwargs)
 
 

lm_eval/models/huggingface.py

@@ -72,7 +72,7 @@ class HuggingFaceAutoLM(BaseLM):
         tokenizer: Optional[str] = None,
         subfolder: Optional[str] = None,
         revision: Optional[str] = "main",
-        batch_size: Optional[Union[int,str]] = 1,
+        batch_size: Optional[Union[int, str]] = 1,
         max_gen_toks: Optional[int] = 256,
         max_length: Optional[int] = None,
         add_special_tokens: Optional[bool] = None,
@@ -159,7 +159,7 @@ class HuggingFaceAutoLM(BaseLM):
             ), "Evaluating causal models with `add_special_tokens=True` is currently not supported."
 
         # setup for automatic batch size detection
-        if batch_size == 'auto': 
+        if batch_size == "auto":
             self._batch_size = batch_size
         else:
             self._batch_size = int(batch_size)
@@ -369,7 +369,9 @@ class HuggingFaceAutoLM(BaseLM):
     def tok_decode(self, tokens: torch.LongTensor) -> List[str]:
         return self.tokenizer.batch_decode(tokens, skip_special_tokens=True)
 
-    def greedy_until(self, requests: List[Tuple[str, Union[List[str], str]]]) -> List[str]:
+    def greedy_until(
+        self, requests: List[Tuple[str, Union[List[str], str]]]
+    ) -> List[str]:
         def _collate(x):
             tokens = self.tok_encode(x[0])
             return len(tokens), x[0]
@@ -378,14 +380,19 @@ class HuggingFaceAutoLM(BaseLM):
         reorder = utils.Reorderer(requests, _collate)
 
         adaptive_batch_size = None
-        if self.batch_size == 'auto': 
+        if self.batch_size == "auto":
             # using rolling window with maximum context
-            print('Passed argument batch_size = auto. Detecting largest batch size')
-            @find_executable_batch_size(starting_batch_size=512) # if OOM, then halves batch_size and tries again
+            print("Passed argument batch_size = auto. Detecting largest batch size")
+
+            @find_executable_batch_size(
+                starting_batch_size=512
+            )  # if OOM, then halves batch_size and tries again
             def forward_batch(batch_size):
-                test_batch = torch.ones((batch_size, self.max_length), device=self.device).long()
+                test_batch = torch.ones(
+                    (batch_size, self.max_length), device=self.device
+                ).long()
                 for _ in range(5):
-                    out = F.log_softmax(self._model_call(test_batch), dim = -1).cpu()
+                    _ = F.log_softmax(self._model_call(test_batch), dim=-1).cpu()
                 return batch_size
 
             batch_size = forward_batch()
@@ -393,11 +400,12 @@ class HuggingFaceAutoLM(BaseLM):
             adaptive_batch_size = batch_size
 
         for chunk in utils.chunks(
-            tqdm(reorder.get_reordered(), disable=False), self.batch_size if self.batch_size != "auto" else adaptive_batch_size
+            tqdm(reorder.get_reordered(), disable=False),
+            self.batch_size if self.batch_size != "auto" else adaptive_batch_size,
         ):
             context = [c[0] for c in chunk]
             request_args = chunk[0][1]
-            stop = request_args.get('until', None)
+            stop = request_args.get("until", None)
             stop_sequences = stop if isinstance(stop, list) else [stop]
             max_generation_length = request_args.get("max_length", None)
 

lm_eval/models/textsynth.py

@@ -124,7 +124,7 @@ class TextSynthLM(BaseLM):
         for request in tqdm(requests):
             inp = request[0]
             request_args = request[1]
-            until = request_args['until']
+            until = request_args["until"]
             response = textsynth_completion(
                 url=self.api_url + "/v1/engines/" + self.engine + "/completions",
                 headers={"Authorization": "Bearer " + self.api_key},

lm_eval/tasks/__init__.py

@@ -52,7 +52,13 @@ from . import gsm8k
 from . import storycloze
 from . import toxigen
 from . import crowspairs
+from . import xcopa
 from . import bigbench
+from . import xstorycloze
+from . import xwinograd
+from . import pawsx
+from . import xnli
+from . import mgsm
 
 ########################################
 # Translation tasks
@@ -311,7 +317,13 @@ TASK_REGISTRY = {
     # "storycloze_2016": storycloze.StoryCloze2016,
     # "storycloze_2018": storycloze.StoryCloze2018,
     # "sat": sat.SATAnalogies,
+    **xcopa.construct_tasks(),
     **bigbench.create_all_tasks(),
+    **xstorycloze.create_all_tasks(),
+    **xwinograd.create_all_tasks(),
+    **pawsx.construct_tasks(),
+    **xnli.construct_tasks(),
+    **mgsm.construct_tasks(),
 }
 
 

lm_eval/tasks/coqa.py

@@ -141,7 +141,7 @@ class CoQA(Task):
             language description, as well as the few shot examples, and the question
             part of the document for `doc`.
         """
-        cont_request = rf.greedy_until(ctx, {'until': ["\nQ:"]})
+        cont_request = rf.greedy_until(ctx, {"until": ["\nQ:"]})
         return cont_request
 
     def process_results(self, doc, results):

lm_eval/tasks/drop.py

@@ -134,7 +134,7 @@ class DROP(Task):
             language description, as well as the few shot examples, and the question
             part of the document for `doc`.
         """
-        conts = [rf.greedy_until(ctx, {'until': ["."]})]
+        conts = [rf.greedy_until(ctx, {"until": ["."]})]
         return conts
 
     def process_results(self, doc, results):

lm_eval/tasks/gsm8k.py

@@ -79,7 +79,7 @@ class GradeSchoolMath8K(Task):
         """
         # NOTE: The paper implements "verifiers" that assign a score to multiple
         # solutions and output the highest ranked solution.
-        completion = rf.greedy_until(ctx, {'until': ["\n"]})
+        completion = rf.greedy_until(ctx, {"until": [":", "Question:", "Question"]})
         return completion
 
     def _extract_answer(self, completion):

lm_eval/tasks/hendrycks_math.py

@@ -63,7 +63,7 @@ class Math(Task):
         return " " + doc["solution"]
 
     def construct_requests(self, doc, ctx):
-        return rf.greedy_until(ctx, {'until': ["\n"]})
+        return rf.greedy_until(ctx, {"until": ["\n"]})
 
     def process_results(self, doc, results):
         retval = 0

lm_eval/tasks/mgsm.py

+"""
+Language Models are Multilingual Chain-of-Thought Reasoners
+https://arxiv.org/abs/2210.03057
+
+Multilingual Grade School Math Benchmark (MGSM) is a benchmark of grade-school math problems, proposed in the paper [Language models are multilingual chain-of-thought reasoners](http://arxiv.org/abs/2210.03057).
+
+The same 250 problems from [GSM8K](https://arxiv.org/abs/2110.14168) are each translated via human annotators in 10 languages. The 10 languages are:
+- Spanish
+- French
+- German
+- Russian
+- Chinese
+- Japanese
+- Thai
+- Swahili
+- Bengali
+- Telugu
+
+GSM8K (Grade School Math 8K) is a dataset of 8.5K high quality linguistically diverse grade school math word problems. The dataset was created to support the task of question answering on basic mathematical problems that require multi-step reasoning.
+
+You can find the input and targets for each of the ten languages (and English) as `.tsv` files.
+We also include few-shot exemplars that are also manually translated from each language in `exemplars.py`.
+
+Homepage: https://github.com/google-research/url-nlp/tree/main/mgsm
+"""
+import re
+from lm_eval.base import Task, rf
+from lm_eval.metrics import mean
+
+
+_CITATION = """
+@misc{cobbe2021training,
+    title={Training Verifiers to Solve Math Word Problems},
+    author={Karl Cobbe and Vineet Kosaraju and Mohammad Bavarian and Jacob Hilton and Reiichiro Nakano and Christopher Hesse and John Schulman},
+    year={2021},
+    eprint={2110.14168},
+    archivePrefix={arXiv},
+    primaryClass={cs.LG}
+}
+@misc{shi2022language,
+    title={Language Models are Multilingual Chain-of-Thought Reasoners},
+    author={Freda Shi and Mirac Suzgun and Markus Freitag and Xuezhi Wang and Suraj Srivats and Soroush Vosoughi and Hyung Won Chung and Yi Tay and Sebastian Ruder and Denny Zhou and Dipanjan Das and Jason Wei},
+    year={2022},
+    eprint={2210.03057},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+"""
+
+ANS_RE = re.compile(r"(\-?\d+)")
+INVALID_ANS = "[invalid]"
+
+
+class MGSM(Task):
+    VERSION = 0
+    DATASET_PATH = "juletxara/mgsm"
+    DATASET_NAME = None
+    QUESTION = "Question:"
+    ANSWER = "Step-by-Step Answer:"
+
+    def has_training_docs(self):
+        return True
+
+    def has_validation_docs(self):
+        return False
+
+    def has_test_docs(self):
+        return True
+
+    def training_docs(self):
+        return self.dataset["train"]
+
+    def validation_docs(self):
+        raise NotImplementedError
+
+    def test_docs(self):
+        return self.dataset["test"]
+
+    def doc_to_text(self, doc):
+        if doc["answer"] is not None:
+            return doc["question"] + "\n" + self.ANSWER
+        else:
+            return self.QUESTION + " " + doc["question"] + "\n" + self.ANSWER
+
+    def doc_to_target(self, doc):
+        if doc["answer"] is not None:
+            return " " + doc["answer"][len(self.ANSWER) + 1 :]
+        else:
+            return " " + str(doc["answer_number"])
+
+    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`.
+        """
+        completion = rf.greedy_until(ctx, {"until": ["\n", ":", self.QUESTION]})
+        return completion
+
+    def _extract_answer(self, completion):
+        match = re.findall(ANS_RE, completion)
+        if match:
+            return int(match[-1])
+        else:
+            return INVALID_ANS
+
+    def _is_correct(self, completion, answer):
+        gold = answer
+        assert gold != INVALID_ANS, "No ground truth answer found in the document."
+        return self._extract_answer(completion) == gold
+
+    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.
+        """
+        completion = results[0]
+        answer = doc["answer_number"]
+        return {"acc": self._is_correct(completion, answer)}
+
+    def aggregation(self):
+        """
+        :returns: {str: [float] -> float}
+            A dictionary where keys are the names of submetrics and values are
+            functions that aggregate a list of metrics
+        """
+        return {"acc": mean}
+
+    def higher_is_better(self):
+        """
+        :returns: {str: bool}
+            A dictionary where keys are the names of submetrics and values are
+            whether a higher value of the submetric is better
+        """
+        return {"acc": True}
+
+
+class MGSM_English(MGSM):
+    DATASET_NAME = "en"
+    QUESTION = "Question:"
+    ANSWER = "Step-by-Step Answer:"
+
+
+class MGSM_Spanish(MGSM):
+    DATASET_NAME = "es"
+    QUESTION = "Pregunta:"
+    ANSWER = "Respuesta paso a paso:"
+
+
+class MGSM_French(MGSM):
+    DATASET_NAME = "fr"
+    QUESTION = "Question :"
+    ANSWER = "R\u00e9ponse \u00e9tape par \u00e9tape :"
+
+
+class MGSM_German(MGSM):
+    DATASET_NAME = "de"
+    QUESTION = "Frage:"
+    ANSWER = "Schritt-f\u00fcr-Schritt-Antwort:"
+
+
+class MGSM_Russian(MGSM):
+    DATASET_NAME = "ru"
+    QUESTION = "\u0417\u0430\u0434\u0430\u0447\u0430:"
+    ANSWER = "\u041f\u043e\u0448\u0430\u0433\u043e\u0432\u043e\u0435\u0440\u0435\u0448\u0435\u043d\u0438\u0435:"
+
+
+class MGSM_Chinese(MGSM):
+    DATASET_NAME = "zh"
+    QUESTION = "\u95ee\u9898:"
+    ANSWER = "\u9010\u6b65\u89e3\u7b54:"
+
+
+class MGSM_Japanese(MGSM):
+    DATASET_NAME = "ja"
+    QUESTION = "\u554f\u984c:"
+    ANSWER = "\u30b9\u30c6\u30c3\u30d7\u3054\u3068\u306e\u7b54\u3048:"
+
+
+class MGSM_Thai(MGSM):
+    DATASET_NAME = "th"
+    QUESTION = "\u0e42\u0e08\u0e17\u0e22\u0e4c:"
+    ANSWER = "\u0e04\u0e33\u0e15\u0e2d\u0e1a\u0e17\u0e35\u0e25\u0e30\u0e02\u0e31\u0e49\u0e19\u0e15\u0e2d\u0e19:"
+
+
+class MGSM_Swahili(MGSM):
+    DATASET_NAME = "sw"
+    QUESTION = "Swali:"
+    ANSWER = "Jibu la Hatua kwa Hatua:"
+
+
+class MGSM_Bengali(MGSM):
+    DATASET_NAME = "bn"
+    QUESTION = "\u09aa\u09cd\u09b0\u09b6\u09cd\u09a8:"
+    ANSWER = "\u09a7\u09be\u09aa\u09c7 \u09a7\u09be\u09aa\u09c7 \u0989\u09a4\u09cd\u09a4\u09b0:"
+
+
+class MGSM_Telugu(MGSM):
+    DATASET_NAME = "te"
+    QUESTION = "\u0c2a\u0c4d\u0c30\u0c36\u0c4d\u0c28:"
+    ANSWER = "\u0c26\u0c36\u0c32\u0c35\u0c3e\u0c30\u0c40\u0c17\u0c3e \u0c38\u0c2e\u0c3e\u0c27\u0c3e\u0c28\u0c02:"
+
+
+LANGS = ["en", "es", "fr", "de", "ru", "zh", "ja", "th", "sw", "bn", "te"]
+
+LANG_CLASSES = [
+    MGSM_English,
+    MGSM_Spanish,
+    MGSM_French,
+    MGSM_German,
+    MGSM_Russian,
+    MGSM_Chinese,
+    MGSM_Japanese,
+    MGSM_Thai,
+    MGSM_Swahili,
+    MGSM_Bengali,
+    MGSM_Telugu,
+]
+
+
+def construct_tasks():
+    tasks = {}
+    for lang, lang_class in zip(LANGS, LANG_CLASSES):
+        tasks[f"mgsm_{lang}"] = lang_class
+    return tasks

lm_eval/tasks/pawsx.py

+"""
+PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification
+https://arxiv.org/abs/1908.11828
+
+The dataset consists of 23,659 human translated PAWS evaluation pairs and
+296,406 machine translated training pairs in 6 typologically distinct languages.
+
+Examples are adapted from  PAWS-Wiki
+
+Prompt format (same as in mGPT):
+
+"<s>" + sentence1 + ", right? " + mask + ", " + sentence2 + "</s>",
+
+where mask is the string that matches the label:
+
+Yes, No.
+
+Example:
+
+<s> The Tabaci River is a tributary of the River Leurda in Romania, right? No, The Leurda River is a tributary of the River Tabaci in Romania.</s>
+
+Language specific prompts are translated word-by-word with Google Translate
+and may differ from the ones used by mGPT and XGLM (they do not provide their prompts).
+
+Homepage: https://github.com/google-research-datasets/paws/tree/master/pawsx
+"""
+from lm_eval.base import Task, rf
+from lm_eval.metrics import mean
+
+_CITATION = """
+@inproceedings{yang-etal-2019-paws,
+    title = "{PAWS}-{X}: A Cross-lingual Adversarial Dataset for Paraphrase Identification",
+    author = "Yang, Yinfei  and
+      Zhang, Yuan  and
+      Tar, Chris  and
+      Baldridge, Jason",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
+    month = nov,
+    year = "2019",
+    address = "Hong Kong, China",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/D19-1382",
+    doi = "10.18653/v1/D19-1382",
+    pages = "3687--3692",
+}"""
+
+
+class PAWSXBase(Task):
+    VERSION = 0
+    DATASET_PATH = "paws-x"
+    DATASET_NAME = None  # 'en'
+
+    YES = None  # 'Yes'
+    NO = None  # 'No'
+    QUESTION_WORD = None  # 'right'
+
+    def has_training_docs(self):
+        return True
+
+    def has_validation_docs(self):
+        return True
+
+    def has_test_docs(self):
+        return True
+
+    def training_docs(self):
+        return self.dataset["train"]
+
+    def validation_docs(self):
+        return self.dataset["validation"]
+
+    def test_docs(self):
+        return self.dataset["test"]
+
+    def doc_to_text(self, doc):
+        # same as in mGPT paper
+        return (
+            doc["sentence1"]
+            + ", "
+            + self.QUESTION_WORD
+            + "? [MASK], "
+            + doc["sentence2"]
+        )
+
+    def doc_to_target(self, doc):
+        return " " + [self.YES, self.NO][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_yes = rf.loglikelihood_rolling(ctx.replace("[MASK]", self.YES))
+        ll_no = rf.loglikelihood_rolling(ctx.replace("[MASK]", self.NO))
+
+        return ll_yes, ll_no
+
+    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.
+        """
+        ll_yes, ll_no = results
+
+        pred = ll_yes > ll_no
+
+        true_label = doc["label"]
+
+        return {
+            "acc": pred == true_label,
+        }
+
+    def aggregation(self):
+        """
+        :returns: {str: [metric_score] -> float}
+            A dictionary where keys are the names of submetrics and values are
+            functions that aggregate a list of metric scores
+        """
+        return {
+            "acc": mean,
+        }
+
+    def higher_is_better(self):
+        return {"acc": True}
+
+
+class PAWSX_en(PAWSXBase):
+    DATASET_NAME = "en"
+    YES = "Yes"
+    NO = "No"
+    QUESTION_WORD = "right"
+
+
+class PAWSX_de(PAWSXBase):
+    DATASET_NAME = "de"
+    YES = "Ja"
+    NO = "Nein"
+    QUESTION_WORD = "richtig"
+
+
+class PAWSX_fr(PAWSXBase):
+    DATASET_NAME = "fr"
+    YES = "Oui"
+    NO = "No"
+    QUESTION_WORD = "right"
+
+
+class PAWSX_es(PAWSXBase):
+    DATASET_NAME = "es"
+    YES = "Sí"
+    NO = "No"
+    QUESTION_WORD = "verdad"
+
+
+class PAWSX_ja(PAWSXBase):
+    DATASET_NAME = "ja"
+    YES = "はい"
+    NO = "いいえ"
+    QUESTION_WORD = "ですね"
+
+
+class PAWSX_ko(PAWSXBase):
+    DATASET_NAME = "ko"
+    YES = "예"
+    NO = "아니요"
+    QUESTION_WORD = "맞죠"
+
+
+class PAWSX_zh(PAWSXBase):
+    DATASET_NAME = "zh"
+    YES = "是"
+    NO = "不是"
+    QUESTION_WORD = "对吧"
+
+
+LANGS = [
+    "en",
+    "de",
+    "es",
+    "fr",
+    "ja",
+    "ko",
+    "zh",
+]
+
+LANG_CLASSES = [
+    PAWSX_en,
+    PAWSX_de,
+    PAWSX_es,
+    PAWSX_fr,
+    PAWSX_ja,
+    PAWSX_ko,
+    PAWSX_zh,
+]
+
+
+def construct_tasks():
+    tasks = {}
+    for lang, lang_class in zip(LANGS, LANG_CLASSES):
+        tasks[f"pawsx_{lang}"] = lang_class
+    return tasks

lm_eval/tasks/qasper.py

@@ -214,7 +214,7 @@ class QASPER(Task):
         """
         # unanswerable = rf.loglikelihood(ctx, " " + "unanswerable")
         if doc["answer_type"] in ("free form answer"):
-            return [rf.greedy_until(ctx, {'until': ["\n"]})]
+            return [rf.greedy_until(ctx, {"until": ["\n"]})]
         elif doc["answer_type"] in ("bool"):
             ll_yes, _ = rf.loglikelihood(ctx, " yes")
             ll_no, _ = rf.loglikelihood(ctx, " no")

lm_eval/tasks/squad.py

@@ -107,7 +107,7 @@ class SQuAD2(Task):
             language description, as well as the few shot examples, and the question
             part of the document for `doc`.
         """
-        continuation = rf.greedy_until(ctx, {'until': ["\n"]})
+        continuation = rf.greedy_until(ctx, {"until": ["\n"]})
         is_unanswerable = rf.loglikelihood(ctx, " " + "unanswerable")
         return continuation, is_unanswerable
 

lm_eval/tasks/translation.py

@@ -184,7 +184,7 @@ class GeneralTranslationTask(Task):
             language description, as well as the few shot examples, and the question
             part of the document for `doc`.
         """
-        return rf.greedy_until(ctx, {'until': ["\n"]})
+        return rf.greedy_until(ctx, {"until": ["\n"]})
 
     def process_results(self, doc, results):
         # Add spaces between words for BLEU score calculation of target languages like Chinese