Add files using upload-large-folder tool

.editorconfig

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+# http://editorconfig.org
+
+root = true
+
+[*]
+indent_style = space
+indent_size = 4
+trim_trailing_whitespace = true
+insert_final_newline = true
+charset = utf-8
+end_of_line = lf
+
+[*.bat]
+indent_style = tab
+end_of_line = crlf
+
+[LICENSE]
+insert_final_newline = false
+
+[Makefile]
+indent_style = tab

CONTRIBUTING.md

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+# Contributing
+
+This is a community project and contributions are welcome, and they are greatly appreciated! Every little bit helps, and credit will always be given.
+
+If you are inspired to contribute please see the following entries:
+
+Megatron-DeeepSpeed:
+
+- [Megatron-DeepSpeed Issues](https://github.com/bigscience-workshop/Megatron-DeepSpeed/issues)
+- [Good First Issues](https://github.com/bigscience-workshop/Megatron-DeepSpeed/contribute)
+
+General BigScience:
+
+- [bigscience Issues](https://github.com/bigscience-workshop/bigscience/issues)
+- [Good First Issues](https://github.com/bigscience-workshop/bigscience/contribute)
+
+
+
+### Report Bugs
+
+Report bugs at
+<https://github.com/bigscience-workshop/bigscience/issues>.
+
+If you are reporting a bug, please include:
+
+-   Your operating system name and version.
+-   Any details about your local setup that might be helpful in
+    troubleshooting.
+-   Detailed steps to reproduce the bug.
+
+### Fix Bugs
+
+Look through the GitHub issues for bugs. Anything tagged with "bug" and
+"help wanted" is open to whoever wants to implement it.
+
+### Implement Features
+
+Look through the GitHub issues for features. Anything tagged with
+"enhancement" and "help wanted" is open to whoever wants to implement
+it.
+
+### Write Documentation
+
+Big Science could always use more documentation, whether as part of the
+official Big Science docs, in docstrings, or even on the web in blog
+posts, articles, and such.
+
+### Submit Feedback
+
+The best way to send feedback is to file an issue at
+<https://github.com/bigscience-workshop/bigscience/issues>.
+
+If you are proposing a feature:
+
+-   Explain in detail how it would work.
+-   Keep the scope as narrow as possible, to make it easier to
+    implement.
+-   Remember that this is a volunteer-driven project, and that
+    contributions are welcome :)
+
+Get Started!
+------------
+
+Ready to contribute? Here's how to set up bigscience for local
+development.
+
+1.  Fork the bigscience repo on GitHub.
+2.  Clone your fork locally:
+
+        $ git clone [email protected]:your_name_here/bigscience.git
+
+3.  Install your local copy into a virtualenv. Assuming you have
+    virtualenvwrapper installed, this is how you set up your fork for
+    local development:
+```
+        $ mkvirtualenv bigscience
+        $ cd bigscience/
+        $ python setup.py develop
+```
+4.  Create a branch for local development:
+```
+        $ git checkout -b name-of-your-bugfix-or-feature
+```
+    Now you can make your changes locally.
+
+5.  When you're done making changes, check that your changes pass flake8
+    and the tests, including testing other Python versions with tox:
+```
+        $ flake8 bigscience tests
+        $ python setup.py test or pytest
+        $ tox
+```
+    To get flake8 and tox, just pip install them into your virtualenv.
+
+6.  Commit your changes and push your branch to GitHub:
+```
+        $ git add .
+        $ git commit -m "Your detailed description of your changes."
+        $ git push origin name-of-your-bugfix-or-feature
+```
+7.  Submit a pull request through the GitHub website.
+
+Pull Request Guidelines
+-----------------------
+
+Before you submit a pull request, check that it meets these guidelines:
+
+1.  The pull request should include tests.
+2.  If the pull request adds functionality, the docs should be updated.
+    Put your new functionality into a function with a docstring, and add
+    the feature to the list in README.rst.

README.md

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+# bigscience
+
+[Research workshop on large language models - The Summer of Language Models 21](https://bigscience.huggingface.co/)
+
+At the moment we have 2 code repos:
+
+1. https://github.com/bigscience-workshop/Megatron-DeepSpeed - this is our flagship code base
+2. https://github.com/bigscience-workshop/bigscience - (this repo) for everything else - docs, experiments, etc.
+
+Currently, the most active segments of this repo are:
+
+- [JZ](./jz/) - Lots of information about our work environment which helps evaluate, plan and get things done
+- [Experiments](./experiments) - many experiments are being done. Documentation, result tables, scripts and logs are all there
+- [Datasets info](./data/)
+- [Train](./train) - all the information about the current trainings (see below for the most important ones)
+
+We have READMEs for specific aspects, such as:
+- [hub integration](./tools/README.md)
+
+
+## Trainings
+
+While we keep detailed chronicles of experiments and findings for some of the main trainings, here is a doc that contains a summary of the most important findings: [Lessons learned](train/lessons-learned.md)
+
+
+### Train 1 - 13B - unmodified Megatron gpt2 - baseline
+
+* [the full spec and discussions](./train/tr1-13B-base)
+* [the training script](./train/tr1-13B-base/tr1-13B-round1.slurm)
+* checkpoints and logs:
+   - [tensorboard](https://huggingface.co/bigscience/tr1-13B-tensorboard/tensorboard)
+   - [logs](https://huggingface.co/bigscience/tr1-13B-logs/)
+* [chronicles](./train/tr1-13B-base/chronicles.md)
+
+You can watch the training logs live by running this `tail -f` like script over remote log file that gets synced to the hub once an hour:
+```
+perl -e '$u=shift; $b=0; while(1){($e)=qx[curl -sI $u]=~/content-length: (\d+)/; \
+print qx[curl -sr $b-$e -L $u] if $e>$b; $b=$e; sleep 300}' \
+https://huggingface.co/bigscience/tr1-13B-logs/resolve/main/main_log.txt
+
+```
+
+### Train 3
+
+Architecture and scaling baseline runs: no fancy tricks, just GPT2. Here are links to the respective tensorboards:
+
+| Size                	| 1B3 	| 760M 	| 350M 	| 125M 	|
+|---------------------	|-----	|------	|------	|------	|
+| C4 + low warmup     	| [a](https://huggingface.co/bigscience/tr3-1B3-modeling-baseline-tensorboard)   	| [b](https://huggingface.co/bigscience/tr3b-760M-modeling-baseline-tensorboard)    	| [c](https://huggingface.co/bigscience/tr3c-350M-modeling-baseline-tensorboard)    	|      	|
+| OSCAR + low warmup  	| [f](https://huggingface.co/bigscience/tr3f-1B3-diagnostic2-low-warmup-oscar-tensorboard)   	|      	|      	|      	|
+| C4 + high warmup    	| [e](https://huggingface.co/bigscience/tr3e-1B3-diagnostic1-warmup-c4-tensorboard)   	|      	|      	|      	|
+| OSCAR + high warmup 	| **[d (current baseline)](https://huggingface.co/bigscience/tr3d-1B3-more-warmup-tensorboard)**   	| [g](https://huggingface.co/bigscience/tr3g-760M-v2-tensorboard)    	| [h](https://huggingface.co/bigscience/tr3h-350M-v2-tensorboard)    	| [i](https://huggingface.co/bigscience/tr3i-125M-v2-tensorboard)    	|
+| Pile + high warmup  	| [m](https://huggingface.co/bigscience/tr3m-1B3-pile-tensorboard)   	| [j](https://huggingface.co/bigscience/tr3j-760M-pile-tensorboard)    	| [k](https://huggingface.co/bigscience/tr3k-350M-pile-tensorboard)    	| [l](https://huggingface.co/bigscience/tr3l-125M-pile-tensorboard)    	|
+
+
+### Train 8
+
+104B - unmodified Megatron gpt2 - with extra-wide hidden size to learn how to deal with training instabilities
+
+* [the full spec and discussions](./train/tr8-104B-wide)
+* [the training script](./train/tr8-104B-wide/tr8-104B.slurm)
+* checkpoints and logs:
+   - [tensorboard](https://huggingface.co/bigscience/tr8-104B-logs/tensorboard)
+   - [logs](https://huggingface.co/bigscience/tr8-104B-logs/tree/main/logs)
+* [chronicles](./train/tr8-104B-wide/chronicles.md)
+
+You can watch the training logs live by running this `tail -f` like script over remote log file that gets synced to the hub once an hour:
+```
+perl -e '$u=shift; $b=0; while(1){($e)=qx[curl -sI $u]=~/content-length: (\d+)/; \
+print qx[curl -sr $b-$e -L $u] if $e>$b; $b=$e; sleep 300}' \
+https://cdn-lfs.huggingface.co/bigscience/tr8-104B-logs/b2cc478d5ae7c9ec937ea2db1d2fe09de593fa2ec38c171d6cc5dca094cd79f9
+```
+
+### Train 11
+
+**This is the current main training**
+
+tr11-176B-ml
+
+* [the full spec and discussions](./train/tr11-176B-ml/)
+* [the training script](./train/tr11-176B-ml/tr11-176B-ml.slurm)
+* checkpoints and logs:
+   - [tensorboard](https://huggingface.co/bigscience/tr11-176B-ml-logs/tensorboard)
+   - [logs](https://huggingface.co/bigscience/tr11-176B-ml-logs/tree/main/logs/main)
+* [chronicles-prequel](./train/tr11-176B-ml/chronicles-prequel.md)
+* [chronicles](./train/tr11-176B-ml/chronicles.md)
+
+You can watch the training logs live by running this `tail -f` like script over remote log file that gets synced to the hub once an hour:
+```
+perl -e '$u=shift; $b=0; while(1){($e)=qx[curl -LsI $u]=~/2 200.*?content-length: (\d+)/s; \
+print qx[curl -Lsr $b-$e $u] if $e>$b; $b=$e; sleep 300}' \
+https://huggingface.co/bigscience/tr11-176B-ml-logs/resolve/main/logs/main/main_log.txt
+```

bigscience/__init__.py

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+"""Top-level package for Big Science."""
+
+__author__ = """Stas Bekman"""
+__email__ = '[email protected]'
+__version__ = '0.1.0'

bigscience/bigscience.py

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+"""Main module."""

evaluation/README.md

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+# Evaluation
+
+This folder contains scripts and results for intermediate evaluation, mostly based on zero-shot prompting performance. Most are performed with Eleuther AI's [LM eval harness](https://github.com/EleutherAI/lm-evaluation-harness).
+
+Evaluated models:
+- BLOOM (tr11 / The `bigscience/bloom` model in 176B / 6B3 / 2B5 / 1B3 / 750M / 350M variants)
+- [13B](https://github.com/bigscience-workshop/bigscience/blob/master/evaluation/Tr1-13B-harness-eval.json)

evaluation/generation/generate.py

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+import argparse
+import datetime
+
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--checkpoint", type=str, help="Checkpoint path", required=True)
+    parser.add_argument("--max-memory-per-gpu", type=str, help="Defines maximum memory allocated to gpu", required=True)
+    parser.add_argument("--global-step", type=str, default=None)
+    parser.add_argument("--generate-max-length", type=int, default=50, help="max generation length")
+    parser.add_argument("--greedy", action="store_true")
+    parser.add_argument("--top-k", type=int, default=0)
+    parser.add_argument("--top-p", type=float, default=0.)
+    parser.add_argument("--offload_folder", type=str, help="offload folder for accelerate", default="./offload")
+
+    return parser.parse_args()
+
+def get_gpus_max_memory(max_memory):
+    max_memory = {i: max_memory for i in range(torch.cuda.device_count())}
+    return max_memory
+
+def generate_from_text(model, text, tokenizer, max_length=200, greedy=False, top_k=0, top_p=0.):
+    input_ids = tokenizer.encode(text, return_tensors='pt').to("cuda:0")
+    max_length = input_ids.size(-1) + max_length
+    
+    greedy_output = model.generate(
+        input_ids.to('cuda:0'),
+        max_length=max_length,
+        do_sample=not greedy,
+        top_k=None if greedy else top_k,
+        top_p=None if greedy else top_p
+    )
+    return tokenizer.decode(greedy_output[0], skip_special_tokens=True)
+
+def main():
+    args = get_args()
+    print("Loading model")
+
+    tokenizer = AutoTokenizer.from_pretrained(args.checkpoint, padding_side="left")
+
+    print("Loaded tokenizer!")
+    start = datetime.datetime.now()
+    model = AutoModelForCausalLM.from_pretrained(
+        args.checkpoint,
+        device_map="auto",
+        max_memory=get_gpus_max_memory(args.max_memory_per_gpu),
+        torch_dtype=torch.bfloat16,
+        revision="gs{}".format(args.global_step) if args.global_step else None,
+        offload_folder=args.offload_folder,
+    )
+    print(f"Loaded model in {datetime.datetime.now() - start}")
+
+    texts = []
+    while True:
+        try:
+            dummy = input('''Enter the paragraph (Enter for to validate new input line and Ctrl-c to start generating the prompt):''')
+            texts.append(dummy)
+        except KeyboardInterrupt:
+            text = "\n".join(texts)
+            output = generate_from_text(model, text, tokenizer, max_length=args.generate_max_length, greedy=args.greedy, top_k=args.top_k, top_p=args.top_p)
+            print(output)
+            texts = []
+
+if __name__ == "__main__":
+    main()

evaluation/results/tr1/Tr1-13B-harness-eval.json

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+{
+  "results": {
+    "lambada": {
+      "ppl": 5.020137688328123,
+      "ppl_stderr": 0.11575351197990837,
+      "acc": 0.634193673588201,
+      "acc_stderr": 0.006710403442216892
+    },
+    "winogrande": {
+      "acc": 0.6471981057616417,
+      "acc_stderr": 0.013429728101788954
+    },
+    "hellaswag": {
+      "acc": 0.5416251742680741,
+      "acc_stderr": 0.004972460206842306,
+      "acc_norm": 0.7162915753833897,
+      "acc_norm_stderr": 0.004498757194493409
+    },
+    "piqa": {
+      "acc": 0.7769314472252449,
+      "acc_stderr": 0.009713057213018522,
+      "acc_norm": 0.7878128400435256,
+      "acc_norm_stderr": 0.009539299828174046
+    },
+    "cola": {
+      "mcc": 0.05586916675965605,
+      "mcc_stderr": 0.034250689348891604
+    },
+    "mnli": {
+      "acc": 0.3959246051961284,
+      "acc_stderr": 0.004936609703575665
+    },
+    "mnli_mismatched": {
+      "acc": 0.3984947111472742,
+      "acc_stderr": 0.004937784794740595
+    },
+    "mrpc": {
+      "acc": 0.6764705882352942,
+      "acc_stderr": 0.023189113109403536,
+      "f1": 0.8058823529411765,
+      "f1_stderr": 0.016598529068410604
+    },
+    "rte": {
+      "acc": 0.5234657039711191,
+      "acc_stderr": 0.03006330041190266
+    },
+    "qnli": {
+      "acc": 0.5171151382024529,
+      "acc_stderr": 0.006761445834294947
+    },
+    "qqp": {
+      "acc": 0.36772198862231015,
+      "acc_stderr": 0.0023981002797098354,
+      "f1": 0.532523819102829,
+      "f1_stderr": 0.0025759259415034795
+    },
+    "sst": {
+      "acc": 0.5137614678899083,
+      "acc_stderr": 0.01693543564494107
+    },
+    "wnli": {
+      "acc": 0.18309859154929578,
+      "acc_stderr": 0.046225147349214284
+    },
+    "boolq": {
+      "acc": 0.5868501529051988,
+      "acc_stderr": 0.008612117547803569
+    },
+    "copa": {
+      "acc": 0.88,
+      "acc_stderr": 0.03265986323710906
+    },
+    "multirc": {
+      "acc": 0.017838405036726127,
+      "acc_stderr": 0.00428993794671089
+    },
+    "record": {
+      "f1": 0.885354285714286,
+      "f1_stderr": 0.00314773987203575,
+      "em": 0.8783,
+      "em_stderr": 0.003269553486028481
+    },
+    "wic": {
+      "acc": 0.49843260188087773,
+      "acc_stderr": 0.019810623954060382
+    },
+    "wsc": {
+      "acc": 0.5,
+      "acc_stderr": 0.04926646390821466
+    },
+    "prost": {
+      "acc": 0.28047608881298036,
+      "acc_stderr": 0.003282038627279345,
+      "acc_norm": 0.2830380017079419,
+      "acc_norm_stderr": 0.003291119066155946
+    },
+    "mc_taco": {
+      "em": 0.12612612612612611,
+      "f1": 0.4965489467730623
+    },
+    "pubmedqa": {
+      "acc": 0.615,
+      "acc_stderr": 0.015395194445410805
+    },
+    "sciq": {
+      "acc": 0.895,
+      "acc_stderr": 0.009698921026024957,
+      "acc_norm": 0.815,
+      "acc_norm_stderr": 0.012285191326386693
+    },
+    "triviaqa": {
+      "acc": 0.13294440024750287,
+      "acc_stderr": 0.0031921904944669202
+    },
+    "arc_easy": {
+      "acc": 0.6813973063973064,
+      "acc_stderr": 0.009560775507673364,
+      "acc_norm": 0.6001683501683501,
+      "acc_norm_stderr": 0.010051788039412911
+    },
+    "arc_challenge": {
+      "acc": 0.3216723549488055,
+      "acc_stderr": 0.013650488084494164,
+      "acc_norm": 0.34215017064846415,
+      "acc_norm_stderr": 0.013864152159177275
+    },
+    "logiqa": {
+      "acc": 0.23195084485407066,
+      "acc_stderr": 0.0165552524979259,
+      "acc_norm": 0.2749615975422427,
+      "acc_norm_stderr": 0.01751297178222522
+    },
+    "openbookqa": {
+      "acc": 0.294,
+      "acc_stderr": 0.020395095484936603,
+      "acc_norm": 0.412,
+      "acc_norm_stderr": 0.022033677993740865
+    },
+    "race": {
+      "acc": 0.3741626794258373,
+      "acc_stderr": 0.014976513181619648
+    },
+    "headqa": {
+      "acc": 0.22283005105762219,
+      "acc_stderr": 0.007948594863726302,
+      "acc_norm": 0.26258205689277897,
+      "acc_norm_stderr": 0.00840494460823324
+    },
+    "mathqa": {
+      "acc": 0.2375209380234506,
+      "acc_stderr": 0.0077905030438074,
+      "acc_norm": 0.23450586264656617,
+      "acc_norm_stderr": 0.007756188894243557
+    },
+    "webqs": {
+      "acc": 0.0265748031496063,
+      "acc_stderr": 0.003568875174120304
+    },
+    "wikitext": {
+      "word_perplexity": 12.921754196505068,
+      "byte_perplexity": 1.6136995247803747,
+      "bits_per_byte": 0.4785293844744369
+    }
+  }
+}

evaluation/results/tr11/bloom1b3/bslmevalfiles/concat.py

@@ -0,0 +1,103 @@
+import argparse
+import json
+import re
+from pathlib import Path
+from re import Pattern
+from typing import List, Dict
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--results-dir", required=True, type=Path, help="Path to the list of results")
+    parser.add_argument("--concatenate-output-file", required=True, type=Path, help="Path to store the final output file")
+    return parser.parse_args()
+
+MODEL = "tr11b-1b3-ml-bsevalharness-results_lm-eval_global_step340500"
+# MODEL = "global_step95000"
+RESULTS_REGEX = re.compile(rf"(eai|bs)_results_lm-eval_{MODEL}_(\d{4}-\d{2}-\d{2}-\d{2}-\d{2}-\d{2})_backup\.json")
+RESULTS_REGEX = re.compile(rf"{MODEL}_*.json")
+#tr11b-1b3-ml-bsevalharness-results_lm-eval_global_step340500_2022-07-14-10-03-25.json
+def get_all_files_that_match_results_in_folder(root_folder: Path) -> List[Path]:
+    json_files = []
+    for folder in root_folder.iterdir():
+        if folder.is_dir():
+            json_files += get_all_files_that_match_results_in_folder(folder)
+        else:
+            # it's actually a file
+            file = folder
+
+            #match = RESULTS_REGEX.match(file.name)
+
+            if not str(file.name).endswith("json"):
+                continue
+            else:
+                json_files.append(file)
+    return json_files
+
+def sort_dict(dictionary: Dict) -> Dict:
+    results = {}
+
+    for key, value in sorted(dictionary.items()):
+        new_value = value
+
+        if isinstance(value, dict):
+            new_value = sort_dict(new_value)
+        elif isinstance(value, list):
+            new_value = sorted(value)
+
+        results[key] = new_value
+
+    return results
+
+def main():
+    args = get_args()
+
+    # Get all json files
+    json_files = get_all_files_that_match_results_in_folder(args.results_dir)
+    print("GOT", json_files)
+    # Merge all json files
+    final_result = {
+        "results": {},
+        "versions": {}
+    }
+    for file in json_files:
+        with open(file, "r") as fi:
+            task_result = json.load(fi)
+
+        #match = RESULTS_REGEX.match(file.name)
+        #assert match is not None
+        prefix = "bs" if "bs" in file.name else "eai"#match.group(1)
+        datetime_string = file.name[file.name.index("global_step340500_") + len("global_step340500_"):].replace(".json", "")#match.group(2)
+
+        if prefix == "eai":
+            results_key = "results"
+        elif prefix == "bs":
+            results_key = "table_results"
+        else:
+            raise ValueError(f"Unsupported key: {prefix}")
+
+        for key, value in task_result[results_key].items():
+            if key not in final_result["results"]:
+                final_result["results"][key] = {
+                    datetime_string: value
+                }
+            #else:
+            #    assert datetime_string not in final_result["results"][key]
+            #    final_result["results"][key][datetime_string] = value
+
+        for key, value in task_result["versions"].items():
+            final_result["versions"][key] = value
+
+    # We sort dict, better for serialization
+    print(final_result)
+    final_result = sort_dict(final_result)
+
+    # Save result
+    with open(args.concatenate_output_file, "w") as fo:
+        json.dump(final_result, fo, indent=2)
+
+    pass
+
+if __name__ == "__main__":
+    main()
+

evaluation/results/tr11/bloom1b3/bslmevalfiles/tr11b-1b3-ml-bsevalharness-results_lm-eval_global_step340500_2022-07-13-19-23-37.json

@@ -0,0 +1,701 @@
+{
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+            "comment": "",
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+            "prompt_id": "c0724198-97e7-44a1-89d8-c51e97ce0b04",
+            "prompt_jinja": "Question 1: {{question1}}\nQuestion 2: {{question2}}\n\nDo these two questions convey the same meaning? Yes or no? ||| {{answer_choices[label]}}",
+            "prompt_original_task": true,
+            "comment": "",
+            "acc_stderr": 0.0023986729832071916
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+            "task_name": "qqp",
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+            "fixed_answer_choice_list": [
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+                "Yes"
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+            "dataset_name": "qqp",
+            "subset": null,
+            "prompt_id": "c0724198-97e7-44a1-89d8-c51e97ce0b04",
+            "prompt_jinja": "Question 1: {{question1}}\nQuestion 2: {{question2}}\n\nDo these two questions convey the same meaning? Yes or no? ||| {{answer_choices[label]}}",
+            "prompt_original_task": true,
+            "comment": "",
+            "acc_norm_stderr": 0.002398706610614492
+        },
+        {
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+            "dataset_path": "glue",
+            "dataset_name": "qqp",
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+            "prompt_id": "8e711799-a57c-4941-833b-466bedfb80ad",
+            "prompt_jinja": "I'm an administrator on the website Quora. There are two posts, one that asks \"{{question1}}\" and another that asks \"{{question2}}\". I can merge questions if they are asking the same thing. Can I merge these two questions? ||| {{ answer_choices[label] }}",
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+            "prompt_id": "8e711799-a57c-4941-833b-466bedfb80ad",
+            "prompt_jinja": "I'm an administrator on the website Quora. There are two posts, one that asks \"{{question1}}\" and another that asks \"{{question2}}\". I can merge questions if they are asking the same thing. Can I merge these two questions? ||| {{ answer_choices[label] }}",
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+            "prompt_id": "a45ad5cd-a3ba-4ab2-a728-a9ea0f27102b",
+            "prompt_jinja": "Are the questions \"{{question1}}\" and \"{{question2}}\" asking the same thing? ||| {{ answer_choices[label] }}",
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+                "yes"
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+            "dataset_name": "qqp",
+            "subset": null,
+            "prompt_id": "a45ad5cd-a3ba-4ab2-a728-a9ea0f27102b",
+            "prompt_jinja": "Are the questions \"{{question1}}\" and \"{{question2}}\" asking the same thing? ||| {{ answer_choices[label] }}",
+            "prompt_original_task": true,
+            "comment": "",
+            "acc_norm_stderr": 0.002398706610614492
+        },
+        {
+            "task_name": "rte",
+            "prompt_name": "does the claim\u2026 follow the fact\u2026",
+            "acc": 0.4729241877256318,
+            "fixed_answer_choice_list": [
+                "yes",
+                "no"
+            ],
+            "dataset_path": "glue",
+            "dataset_name": "rte",
+            "subset": null,
+            "prompt_id": "4ee6ff27-de63-4e7b-a9d4-82a17eba407a",
+            "prompt_jinja": "Does the claim \"{{sentence2}}\" follow from the fact that \"{{sentence1}}\"? Please answer either {{\"yes\"}} or {{\"no\"}}.\n|||\n{{answer_choices[label]}}",
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+            "acc_stderr": 0.030052303463143706
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+            "prompt_id": "4ee6ff27-de63-4e7b-a9d4-82a17eba407a",
+            "prompt_jinja": "Does the claim \"{{sentence2}}\" follow from the fact that \"{{sentence1}}\"? Please answer either {{\"yes\"}} or {{\"no\"}}.\n|||\n{{answer_choices[label]}}",
+            "prompt_original_task": true,
+            "comment": "",
+            "acc_norm_stderr": 0.0300523034631437
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+            "prompt_name": "entailment explained",
+            "acc": 0.49458483754512633,
+            "fixed_answer_choice_list": [
+                "entailment",
+                "not entailment"
+            ],
+            "dataset_path": "glue",
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+            "subset": null,
+            "prompt_id": "9e2b4267-ec23-44c8-b82a-107e2c890fec",
+            "prompt_jinja": "We say that one sentence \"{{\"entails\"}}\" another sentence when the first sentence implies the second sentence. Consider the following two sentences:\n{{sentence1}}\n{{sentence2}}\nIs the relationship from the first to the second sentence \"{{\"entailment\"}}\" or \"{{\"not entailment\"}}\"?\n|||\n{{answer_choices[label]}}",
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+            "subset": null,
+            "prompt_id": "9e2b4267-ec23-44c8-b82a-107e2c890fec",
+            "prompt_jinja": "We say that one sentence \"{{\"entails\"}}\" another sentence when the first sentence implies the second sentence. Consider the following two sentences:\n{{sentence1}}\n{{sentence2}}\nIs the relationship from the first to the second sentence \"{{\"entailment\"}}\" or \"{{\"not entailment\"}}\"?\n|||\n{{answer_choices[label]}}",
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+        },
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+            "prompt_name": "imply",
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+            "prompt_id": "c8dfc879-40f2-412d-be1e-4cd70107f6e6",
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+            "prompt_id": "c8dfc879-40f2-412d-be1e-4cd70107f6e6",
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+            "acc_norm_stderr": 0.0300523034631437
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+            "prompt_name": "imply separated",
+            "acc": 0.45126353790613716,
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+                "no"
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+            "dataset_path": "glue",
+            "dataset_name": "rte",
+            "subset": null,
+            "prompt_id": "f56ffced-9b16-431a-8a17-501e63cddf73",
+            "prompt_jinja": "{{sentence1}}\nDoes this imply\n{{sentence2}}\nPlease answer {{\"A) yes or B) no.\"}}\n|||\n{{answer_choices[label]}}",
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+            "acc_stderr": 0.029953149241808943
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+            "prompt_name": "imply separated",
+            "acc_norm": 0.5270758122743683,
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+            "dataset_path": "glue",
+            "dataset_name": "rte",
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+            "prompt_id": "f56ffced-9b16-431a-8a17-501e63cddf73",
+            "prompt_jinja": "{{sentence1}}\nDoes this imply\n{{sentence2}}\nPlease answer {{\"A) yes or B) no.\"}}\n|||\n{{answer_choices[label]}}",
+            "prompt_original_task": true,
+            "comment": "",
+            "acc_norm_stderr": 0.0300523034631437
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+            "prompt_name": "mean",
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+            "dataset_path": "glue",
+            "dataset_name": "rte",
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+            "prompt_id": "03a7ae07-5ddd-46c4-92f3-2152223d44ec",
+            "prompt_jinja": "{{sentence1}}\nDoes this mean that \"{{sentence2}}\" is true? {{\"A) yes or B) no.\"}}\n|||\n{{answer_choices[label]}}",
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+            "comment": "",
+            "acc_norm_stderr": 0.0300523034631437
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+            "prompt_name": "following positive negative",
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+            "prompt_jinja": "Does the following sentence have a {{\"positive\"}} or {{\"negative\"}} sentiment?\n{{sentence}}\n|||\n{{ answer_choices[label] }}",
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+            "task_name": "sst",
+            "prompt_name": "happy or mad",
+            "acc": 0.5091743119266054,
+            "fixed_answer_choice_list": [
+                "bad",
+                "good"
+            ],
+            "dataset_path": "glue",
+            "dataset_name": "sst2",
+            "subset": null,
+            "prompt_id": "6dd74cd5-e074-4612-9e96-c17ca88c3bc4",
+            "prompt_jinja": "Someone sent me an email with the sentence \"{{sentence}}\". Do you think they are feeling {{\"good\"}} or {{\"bad\"}}? ||| {{ answer_choices[label] }}",
+            "prompt_original_task": true,
+            "comment": "",
+            "acc_stderr": 0.01693900152535154
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+        {
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+            "prompt_name": "happy or mad",
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+            "subset": null,
+            "prompt_id": "6dd74cd5-e074-4612-9e96-c17ca88c3bc4",
+            "prompt_jinja": "Someone sent me an email with the sentence \"{{sentence}}\". Do you think they are feeling {{\"good\"}} or {{\"bad\"}}? ||| {{ answer_choices[label] }}",
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+            "comment": "",
+            "acc_norm_stderr": 0.01693900152535154
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+            "prompt_name": "review",
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+            "prompt_id": "228fcae7-7f4c-4e3c-9ac4-e49b26bc103d",
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+            "comment": "",
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+            "prompt_name": "said",
+            "acc": 0.4908256880733945,
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+            "prompt_id": "5aa0cea9-0f8d-454d-b25b-b0d4cda273b8",
+            "prompt_jinja": "Someone just said to me \"{{sentence}}\".\n\nDo you think they are {{\"sad\"}} or {{\"happy\"}}? ||| {{ answer_choices[label] }}",
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+            "comment": "",
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+        {
+            "task_name": "sst",
+            "prompt_name": "said",
+            "acc_norm": 0.5091743119266054,
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evaluation/results/tr11/bloom1b3/bslmevalfiles/tr11b-1b3-ml-bsevalharness-results_lm-eval_global_step340500_2022-07-14-10-03-25.json

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evaluation/results/tr11/bloom1b3/bslmevalfiles/tr11b-1b3-ml-bsevalharness-results_lm-eval_global_step340500_2022-07-14-12-00-55.json

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+            "word_perplexity": 8232620282886.167,
+            "byte_perplexity": 5.086518347981296,
+            "bits_per_byte": 2.3466784891528936
+        },
+        "gsarti/flores_101_orm+null": {
+            "task_name": "gsarti/flores_101_orm",
+            "prompt_name": "null",
+            "word_perplexity": 1286222337.8393624,
+            "byte_perplexity": 13.414303089263644,
+            "bits_per_byte": 3.7457001993717243
+        },
+        "gsarti/flores_101_pus+null": {
+            "task_name": "gsarti/flores_101_pus",
+            "prompt_name": "null",
+            "word_perplexity": 200303.57214724104,
+            "byte_perplexity": 4.650458574106675,
+            "bits_per_byte": 2.2173729850313615
+        },
+        "gsarti/flores_101_fas+null": {
+            "task_name": "gsarti/flores_101_fas",
+            "prompt_name": "null",
+            "word_perplexity": 59965.98383842629,
+            "byte_perplexity": 3.1572599808371367,
+            "bits_per_byte": 1.6586730625582675
+        },
+        "gsarti/flores_101_pol+null": {
+            "task_name": "gsarti/flores_101_pol",
+            "prompt_name": "null",
+            "word_perplexity": 239703.75452947227,
+            "byte_perplexity": 5.165261846492578,
+            "bits_per_byte": 2.3688414865658434
+        },
+        "gsarti/flores_101_por+null": {
+            "task_name": "gsarti/flores_101_por",
+            "prompt_name": "null",
+            "word_perplexity": 78.66129921108659,
+            "byte_perplexity": 2.012150908931838,
+            "bits_per_byte": 1.0087385096181816
+        },
+        "gsarti/flores_101_pan+null": {
+            "task_name": "gsarti/flores_101_pan",
+            "prompt_name": "null",
+            "word_perplexity": 2003582065.835696,
+            "byte_perplexity": 5.012603107956229,
+            "bits_per_byte": 2.3255600077385723
+        },
+        "gsarti/flores_101_ron+null": {
+            "task_name": "gsarti/flores_101_ron",
+            "prompt_name": "null",
+            "word_perplexity": 80490.92705368399,
+            "byte_perplexity": 5.603607947317877,
+            "bits_per_byte": 2.486356022105963
+        },
+        "gsarti/flores_101_rus+null": {
+            "task_name": "gsarti/flores_101_rus",
+            "prompt_name": "null",
+            "word_perplexity": 22038.65288574451,
+            "byte_perplexity": 2.1372096174466697,
+            "bits_per_byte": 1.095728414417906
+        },
+        "gsarti/flores_101_srp+null": {
+            "task_name": "gsarti/flores_101_srp",
+            "prompt_name": "null",
+            "word_perplexity": 359037.4163692842,
+            "byte_perplexity": 3.050738229673983,
+            "bits_per_byte": 1.6091583939601046
+        },
+        "gsarti/flores_101_sna+null": {
+            "task_name": "gsarti/flores_101_sna",
+            "prompt_name": "null",
+            "word_perplexity": 151658287.08006003,
+            "byte_perplexity": 9.361234419948593,
+            "bits_per_byte": 3.226698783453375
+        },
+        "gsarti/flores_101_snd+null": {
+            "task_name": "gsarti/flores_101_snd",
+            "prompt_name": "null",
+            "word_perplexity": 2195879.0537875695,
+            "byte_perplexity": 5.678399375652783,
+            "bits_per_byte": 2.505484320885354
+        },
+        "gsarti/flores_101_slk+null": {
+            "task_name": "gsarti/flores_101_slk",
+            "prompt_name": "null",
+            "word_perplexity": 1873211.2703176092,
+            "byte_perplexity": 7.294354718439043,
+            "bits_per_byte": 2.8667803584469502
+        },
+        "gsarti/flores_101_slv+null": {
+            "task_name": "gsarti/flores_101_slv",
+            "prompt_name": "null",
+            "word_perplexity": 609965.8362492598,
+            "byte_perplexity": 7.438107250941839,
+            "bits_per_byte": 2.894935550489075
+        },
+        "gsarti/flores_101_som+null": {
+            "task_name": "gsarti/flores_101_som",
+            "prompt_name": "null",
+            "word_perplexity": 12921970.127169678,
+            "byte_perplexity": 12.622705630414286,
+            "bits_per_byte": 3.6579492747174616
+        },
+        "gsarti/flores_101_ckb+null": {
+            "task_name": "gsarti/flores_101_ckb",
+            "prompt_name": "null",
+            "word_perplexity": 11104497.438038943,
+            "byte_perplexity": 3.842852526862475,
+            "bits_per_byte": 1.9421776126623524
+        },
+        "gsarti/flores_101_spa+null": {
+            "task_name": "gsarti/flores_101_spa",
+            "prompt_name": "null",
+            "word_perplexity": 55.14408503293887,
+            "byte_perplexity": 1.9240269109386998,
+            "bits_per_byte": 0.9441289779054047
+        },
+        "gsarti/flores_101_swh+null": {
+            "task_name": "gsarti/flores_101_swh",
+            "prompt_name": "null",
+            "word_perplexity": 6985.646204087442,
+            "byte_perplexity": 3.923430589092355,
+            "bits_per_byte": 1.9721156771582438
+        },
+        "gsarti/flores_101_swe+null": {
+            "task_name": "gsarti/flores_101_swe",
+            "prompt_name": "null",
+            "word_perplexity": 104567.9891705103,
+            "byte_perplexity": 5.634635291846611,
+            "bits_per_byte": 2.4943222333483153
+        },
+        "gsarti/flores_101_tgk+null": {
+            "task_name": "gsarti/flores_101_tgk",
+            "prompt_name": "null",
+            "word_perplexity": 10003619.893239152,
+            "byte_perplexity": 3.836804862794101,
+            "bits_per_byte": 1.9399053923480125
+        },
+        "gsarti/flores_101_tam+null": {
+            "task_name": "gsarti/flores_101_tam",
+            "prompt_name": "null",
+            "word_perplexity": 4220234444737767.0,
+            "byte_perplexity": 4.286894531607389,
+            "bits_per_byte": 2.0999329236632325
+        },
+        "gsarti/flores_101_tel+null": {
+            "task_name": "gsarti/flores_101_tel",
+            "prompt_name": "null",
+            "word_perplexity": 7315913985648022.0,
+            "byte_perplexity": 5.852344181819556,
+            "bits_per_byte": 2.549014618212334
+        },
+        "gsarti/flores_101_tha+null": {
+            "task_name": "gsarti/flores_101_tha",
+            "prompt_name": "null",
+            "word_perplexity": 6.85384626099906e+32,
+            "byte_perplexity": 2.458737675753546,
+            "bits_per_byte": 1.2979178211163922
+        },
+        "gsarti/flores_101_tur+null": {
+            "task_name": "gsarti/flores_101_tur",
+            "prompt_name": "null",
+            "word_perplexity": 1230000.8194755162,
+            "byte_perplexity": 5.323529328304652,
+            "bits_per_byte": 2.4123830232149
+        },
+        "gsarti/flores_101_ukr+null": {
+            "task_name": "gsarti/flores_101_ukr",
+            "prompt_name": "null",
+            "word_perplexity": 780615.9486315987,
+            "byte_perplexity": 2.8843863497020608,
+            "bits_per_byte": 1.5282644195953918
+        },
+        "gsarti/flores_101_umb+null": {
+            "task_name": "gsarti/flores_101_umb",
+            "prompt_name": "null",
+            "word_perplexity": 346118506.64866126,
+            "byte_perplexity": 13.088423907901921,
+            "bits_per_byte": 3.710219475046473
+        },
+        "gsarti/flores_101_urd+null": {
+            "task_name": "gsarti/flores_101_urd",
+            "prompt_name": "null",
+            "word_perplexity": 335.1943886252716,
+            "byte_perplexity": 2.010562039704537,
+            "bits_per_byte": 1.0075988539165108
+        },
+        "gsarti/flores_101_uzb+null": {
+            "task_name": "gsarti/flores_101_uzb",
+            "prompt_name": "null",
+            "word_perplexity": 1248263505.2751954,
+            "byte_perplexity": 12.980834294137205,
+            "bits_per_byte": 3.69831120498359
+        },
+        "gsarti/flores_101_vie+null": {
+            "task_name": "gsarti/flores_101_vie",
+            "prompt_name": "null",
+            "word_perplexity": 33.51752264232948,
+            "byte_perplexity": 1.7976491760484148,
+            "bits_per_byte": 0.8461114961807352
+        },
+        "gsarti/flores_101_cym+null": {
+            "task_name": "gsarti/flores_101_cym",
+            "prompt_name": "null",
+            "word_perplexity": 5900331.966242436,
+            "byte_perplexity": 14.390369428021707,
+            "bits_per_byte": 3.8470317241534553
+        },
+        "gsarti/flores_101_wol+null": {
+            "task_name": "gsarti/flores_101_wol",
+            "prompt_name": "null",
+            "word_perplexity": 199684.7010180392,
+            "byte_perplexity": 10.072733993132132,
+            "bits_per_byte": 3.332383415073327
+        },
+        "gsarti/flores_101_xho+null": {
+            "task_name": "gsarti/flores_101_xho",
+            "prompt_name": "null",
+            "word_perplexity": 141017733.33017766,
+            "byte_perplexity": 8.241450154294917,
+            "bits_per_byte": 3.0428982143908727
+        },
+        "gsarti/flores_101_yor+null": {
+            "task_name": "gsarti/flores_101_yor",
+            "prompt_name": "null",
+            "word_perplexity": 171980.641422536,
+            "byte_perplexity": 6.165831615133067,
+            "bits_per_byte": 2.62429549091613
+        },
+        "gsarti/flores_101_zul+null": {
+            "task_name": "gsarti/flores_101_zul",
+            "prompt_name": "null",
+            "word_perplexity": 998742068.9481835,
+            "byte_perplexity": 9.202622963132773,
+            "bits_per_byte": 3.2020451216662975
+        }
+    },
+    "config": {
+        "adaptive_seq_len": true,
+        "num_fewshot": 0,
+        "bootstrap_iters": 100000
+    }
+}

evaluation/results/tr11/bloom2b5/bslmevalfiles/concat.py

@@ -0,0 +1,103 @@
+import argparse
+import json
+import re
+from pathlib import Path
+from re import Pattern
+from typing import List, Dict
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--results-dir", required=True, type=Path, help="Path to the list of results")
+    parser.add_argument("--concatenate-output-file", required=True, type=Path, help="Path to store the final output file")
+    return parser.parse_args()
+
+MODEL = "tr11b-1b3-ml-bsevalharness-results_lm-eval_global_step337250"
+# MODEL = "global_step95000"
+RESULTS_REGEX = re.compile(rf"(eai|bs)_results_lm-eval_{MODEL}_(\d{4}-\d{2}-\d{2}-\d{2}-\d{2}-\d{2})_backup\.json")
+RESULTS_REGEX = re.compile(rf"{MODEL}_*.json")
+#tr11b-1b3-ml-bsevalharness-results_lm-eval_global_step340500_2022-07-14-10-03-25.json
+def get_all_files_that_match_results_in_folder(root_folder: Path) -> List[Path]:
+    json_files = []
+    for folder in root_folder.iterdir():
+        if folder.is_dir():
+            json_files += get_all_files_that_match_results_in_folder(folder)
+        else:
+            # it's actually a file
+            file = folder
+
+            #match = RESULTS_REGEX.match(file.name)
+
+            if not str(file.name).endswith("json"):
+                continue
+            else:
+                json_files.append(file)
+    return json_files
+
+def sort_dict(dictionary: Dict) -> Dict:
+    results = {}
+
+    for key, value in sorted(dictionary.items()):
+        new_value = value
+
+        if isinstance(value, dict):
+            new_value = sort_dict(new_value)
+        elif isinstance(value, list):
+            new_value = sorted(value)
+
+        results[key] = new_value
+
+    return results
+
+def main():
+    args = get_args()
+
+    # Get all json files
+    json_files = get_all_files_that_match_results_in_folder(args.results_dir)
+    print("GOT", json_files)
+    # Merge all json files
+    final_result = {
+        "results": {},
+        "versions": {}
+    }
+    for file in json_files:
+        with open(file, "r") as fi:
+            task_result = json.load(fi)
+
+        #match = RESULTS_REGEX.match(file.name)
+        #assert match is not None
+        prefix = "bs" if "bs" in file.name else "eai"#match.group(1)
+        datetime_string = file.name[file.name.index("global_step337250_") + len("global_step337250_"):].replace(".json", "")#match.group(2)
+
+        if prefix == "eai":
+            results_key = "results"
+        elif prefix == "bs":
+            results_key = "table_results"
+        else:
+            raise ValueError(f"Unsupported key: {prefix}")
+
+        for key, value in task_result[results_key].items():
+            if key not in final_result["results"]:
+                final_result["results"][key] = {
+                    datetime_string: value
+                }
+            #else:
+            #    assert datetime_string not in final_result["results"][key]
+            #    final_result["results"][key][datetime_string] = value
+
+        for key, value in task_result["versions"].items():
+            final_result["versions"][key] = value
+
+    # We sort dict, better for serialization
+    print(final_result)
+    final_result = sort_dict(final_result)
+
+    # Save result
+    with open(args.concatenate_output_file, "w") as fo:
+        json.dump(final_result, fo, indent=2)
+
+    pass
+
+if __name__ == "__main__":
+    main()
+

evaluation/results/tr11/bloom2b5/bslmevalfiles/tr11c-2b5-ml-evalharness-results_lm-eval_global_step337250_2022-07-13-09-55-04.json

@@ -0,0 +1,172 @@
+{
+    "results": {
+        "arc_challenge": {
+            "acc": 0.27986348122866894,
+            "acc_stderr": 0.013119040897725922,
+            "acc_norm": 0.3054607508532423,
+            "acc_norm_stderr": 0.013460080478002498
+        },
+        "arc_easy": {
+            "acc": 0.5946969696969697,
+            "acc_stderr": 0.010074093589739182,
+            "acc_norm": 0.5324074074074074,
+            "acc_norm_stderr": 0.010238210368801902
+        },
+        "boolq": {
+            "acc": 0.6165137614678899,
+            "acc_stderr": 0.008504304838837027
+        },
+        "copa": {
+            "acc": 0.74,
+            "acc_stderr": 0.04408440022768078
+        },
+        "headqa": {
+            "acc": 0.26440554339897887,
+            "acc_stderr": 0.008423643607316284,
+            "acc_norm": 0.3099927060539752,
+            "acc_norm_stderr": 0.008833810133604958
+        },
+        "hellaswag": {
+            "acc": 0.41236805417247563,
+            "acc_stderr": 0.004912547040132878,
+            "acc_norm": 0.527185819557857,
+            "acc_norm_stderr": 0.0049824003689396615
+        },
+        "lambada": {
+            "ppl": 9.094305394880015,
+            "ppl_stderr": 0.2651922806718523,
+            "acc": 0.5181447700368718,
+            "acc_stderr": 0.0069613892910728266
+        },
+        "logiqa": {
+            "acc": 0.2073732718894009,
+            "acc_stderr": 0.015902084913876333,
+            "acc_norm": 0.29185867895545314,
+            "acc_norm_stderr": 0.017831570553971925
+        },
+        "mathqa": {
+            "acc": 0.24958123953098826,
+            "acc_stderr": 0.007922429819042544,
+            "acc_norm": 0.2492462311557789,
+            "acc_norm_stderr": 0.007918877981680667
+        },
+        "mc_taco": {
+            "em": 0.11936936936936937,
+            "f1": 0.4957122298258418
+        },
+        "mrpc": {
+            "acc": 0.5857843137254902,
+            "acc_stderr": 0.02441658575130785,
+            "f1": 0.6998223801065719,
+            "f1_stderr": 0.021967079752819446
+        },
+        "multirc": {
+            "acc": 0.012591815320041973,
+            "acc_stderr": 0.0036138827653638874
+        },
+        "openbookqa": {
+            "acc": 0.216,
+            "acc_stderr": 0.01842190906141194,
+            "acc_norm": 0.322,
+            "acc_norm_stderr": 0.020916668330019882
+        },
+        "piqa": {
+            "acc": 0.7078346028291621,
+            "acc_stderr": 0.010610252174513661,
+            "acc_norm": 0.705114254624592,
+            "acc_norm_stderr": 0.010639030620156982
+        },
+        "prost": {
+            "acc": 0.22683603757472245,
+            "acc_stderr": 0.003059602302050251,
+            "acc_norm": 0.26371690862510677,
+            "acc_norm_stderr": 0.003219323004106053
+        },
+        "pubmedqa": {
+            "acc": 0.616,
+            "acc_stderr": 0.01538768276189707
+        },
+        "qnli": {
+            "acc": 0.5072304594545122,
+            "acc_stderr": 0.006764703129634549
+        },
+        "qqp": {
+            "acc": 0.38211723967350975,
+            "acc_stderr": 0.0024166004681771985,
+            "f1": 0.5301408768597062,
+            "f1_stderr": 0.002619199330934276
+        },
+        "race": {
+            "acc": 0.3521531100478469,
+            "acc_stderr": 0.014782629897202264
+        },
+        "rte": {
+            "acc": 0.5631768953068592,
+            "acc_stderr": 0.029855247390314945
+        },
+        "sciq": {
+            "acc": 0.892,
+            "acc_stderr": 0.009820001651345703,
+            "acc_norm": 0.817,
+            "acc_norm_stderr": 0.012233587399477823
+        },
+        "sst": {
+            "acc": 0.49426605504587157,
+            "acc_stderr": 0.01694073961990489
+        },
+        "triviaqa": {
+            "acc": 0.041633518960487934,
+            "acc_stderr": 0.0018780954895624524
+        },
+        "webqs": {
+            "acc": 0.01673228346456693,
+            "acc_stderr": 0.0028461549169432184
+        },
+        "wic": {
+            "acc": 0.49843260188087773,
+            "acc_stderr": 0.019810623954060382
+        },
+        "winogrande": {
+            "acc": 0.5864246250986582,
+            "acc_stderr": 0.013840971763195303
+        },
+        "wnli": {
+            "acc": 0.4507042253521127,
+            "acc_stderr": 0.05947027187737998
+        },
+        "wsc": {
+            "acc": 0.375,
+            "acc_stderr": 0.04770204856076104
+        }
+    },
+    "versions": {
+        "arc_challenge": 0,
+        "arc_easy": 0,
+        "boolq": 1,
+        "copa": 0,
+        "headqa": 0,
+        "hellaswag": 0,
+        "lambada": 0,
+        "logiqa": 0,
+        "mathqa": 0,
+        "mc_taco": 0,
+        "mrpc": 0,
+        "multirc": 1,
+        "openbookqa": 0,
+        "piqa": 0,
+        "prost": 0,
+        "pubmedqa": 0,
+        "qnli": 0,
+        "qqp": 0,
+        "race": 1,
+        "rte": 0,
+        "sciq": 0,
+        "sst": 0,
+        "triviaqa": 0,
+        "webqs": 0,
+        "wic": 0,
+        "winogrande": 0,
+        "wnli": 1,
+        "wsc": 0
+    }
+}

evaluation/results/tr11/bloom2b5/humaneval_temp02.json

@@ -0,0 +1 @@
+{"pass@1": 0.06478658536585366, "pass@10": 0.09537740748119838, "pass@100": 0.12348600494571815}

evaluation/results/tr11/bloom2b5/humaneval_temp06.json

@@ -0,0 +1 @@
+{"pass@1": 0.04460365853658537, "pass@10": 0.11354616672373204, "pass@100": 0.1866822927112951}

evaluation/results/tr11/bloom2b5/humaneval_temp08.json

@@ -0,0 +1 @@
+{"pass@1": 0.03411585365853658, "pass@10": 0.10355342714569304, "pass@100": 0.20427664212871136}

evaluation/results/tr11/bloom2b5/mdmeta.txt

@@ -0,0 +1,1540 @@
+model-index:
+- name: bloom
+  results:
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: arc_challenge
+      type: arc_challenge
+    metrics:
+    - name: acc
+      type: acc
+      value: 0.27986348122866894
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: arc_easy
+      type: arc_easy
+    metrics:
+    - name: acc
+      type: acc
+      value: 0.5946969696969697
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: axb
+      type: axb
+    metrics:
+    - name: acc
+      type: acc
+      value: 0.4433876811594203
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: axg
+      type: axg
+    metrics:
+    - name: acc
+      type: acc
+      value: 0.5
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: boolq
+      type: boolq
+    metrics:
+    - name: acc
+      type: acc
+      value: 0.6165137614678899
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: cb
+      type: cb
+    metrics:
+    - name: acc
+      type: acc
+      value: 0.30357142857142855
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: cola
+      type: cola
+    metrics:
+    - name: acc
+      type: acc
+      value: 0.610738255033557
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: copa
+      type: copa
+    metrics:
+    - name: acc
+      type: acc
+      value: 0.63
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: crows_pairs_english
+      type: crows_pairs_english
+    metrics:
+    - name: acc
+      type: acc
+      value: 0.4973166368515206
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: crows_pairs_french
+      type: crows_pairs_french
+    metrics:
+    - name: acc
+      type: acc
+      value: 0.5032796660703638
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: diabla
+      type: diabla
+    metrics:
+    - name: acc
+      type: acc
+      value: 0.28888308977035493
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_afr
+      type: gsarti/flores_101_afr
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 6.500798737976343
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_amh
+      type: gsarti/flores_101_amh
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 3.9726863338897145
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_ara
+      type: gsarti/flores_101_ara
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 1.8083841089875814
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_asm
+      type: gsarti/flores_101_asm
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 5.699102962086425
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_ast
+      type: gsarti/flores_101_ast
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 3.9252047073429384
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_azj
+      type: gsarti/flores_101_azj
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 6.942805054270002
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_bel
+      type: gsarti/flores_101_bel
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 3.614136245847082
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_ben
+      type: gsarti/flores_101_ben
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 5.121491534300969
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_bos
+      type: gsarti/flores_101_bos
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 5.653353469118798
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_bul
+      type: gsarti/flores_101_bul
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 2.7014693938055068
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_cat
+      type: gsarti/flores_101_cat
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 2.305190041967345
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_ceb
+      type: gsarti/flores_101_ceb
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 6.291000321323428
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_ces
+      type: gsarti/flores_101_ces
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 5.447322753586386
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_ckb
+      type: gsarti/flores_101_ckb
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 3.7255124939234765
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_cym
+      type: gsarti/flores_101_cym
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 12.539424151448149
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_dan
+      type: gsarti/flores_101_dan
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 5.183309001005672
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_deu
+      type: gsarti/flores_101_deu
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 3.1180422286591347
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_ell
+      type: gsarti/flores_101_ell
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 2.467943456164706
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_eng
+      type: gsarti/flores_101_eng
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 2.018740628193298
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_est
+      type: gsarti/flores_101_est
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 9.11654425176368
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_fas
+      type: gsarti/flores_101_fas
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 3.058009097116482
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_fin
+      type: gsarti/flores_101_fin
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 6.847047959628553
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_fra
+      type: gsarti/flores_101_fra
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 1.9975177011840075
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_ful
+      type: gsarti/flores_101_ful
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 11.465912731488828
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_gle
+      type: gsarti/flores_101_gle
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 8.681491663539422
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_glg
+      type: gsarti/flores_101_glg
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 3.029991089015508
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_guj
+      type: gsarti/flores_101_guj
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 4.955224230286231
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_hau
+      type: gsarti/flores_101_hau
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 10.758347356372159
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_heb
+      type: gsarti/flores_101_heb
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 3.6004478129801667
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_hin
+      type: gsarti/flores_101_hin
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 4.712530650588064
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_hrv
+      type: gsarti/flores_101_hrv
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 5.822418943372185
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_hun
+      type: gsarti/flores_101_hun
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 6.440482646965992
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_hye
+      type: gsarti/flores_101_hye
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 3.657718918347166
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_ibo
+      type: gsarti/flores_101_ibo
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 5.564814003872672
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_ind
+      type: gsarti/flores_101_ind
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 2.1597101468869373
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_isl
+      type: gsarti/flores_101_isl
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 8.082349269518136
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_ita
+      type: gsarti/flores_101_ita
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 2.9687591414176207
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_jav
+      type: gsarti/flores_101_jav
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 7.0573805415708994
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_jpn
+      type: gsarti/flores_101_jpn
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 2.7758864197116933
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_kam
+      type: gsarti/flores_101_kam
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 11.072949642861332
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_kan
+      type: gsarti/flores_101_kan
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 5.551730651007082
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_kat
+      type: gsarti/flores_101_kat
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 2.522630524283745
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_kaz
+      type: gsarti/flores_101_kaz
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 3.3901748516975574
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_kea
+      type: gsarti/flores_101_kea
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 8.918534182590863
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_kir
+      type: gsarti/flores_101_kir
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 3.729278369847201
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_kor
+      type: gsarti/flores_101_kor
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 3.932884847226212
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_lao
+      type: gsarti/flores_101_lao
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 2.9077314760849924
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_lav
+      type: gsarti/flores_101_lav
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 7.777221919194806
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_lin
+      type: gsarti/flores_101_lin
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 7.524842908050988
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_lit
+      type: gsarti/flores_101_lit
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 7.369179434621725
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_ltz
+      type: gsarti/flores_101_ltz
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 8.801059747949214
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_lug
+      type: gsarti/flores_101_lug
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 8.483203026364786
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_luo
+      type: gsarti/flores_101_luo
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 11.975963093623681
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_mal
+      type: gsarti/flores_101_mal
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 4.615948455160037
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_mar
+      type: gsarti/flores_101_mar
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 5.483253482821379
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_mkd
+      type: gsarti/flores_101_mkd
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 2.9656732291754087
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_mlt
+      type: gsarti/flores_101_mlt
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 15.004773437665275
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_mon
+      type: gsarti/flores_101_mon
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 3.410598542315402
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_mri
+      type: gsarti/flores_101_mri
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 7.474035895661322
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_msa
+      type: gsarti/flores_101_msa
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 2.5710001772665634
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_mya
+      type: gsarti/flores_101_mya
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 2.413577969878331
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_nld
+      type: gsarti/flores_101_nld
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 4.127831721885065
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_nob
+      type: gsarti/flores_101_nob
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 5.402763169129877
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_npi
+      type: gsarti/flores_101_npi
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 5.199342701937889
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_nso
+      type: gsarti/flores_101_nso
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 8.154626800955667
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_nya
+      type: gsarti/flores_101_nya
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 8.179860208369393
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_oci
+      type: gsarti/flores_101_oci
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 4.8617357393685845
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_orm
+      type: gsarti/flores_101_orm
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 12.911595421079408
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_ory
+      type: gsarti/flores_101_ory
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 5.189421861225964
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_pan
+      type: gsarti/flores_101_pan
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 4.698477289331806
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_pol
+      type: gsarti/flores_101_pol
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 4.625550458479643
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_por
+      type: gsarti/flores_101_por
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 1.9754515986213523
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_pus
+      type: gsarti/flores_101_pus
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 4.4963371422771585
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_ron
+      type: gsarti/flores_101_ron
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 4.965456830031304
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_rus
+      type: gsarti/flores_101_rus
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 2.0498020542445303
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_slk
+      type: gsarti/flores_101_slk
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 6.450822127057479
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_slv
+      type: gsarti/flores_101_slv
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 6.620252120186232
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_sna
+      type: gsarti/flores_101_sna
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 8.462166771382726
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_snd
+      type: gsarti/flores_101_snd
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 5.466066951221973
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_som
+      type: gsarti/flores_101_som
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 11.95918054093392
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_spa
+      type: gsarti/flores_101_spa
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 1.8965140104323535
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_srp
+      type: gsarti/flores_101_srp
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 2.871214785885079
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_swe
+      type: gsarti/flores_101_swe
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 5.054972008155866
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_swh
+      type: gsarti/flores_101_swh
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 3.6973091886730676
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_tam
+      type: gsarti/flores_101_tam
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 4.539493400469833
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_tel
+      type: gsarti/flores_101_tel
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 5.807499987508966
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_tgk
+      type: gsarti/flores_101_tgk
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 3.5994818827380426
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_tgl
+      type: gsarti/flores_101_tgl
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 5.667053833119858
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_tha
+      type: gsarti/flores_101_tha
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 2.365940201944242
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_tur
+      type: gsarti/flores_101_tur
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 4.885014749844601
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_ukr
+      type: gsarti/flores_101_ukr
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 2.7240934990288483
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_umb
+      type: gsarti/flores_101_umb
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 12.766915508610673
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_urd
+      type: gsarti/flores_101_urd
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 1.9797467071381232
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_uzb
+      type: gsarti/flores_101_uzb
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 12.002337637722146
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_vie
+      type: gsarti/flores_101_vie
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 1.76578415476397
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_wol
+      type: gsarti/flores_101_wol
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 9.144285650306488
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_xho
+      type: gsarti/flores_101_xho
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 7.403240538286952
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_yor
+      type: gsarti/flores_101_yor
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 5.91272037551173
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_zho_simpl
+      type: gsarti/flores_101_zho_simpl
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 2.2769070822768533
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_zho_trad
+      type: gsarti/flores_101_zho_trad
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 2.5180582198242383
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_zul
+      type: gsarti/flores_101_zul
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 8.53353320693145
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: headqa
+      type: headqa
+    metrics:
+    - name: acc
+      type: acc
+      value: 0.26440554339897887
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: hellaswag
+      type: hellaswag
+    metrics:
+    - name: acc
+      type: acc
+      value: 0.41236805417247563
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: logiqa
+      type: logiqa
+    metrics:
+    - name: acc
+      type: acc
+      value: 0.2073732718894009
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: mathqa
+      type: mathqa
+    metrics:
+    - name: acc
+      type: acc
+      value: 0.24958123953098826
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: mc_taco
+      type: mc_taco
+    metrics:
+    - name: em
+      type: em
+      value: 0.11936936936936937
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: mnli
+      type: mnli
+    metrics:
+    - name: acc
+      type: acc
+      value: 0.35496688741721855
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: mnli_mismatched
+      type: mnli_mismatched
+    metrics:
+    - name: acc
+      type: acc
+      value: 0.35211554109031734
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: mrpc
+      type: mrpc
+    metrics:
+    - name: acc
+      type: acc
+      value: 0.5857843137254902
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: multirc
+      type: multirc
+    metrics:
+    - name: acc
+      type: acc
+      value: 0.5375412541254125
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: openbookqa
+      type: openbookqa
+    metrics:
+    - name: acc
+      type: acc
+      value: 0.216
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: piqa
+      type: piqa
+    metrics:
+    - name: acc
+      type: acc
+      value: 0.7078346028291621
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: prost
+      type: prost
+    metrics:
+    - name: acc
+      type: acc
+      value: 0.22683603757472245
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: pubmedqa
+      type: pubmedqa
+    metrics:
+    - name: acc
+      type: acc
+      value: 0.616
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: qnli
+      type: qnli
+    metrics:
+    - name: acc
+      type: acc
+      value: 0.5072304594545122
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: qqp
+      type: qqp
+    metrics:
+    - name: acc
+      type: acc
+      value: 0.3842443729903537
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: race
+      type: race
+    metrics:
+    - name: acc
+      type: acc
+      value: 0.3521531100478469
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: rte
+      type: rte
+    metrics:
+    - name: acc
+      type: acc
+      value: 0.47653429602888087
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: sciq
+      type: sciq
+    metrics:
+    - name: acc
+      type: acc
+      value: 0.892
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: sst
+      type: sst
+    metrics:
+    - name: acc
+      type: acc
+      value: 0.5177752293577982
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: triviaqa
+      type: triviaqa
+    metrics:
+    - name: acc
+      type: acc
+      value: 0.041633518960487934
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: tydiqa_primary
+      type: tydiqa_primary
+    metrics:
+    - name: acc
+      type: acc
+      value: 0.3011337608795236
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: webqs
+      type: webqs
+    metrics:
+    - name: acc
+      type: acc
+      value: 0.01673228346456693
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: wic
+      type: wic
+    metrics:
+    - name: acc
+      type: acc
+      value: 0.5015673981191222
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: winogrande
+      type: winogrande
+    metrics:
+    - name: acc
+      type: acc
+      value: 0.5864246250986582
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: wnli
+      type: wnli
+    metrics:
+    - name: acc
+      type: acc
+      value: 0.471830985915493
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: wsc
+      type: wsc
+    metrics:
+    - name: acc
+      type: acc
+      value: 0.4423076923076923
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: humaneval
+      type: humaneval
+    metrics:
+    - name: pass@1
+      type: pass@1
+      value: 0.15524390243902436
+      verified: false
+    - name: pass@10
+      type: pass@10
+      value: 0.3220367632383857
+      verified: false
+    - name: pass@100
+      type: pass@100
+      value: 0.5545431515723145
+      verified: false

evaluation/results/tr11/bloom2b5/mdtable.txt

@@ -0,0 +1,143 @@
+| Task | Language | Metric | BLOOM-2B5 |
+|:----|:----|:----|:----:|
+| arc_challenge | eng | acc ↑ | 0.28 |
+| arc_easy | eng | acc ↑ | 0.595 |
+| axb (Median of 10 prompts) | eng | acc ↑ | 0.443 |
+| axg (Median of 10 prompts) | eng | acc ↑ | 0.5 |
+| boolq (Median of 11 prompts) | eng | acc ↑ | 0.617 |
+| cb (Median of 15 prompts) | eng | acc ↑ | 0.304 |
+| cola (Median of 5 prompts) | eng | acc ↑ | 0.611 |
+| copa (Median of 9 prompts) | eng | acc ↑ | 0.63 |
+| crows_pairs_english (Median of 6 prompts) | eng | acc ↑ | 0.497 |
+| crows_pairs_french (Median of 7 prompts) | fra | acc ↑ | 0.503 |
+| diabla (Median of 2 prompts) | eng | acc ↑ | 0.289 |
+| gsarti/flores_101_afr | afr | byte_perplexity ↓ | 6.501 |
+| gsarti/flores_101_amh | amh | byte_perplexity ↓ | 3.973 |
+| gsarti/flores_101_ara | ara | byte_perplexity ↓ | 1.808 |
+| gsarti/flores_101_asm | asm | byte_perplexity ↓ | 5.699 |
+| gsarti/flores_101_ast | ast | byte_perplexity ↓ | 3.925 |
+| gsarti/flores_101_azj | azj | byte_perplexity ↓ | 6.943 |
+| gsarti/flores_101_bel | bel | byte_perplexity ↓ | 3.614 |
+| gsarti/flores_101_ben | ben | byte_perplexity ↓ | 5.121 |
+| gsarti/flores_101_bos | bos | byte_perplexity ↓ | 5.653 |
+| gsarti/flores_101_bul | bul | byte_perplexity ↓ | 2.701 |
+| gsarti/flores_101_cat | cat | byte_perplexity ↓ | 2.305 |
+| gsarti/flores_101_ceb | ceb | byte_perplexity ↓ | 6.291 |
+| gsarti/flores_101_ces | ces | byte_perplexity ↓ | 5.447 |
+| gsarti/flores_101_ckb | ckb | byte_perplexity ↓ | 3.726 |
+| gsarti/flores_101_cym | cym | byte_perplexity ↓ | 12.539 |
+| gsarti/flores_101_dan | dan | byte_perplexity ↓ | 5.183 |
+| gsarti/flores_101_deu | deu | byte_perplexity ↓ | 3.118 |
+| gsarti/flores_101_ell | ell | byte_perplexity ↓ | 2.468 |
+| gsarti/flores_101_eng | eng | byte_perplexity ↓ | 2.019 |
+| gsarti/flores_101_est | est | byte_perplexity ↓ | 9.117 |
+| gsarti/flores_101_fas | fas | byte_perplexity ↓ | 3.058 |
+| gsarti/flores_101_fin | fin | byte_perplexity ↓ | 6.847 |
+| gsarti/flores_101_fra | fra | byte_perplexity ↓ | 1.998 |
+| gsarti/flores_101_ful | ful | byte_perplexity ↓ | 11.466 |
+| gsarti/flores_101_gle | gle | byte_perplexity ↓ | 8.681 |
+| gsarti/flores_101_glg | glg | byte_perplexity ↓ | 3.03 |
+| gsarti/flores_101_guj | guj | byte_perplexity ↓ | 4.955 |
+| gsarti/flores_101_hau | hau | byte_perplexity ↓ | 10.758 |
+| gsarti/flores_101_heb | heb | byte_perplexity ↓ | 3.6 |
+| gsarti/flores_101_hin | hin | byte_perplexity ↓ | 4.713 |
+| gsarti/flores_101_hrv | hrv | byte_perplexity ↓ | 5.822 |
+| gsarti/flores_101_hun | hun | byte_perplexity ↓ | 6.44 |
+| gsarti/flores_101_hye | hye | byte_perplexity ↓ | 3.658 |
+| gsarti/flores_101_ibo | ibo | byte_perplexity ↓ | 5.565 |
+| gsarti/flores_101_ind | ind | byte_perplexity ↓ | 2.16 |
+| gsarti/flores_101_isl | isl | byte_perplexity ↓ | 8.082 |
+| gsarti/flores_101_ita | ita | byte_perplexity ↓ | 2.969 |
+| gsarti/flores_101_jav | jav | byte_perplexity ↓ | 7.057 |
+| gsarti/flores_101_jpn | jpn | byte_perplexity ↓ | 2.776 |
+| gsarti/flores_101_kam | kam | byte_perplexity ↓ | 11.073 |
+| gsarti/flores_101_kan | kan | byte_perplexity ↓ | 5.552 |
+| gsarti/flores_101_kat | kat | byte_perplexity ↓ | 2.523 |
+| gsarti/flores_101_kaz | kaz | byte_perplexity ↓ | 3.39 |
+| gsarti/flores_101_kea | kea | byte_perplexity ↓ | 8.919 |
+| gsarti/flores_101_kir | kir | byte_perplexity ↓ | 3.729 |
+| gsarti/flores_101_kor | kor | byte_perplexity ↓ | 3.933 |
+| gsarti/flores_101_lao | lao | byte_perplexity ↓ | 2.908 |
+| gsarti/flores_101_lav | lav | byte_perplexity ↓ | 7.777 |
+| gsarti/flores_101_lin | lin | byte_perplexity ↓ | 7.525 |
+| gsarti/flores_101_lit | lit | byte_perplexity ↓ | 7.369 |
+| gsarti/flores_101_ltz | ltz | byte_perplexity ↓ | 8.801 |
+| gsarti/flores_101_lug | lug | byte_perplexity ↓ | 8.483 |
+| gsarti/flores_101_luo | luo | byte_perplexity ↓ | 11.976 |
+| gsarti/flores_101_mal | mal | byte_perplexity ↓ | 4.616 |
+| gsarti/flores_101_mar | mar | byte_perplexity ↓ | 5.483 |
+| gsarti/flores_101_mkd | mkd | byte_perplexity ↓ | 2.966 |
+| gsarti/flores_101_mlt | mlt | byte_perplexity ↓ | 15.005 |
+| gsarti/flores_101_mon | mon | byte_perplexity ↓ | 3.411 |
+| gsarti/flores_101_mri | mri | byte_perplexity ↓ | 7.474 |
+| gsarti/flores_101_msa | msa | byte_perplexity ↓ | 2.571 |
+| gsarti/flores_101_mya | mya | byte_perplexity ↓ | 2.414 |
+| gsarti/flores_101_nld | nld | byte_perplexity ↓ | 4.128 |
+| gsarti/flores_101_nob | nob | byte_perplexity ↓ | 5.403 |
+| gsarti/flores_101_npi | npi | byte_perplexity ↓ | 5.199 |
+| gsarti/flores_101_nso | nso | byte_perplexity ↓ | 8.155 |
+| gsarti/flores_101_nya | nya | byte_perplexity ↓ | 8.18 |
+| gsarti/flores_101_oci | oci | byte_perplexity ↓ | 4.862 |
+| gsarti/flores_101_orm | orm | byte_perplexity ↓ | 12.912 |
+| gsarti/flores_101_ory | ory | byte_perplexity ↓ | 5.189 |
+| gsarti/flores_101_pan | pan | byte_perplexity ↓ | 4.698 |
+| gsarti/flores_101_pol | pol | byte_perplexity ↓ | 4.626 |
+| gsarti/flores_101_por | por | byte_perplexity ↓ | 1.975 |
+| gsarti/flores_101_pus | pus | byte_perplexity ↓ | 4.496 |
+| gsarti/flores_101_ron | ron | byte_perplexity ↓ | 4.965 |
+| gsarti/flores_101_rus | rus | byte_perplexity ↓ | 2.05 |
+| gsarti/flores_101_slk | slk | byte_perplexity ↓ | 6.451 |
+| gsarti/flores_101_slv | slv | byte_perplexity ↓ | 6.62 |
+| gsarti/flores_101_sna | sna | byte_perplexity ↓ | 8.462 |
+| gsarti/flores_101_snd | snd | byte_perplexity ↓ | 5.466 |
+| gsarti/flores_101_som | som | byte_perplexity ↓ | 11.959 |
+| gsarti/flores_101_spa | spa | byte_perplexity ↓ | 1.897 |
+| gsarti/flores_101_srp | srp | byte_perplexity ↓ | 2.871 |
+| gsarti/flores_101_swe | swe | byte_perplexity ↓ | 5.055 |
+| gsarti/flores_101_swh | swh | byte_perplexity ↓ | 3.697 |
+| gsarti/flores_101_tam | tam | byte_perplexity ↓ | 4.539 |
+| gsarti/flores_101_tel | tel | byte_perplexity ↓ | 5.807 |
+| gsarti/flores_101_tgk | tgk | byte_perplexity ↓ | 3.599 |
+| gsarti/flores_101_tgl | tgl | byte_perplexity ↓ | 5.667 |
+| gsarti/flores_101_tha | tha | byte_perplexity ↓ | 2.366 |
+| gsarti/flores_101_tur | tur | byte_perplexity ↓ | 4.885 |
+| gsarti/flores_101_ukr | ukr | byte_perplexity ↓ | 2.724 |
+| gsarti/flores_101_umb | umb | byte_perplexity ↓ | 12.767 |
+| gsarti/flores_101_urd | urd | byte_perplexity ↓ | 1.98 |
+| gsarti/flores_101_uzb | uzb | byte_perplexity ↓ | 12.002 |
+| gsarti/flores_101_vie | vie | byte_perplexity ↓ | 1.766 |
+| gsarti/flores_101_wol | wol | byte_perplexity ↓ | 9.144 |
+| gsarti/flores_101_xho | xho | byte_perplexity ↓ | 7.403 |
+| gsarti/flores_101_yor | yor | byte_perplexity ↓ | 5.913 |
+| gsarti/flores_101_zho_simpl | zho_simpl | byte_perplexity ↓ | 2.277 |
+| gsarti/flores_101_zho_trad | zho_trad | byte_perplexity ↓ | 2.518 |
+| gsarti/flores_101_zul | zul | byte_perplexity ↓ | 8.534 |
+| headqa | esp | acc ↑ | 0.264 |
+| hellaswag | eng | acc ↑ | 0.412 |
+| logiqa | eng | acc ↑ | 0.207 |
+| mathqa | eng | acc ↑ | 0.25 |
+| mc_taco | eng | em ↑ | 0.119 |
+| mnli (Median of 15 prompts) | eng | acc ↑ | 0.355 |
+| mnli_mismatched (Median of 15 prompts) | eng | acc ↑ | 0.352 |
+| mrpc | eng | acc ↑ | 0.586 |
+| multirc (Median of 11 prompts) | eng | acc ↑ | 0.538 |
+| openbookqa | eng | acc ↑ | 0.216 |
+| piqa | eng | acc ↑ | 0.708 |
+| prost | eng | acc ↑ | 0.227 |
+| pubmedqa | eng | acc ↑ | 0.616 |
+| qnli | eng | acc ↑ | 0.507 |
+| qqp (Median of 7 prompts) | eng | acc ↑ | 0.384 |
+| race | eng | acc ↑ | 0.352 |
+| rte (Median of 6 prompts) | eng | acc ↑ | 0.477 |
+| sciq | eng | acc ↑ | 0.892 |
+| sst (Median of 6 prompts) | eng | acc ↑ | 0.518 |
+| triviaqa | eng | acc ↑ | 0.042 |
+| tydiqa_primary (Median of 24 prompts) | eng | acc ↑ | 0.301 |
+| webqs | eng | acc ↑ | 0.017 |
+| wic (Median of 11 prompts) | eng | acc ↑ | 0.502 |
+| winogrande | eng | acc ↑ | 0.586 |
+| wnli (Median of 6 prompts) | eng | acc ↑ | 0.472 |
+| wsc (Median of 11 prompts) | eng | acc ↑ | 0.442 |
+| humaneval | python | pass@1 ↑ | 0.155 |
+| humaneval | python | pass@10 ↑ | 0.322 |
+| humaneval | python | pass@100 ↑ | 0.555 |

evaluation/results/tr11/conversion/json_to_markdown.py

@@ -0,0 +1,307 @@
+"""
+Table example:
+
+| Task | Language | Metric | BLOOM-176B | OPT-176B |
+|:--------|:-----------------|:------------------------|-------------:|------------:|
+| arc_challenge | eng | acc | 0.4112627986348123 | 0.4121160409556314 |
+
+
+Metadata example:
+
+model-index:
+- name: bart-large-cnn-samsum
+  results:
+    - task:
+      type: summarization
+      name: Summarization
+    dataset:
+      name: 'SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization'
+      type: samsum
+    metrics:
+    - name: Validation ROGUE-1
+      type: rogue-1
+      value: 42.621
+    - name: Validation ROGUE-2
+      type: rogue-2
+      value: 21.9825
+    - name: Validation ROGUE-L
+      type: rogue-l
+      value: 33.034
+    - name: Test ROGUE-1
+      type: rogue-1
+      value: 41.3174
+    - name: Test ROGUE-2
+      type: rogue-2
+      value: 20.8716
+    - name: Test ROGUE-L
+      type: rogue-l
+      value: 32.1337
+  - task:
+      type: summarization
+      name: Summarization
+    dataset:
+      name: samsum
+      type: samsum
+      config: samsum
+      split: test
+    metrics:
+    - name: ROUGE-1
+      type: rouge
+      value: 41.3282
+      verified: true
+    - name: ROUGE-2
+      type: rouge
+      value: 20.8755
+      verified: true
+    - name: ROUGE-L
+      type: rouge
+      value: 32.1353
+      verified: true
+    - name: ROUGE-LSUM
+      type: rouge
+      value: 38.401
+      verified: true
+    - name: loss
+      type: loss
+      value: 1.4297215938568115
+      verified: true
+    - name: gen_len
+      type: gen_len
+      value: 60.0757
+      verified: true
+"""
+
+import json
+import statistics
+
+FILE_NAMES = ["bslmeval", "humaneval_temp02", "humaneval_temp06", "humaneval_temp08"]
+
+# Optionally subselect tasks
+SELECTED_LIST = [
+    "winogrande"
+]
+
+with open("bloom2b5/bslmeval.json", "r") as f:
+    bloom_bslmeval = json.load(f)
+
+with open("opt/bslmeval.json", "r") as f:
+    opt_bslmeval = json.load(f)
+
+
+
+results_formatted = {}
+for task_name in bloom_bslmeval["results"]:
+    #if task_name not in SELECTED_LIST:
+    #    continue
+    date_keys = list(bloom_bslmeval["results"][task_name].keys())
+    assert len(date_keys) == 1
+    metrics = bloom_bslmeval["results"][task_name][date_keys[0]]
+
+    lang = "eng"
+    if "gsarti/flores_101_" in task_name:
+        lang = task_name.replace("gsarti/flores_101_", "").replace("+null", "")
+    elif "lambada_mt_de" in task_name:
+        lang = "deu"
+    elif "lambada_mt_en" in task_name:
+        lang = "eng"
+    elif "lambada_mt_es" in task_name:
+        lang = "esp"
+    elif "lambada_mt_it" in task_name:
+        lang = "ita"
+    elif "lambada" == task_name:
+        continue
+    elif "crows_pairs_french" in task_name:
+        lang = "fra"
+    elif "headqa" == task_name:
+        lang = "esp"
+
+    if "acc" in metrics:
+        main_metric_name = "acc ↑"
+    elif "byte_perplexity" in metrics:
+        main_metric_name = "byte_perplexity ↓"
+    elif "pass@100" in metrics:
+        main_metric_name = "pass@100 ↑"
+    elif "em" in metrics:
+        main_metric_name = "em ↑"
+
+    date_keys_opt = list(opt_bslmeval["results"][task_name].keys())
+    score_opt = opt_bslmeval["results"][task_name][date_keys_opt[0]][main_metric_name[:-2]]
+
+    fin_task_name = metrics.get("task_name", task_name)
+    
+    results_formatted.setdefault(fin_task_name, {})
+    results_formatted[fin_task_name].setdefault("prompts", [])
+    results_formatted[fin_task_name].setdefault("all_metrics", [])
+    results_formatted[fin_task_name].setdefault("main_metrics", [])
+
+    if "prompt_name" in metrics:
+        results_formatted[fin_task_name]["prompts"].append(metrics["prompt_name"])
+    results_formatted[fin_task_name]["name"] = fin_task_name
+    results_formatted[fin_task_name]["lang"] = lang
+    results_formatted[fin_task_name]["all_metrics"].append(metrics) # [{name: score}]
+    results_formatted[fin_task_name]["main_metrics"].append((main_metric_name, metrics[main_metric_name[:-2]], score_opt))
+    results_formatted[fin_task_name]["type"] = "text-generation"
+
+# Take Median of scores
+for k, v in results_formatted.items():
+    if "prompts" in v and len(v["prompts"]) > 1:
+        assert len(v["all_metrics"]) == len(v["main_metrics"])
+        num_scores = len(v["main_metrics"])
+
+        bloom_median = statistics.median([triplet[1] for triplet in v["main_metrics"]])
+        opt_median = statistics.median([triplet[2] for triplet in v["main_metrics"]])
+
+        results_formatted[k]["main_metrics"] = [(
+            v["main_metrics"][0][0],
+            bloom_median,
+            opt_median,
+        )]
+
+        results_formatted[k]["name"] = results_formatted[k]["name"] + f" (Median of {num_scores} prompts)"
+
+
+
+def keep_best_score(new_eval, old_eval):
+    for k, v in new_eval.items():
+        old_eval[k] = max(old_eval[k], v) 
+    return old_eval
+
+for i, temp in enumerate(["02", "06", "08"]):
+    with open(f"bloom/humaneval_temp{temp}.json", "r") as f:
+        if i > 0:
+            keep_best_score(json.load(f), bloom_humaneval)
+        else:
+            bloom_humaneval = json.load(f)
+    with open(f"opt/humaneval_temp{temp}.json", "r") as f:
+        if i > 0:
+            keep_best_score(json.load(f), opt_humaneval)
+        else:
+            opt_humaneval = json.load(f)
+
+results_formatted["humaneval"] = {
+    "name": "humaneval",
+    "lang": "python",
+    "all_metrics": [bloom_humaneval], # [{name: score}]
+    "main_metrics": [(f"{name} ↑", score, opt_humaneval[name]) for name, score in bloom_humaneval.items()],
+    "type": "text-generation"
+}
+
+
+
+# Add multilingual average
+for k, v in results_formatted.items():
+    if "prompts" in v and len(v["prompts"]) > 1 and len(v["main_metrics"]) > 1:
+        assert len(v["all_metrics"]) == len(v["main_metrics"]), f"{k}, {len(v['all_metrics'])}, {len(v['main_metrics'])}"
+        num_scores = len(v["main_metrics"])
+
+        bloom_median = statistics.median([triplet[1] for triplet in v["main_metrics"]])
+        opt_median = statistics.median([triplet[2] for triplet in v["main_metrics"]])
+
+        results_formatted[k]["main_metrics"] = [(
+            v["main_metrics"][0][0],
+            bloom_median,
+            opt_median,
+        )]
+
+        results_formatted[k]["name"] = results_formatted[k]["name"] + f" (Median of {num_scores} prompts)"
+
+"""Optional aggregated statistics
+bloom_mean = statistics.mean([triplet[1] for k,v in results_formatted.items() for triplet in v["main_metrics"] if v["lang"] == "eng"])
+opt_mean = statistics.mean([triplet[2] for k,v in results_formatted.items() for triplet in v["main_metrics"] if v["lang"] == "eng"])
+
+results_formatted["mean_eng"] = {
+    "name": "mean_eng ↑",
+    "lang": "eng",
+    "all_metrics": [{"mean": bloom_mean}], # [{name: score}]
+    "main_metrics": [("mean", bloom_mean, opt_mean)],
+    "type": "text-generation"
+}
+
+bloom_mean = statistics.mean([triplet[1] for k,v in results_formatted.items() for triplet in v["main_metrics"] if "flores" in k])
+opt_mean = statistics.mean([triplet[2] for k,v in results_formatted.items() for triplet in v["main_metrics"] if "flores" in k])
+
+results_formatted["mean_multilingual"] = {
+    "name": "mean_multilingual (Flores) ↓",
+    "lang": "mul",
+    "all_metrics": [{"mean": bloom_mean}], # [{name: score}]
+    "main_metrics": [("mean", bloom_mean, opt_mean)],
+    "type": "text-generation"
+}
+
+main_metrics = ([triplet for k,v in results_formatted.items() for triplet in v["main_metrics"]])
+
+bloom_best_on, opt_best_on = 0,0
+for (name, bloom, opt) in main_metrics:
+    if name[:-2] in ["acc", "em"] or "pass" in name:
+        if bloom > opt:
+            bloom_best_on += 1
+        elif bloom < opt:
+            opt_best_on += 1
+    elif name[:-2] in ["byte_perplexity"]:
+        if bloom < opt:
+            bloom_best_on += 1
+        elif bloom > opt:
+            opt_best_on += 1
+"""
+### Markdown Table ###
+
+HEADER = "| Task | Language | Metric | BLOOM-350M | BLOOM-750M | BLOOM-1B3 | BLOOM-2B5 | BLOOM-6B3 | BLOOM-176B |"
+SEP = "|:----|:----|:----|:----:|"
+ONE_LINE = "| {} | {} | {} | {} |"
+
+TABLE_STRING = "\n".join([HEADER, SEP])
+
+for task_name, res_dict in results_formatted.items():
+    for (name, score, score_opt) in res_dict["main_metrics"]:
+        TABLE_STRING += "\n" + ONE_LINE.format(
+            res_dict["name"],
+            res_dict["lang"],
+            name,
+            round(score, 3),
+            round(score_opt, 3),
+        )
+
+with open("./mdtable.txt", "w") as f:
+    f.write(TABLE_STRING)
+
+
+
+### Metadata ###
+
+HEADER = "model-index:"
+MODEL = "- name: bloom"
+RES = "  results:"
+
+META_STRING = "\n".join([HEADER, MODEL, RES])
+
+ONE_TASK = "  - task:\n      type: {}\n      name: {}\n    dataset:\n      name: {}\n      type: {}\n    metrics:"
+ONE_METRIC = "    - name: {}\n      type: {}\n      value: {}\n      verified: false"
+
+for task_name, res_dict in results_formatted.items():
+    META_STRING += "\n" + ONE_TASK.format(
+        res_dict["type"],
+        res_dict["type"].replace("-", " "),
+        task_name,
+        task_name,
+    )
+    for (name, score, score_opt) in res_dict["main_metrics"]:
+            META_STRING += "\n" + ONE_METRIC.format(
+                name.split(" ")[0],
+                name.split(" ")[0],
+                score
+            )   
+"""
+    for metrics in res_dict["all_metrics"]:
+        for metric_name, metric in metrics.items():
+            if isinstance(metric, str):
+                continue
+            META_STRING += "\n" + ONE_METRIC.format(
+                metric_name,
+                metric_name,
+                metric
+            )   
+"""
+
+
+with open("./mdmeta.txt", "w") as f:
+    f.write(META_STRING)

evaluation/results/tr11/opt/humaneval_temp06.json

@@ -0,0 +1 @@
+{"pass@1": 3.0487804878048808e-05, "pass@10": 0.0003048780487804881, "pass@100": 0.003048780487804878}

evaluation/results/tr11/scripts/download_bsevalharness.py

@@ -0,0 +1,21 @@
+# Downloads the specified taks in the evaluation harness
+# This is particularly useful when running in environments where the GPU nodes 
+# do not have internet access. This way we can pre-download them and use the cached data-set during evaluation.
+
+from lm_eval import tasks
+from lm_eval.tasks import ALL_TASKS
+import argparse
+import os
+
+
+parser = argparse.ArgumentParser(description='Download evaluation harness', allow_abbrev=False)
+parser.add_argument('--task_list', type=str, default = "all", help='Either "all" or comma separated list of tasks to download.')
+args = parser.parse_args()
+
+def main():
+    task_list = ALL_TASKS if args.task_list == 'all' else args.task_list.split(',')
+    tasks.get_task_dict_promptsource(task_list)
+
+if __name__ == '__main__':
+    main()
+

evaluation/results/tr11/scripts/run_bsevalharness_generation_6b3.slurm

@@ -0,0 +1,101 @@
+#!/bin/bash
+#SBATCH --job-name=evaluate_t0
+#SBATCH --nodes=1
+#SBATCH --ntasks-per-node=1          # crucial - only 1 task per dist per node!
+#SBATCH --cpus-per-task=8           # number of cores per tasks
+#SBATCH --hint=nomultithread         # we get physical cores not logical
+#SBATCH --gres=gpu:1                 # number of gpus
+#SBATCH --constraint=a100
+#SBATCH --reservation=hug
+#SBATCH --time 20:00:00             # maximum execution time (HH:MM:SS)
+#SBATCH --output=%x-%j.out           # output file name
+#SBATCH --account=six@a100
+
+set -x -e
+
+source $six_ALL_CCFRWORK/start-tr13f-6B3-ml-t0
+conda activate muennighofflmevalgen
+
+echo "START TIME: $(date)"
+
+# defining the right environment variables
+export TRANSFORMERS_CACHE=$six_ALL_CCFRWORK/models
+export HF_DATASETS_CACHE=$six_ALL_CCFRWORK/datasets
+export HF_MODULES_CACHE=$six_ALL_CCFRWORK/modules
+export HF_METRICS_CACHE=$six_ALL_CCFRWORK/metrics
+export HF_DATASETS_OFFLINE=1
+export TRANSFORMERS_OFFLINE=1
+export TOKENIZERS_PARALLELISM=false
+
+# Converted transformer checkpoint
+MODEL_CKPT=/gpfsscratch/rech/six/commun/experiments/muennighoff/bloomckpt/6b3/bloom-7b1
+
+cd /gpfsscratch/rech/six/commun/experiments/muennighoff/bslmevalgeneration/lm-evaluation-harness
+
+# WMT19 ZH-EN does not work
+DATASETS_AND_CONFIGS=(
+GEM/wiki_lingua_en,en,"article_summary_en"
+GEM/wiki_lingua_en,en,"write_abstract_en"
+GEM/wiki_lingua_en,en,"summarize_above_en"
+GEM/wiki_lingua_en,en,"rephrase_en"
+GEM/wiki_lingua_en,en,"tldr_en"
+GEM/wiki_lingua_es,es,"article_summary_es"
+GEM/wiki_lingua_es,es,"write_abstract_es"
+GEM/wiki_lingua_es,es,"summarize_above_es"
+GEM/wiki_lingua_es,es,"rephrase_es"
+GEM/wiki_lingua_es,es,"tldr_es"
+GEM/wiki_lingua_fr,fr,"article_summary_fr"
+GEM/wiki_lingua_fr,fr,"write_abstract_fr"
+GEM/wiki_lingua_fr,fr,"summarize_above_fr"
+GEM/wiki_lingua_fr,fr,"rephrase_fr"
+GEM/wiki_lingua_fr,fr,"tldr_fr"
+GEM/wiki_lingua_hi,hi,"article_summary_hi"
+GEM/wiki_lingua_hi,hi,"write_abstract_hi"
+GEM/wiki_lingua_hi,hi,"summarize_above_hi"
+GEM/wiki_lingua_hi,hi,"rephrase_hi"
+GEM/wiki_lingua_hi,hi,"tldr_hi"
+GEM/wiki_lingua_id,id,"article_summary_id"
+GEM/wiki_lingua_id,id,"write_abstract_id"
+GEM/wiki_lingua_id,id,"summarize_above_id"
+GEM/wiki_lingua_id,id,"rephrase_id"
+GEM/wiki_lingua_id,id,"tldr_id"
+GEM/wiki_lingua_pt,pt,"article_summary_pt"
+GEM/wiki_lingua_pt,pt,"write_abstract_pt"
+GEM/wiki_lingua_pt,pt,"summarize_above_pt"
+GEM/wiki_lingua_pt,pt,"rephrase_pt"
+GEM/wiki_lingua_pt,pt,"tldr_pt"
+GEM/wiki_lingua_vi,vi,"article_summary_vi"
+GEM/wiki_lingua_vi,vi,"write_abstract_vi"
+GEM/wiki_lingua_vi,vi,"summarize_above_vi"
+GEM/wiki_lingua_vi,vi,"rephrase_vi"
+GEM/wiki_lingua_vi,vi,"tldr_vi"
+)
+
+#GEM/wiki_lingua_ar,ar,"article_summary_ar"
+#GEM/wiki_lingua_ar,ar,"write_abstract_ar"
+#GEM/wiki_lingua_ar,ar,"summarize_above_ar"
+#GEM/wiki_lingua_ar,ar,"rephrase_ar"
+#GEM/wiki_lingua_ar,ar,"tldr_ar"
+#GEM/wiki_lingua_zh,zh,"article_summary_zh"
+#GEM/wiki_lingua_zh,zh,"write_abstract_zh"
+#GEM/wiki_lingua_zh,zh,"summarize_above_zh"
+#GEM/wiki_lingua_zh,zh,"rephrase_zh"
+#GEM/wiki_lingua_zh,zh,"tldr_zh"
+
+DATASET_AND_CONFIG=${DATASETS_AND_CONFIGS[$SLURM_ARRAY_TASK_ID]}
+echo $ARGUMENT
+
+IFS=',' read dataset_name lang template_name <<< "${DATASET_AND_CONFIG}"
+
+# Use this fork of lm-eval: https://github.com/bigscience-workshop/lm-evaluation-harness/pull/109
+python main.py \
+    --model_api_name 'hf-causal' \
+    --model_args pretrained=$MODEL_CKPT,use_accelerate=True,tokenizer=$MODEL_CKPT,dtype=float16 \
+    --device cuda \
+    --batch_size 16 \
+    --no_tracking \
+    --task_name $dataset_name \
+    --template_names $template_name \
+    --bootstrap_iters 10
+
+echo "END TIME: $(date)"

evaluation/results/tr11/scripts/run_bsevalharness_tr11-176b-ml.slurm

@@ -0,0 +1,122 @@
+#!/bin/bash
+#SBATCH --job-name=run_bsevalharness-tr11-176b-ml
+#SBATCH --partition=gpu_p5
+#SBATCH --constraint=a100
+#SBATCH --nodes=1
+#SBATCH --ntasks-per-node=1          # crucial - only 1 task per dist per node!
+#SBATCH --cpus-per-task=64           # number of cores per tasks
+#SBATCH --hint=nomultithread         # we get physical cores not logical
+#SBATCH --gres=gpu:8                 # number of gpus
+#SBATCH --time 20:00:00              # maximum execution time (HH:MM:SS)
+#SBATCH --output=%x-%j.out           # output file name
+#SBATCH --account=six@a100
+#SBATCH --reservation=hug
+
+
+set -x -e
+
+source $six_ALL_CCFRWORK/start-muennighofflmeval
+
+echo "START TIME: $(date)"
+
+# a unique identifier for the current eval ideally correspnding to the modelname
+VARIANT="tr11-176b-ml-bsevalharness"
+
+
+CHECKPOINT_PATH=$six_ALL_CCFRSCRATCH/checkpoints/tr11-176B-ml/checkpoints/main/global_step90000
+MEGATRON_DEEPSPEED_REPO=$six_ALL_CCFRSCRATCH/commun/experiments/muennighoff/megdsbslmeval/Megatron-DeepSpeed
+export HF_DATASETS_OFFLINE=1
+export TRANSFORMERS_OFFLINE=1
+
+export TRANSFORMERS_CACHE=$six_ALL_CCFRWORK/models
+export HF_DATASETS_CACHE=$six_ALL_CCFRWORK/datasets
+export HF_MODULES_CACHE=$six_ALL_CCFRWORK/modules
+export HF_METRICS_CACHE=$six_ALL_CCFRWORK/metrics
+
+cd $MEGATRON_DEEPSPEED_REPO
+
+TOKENIZER_NAME_OR_PATH=bigscience-catalogue-data-dev/byte-level-bpe-tokenizer-no-norm-250k-whitespace-and-eos-regex-alpha-v3-dedup-lines-articles
+
+PP_SIZE=8
+TP_SIZE=1
+SEQ_LEN=2048
+
+# different from the training MICRO_BATCH_SIZE - no optim memory, so can do bigger BS
+# make as big as it can fit into gpu w/o OOM, but not too close to 100%
+EVAL_MICRO_BATCH_SIZE=1
+
+#dummy arguments to make megatron happy.
+MEGATRON_REQUIRED_ARGS=" \
+    --num-layers -1 \
+    --hidden-size -1 \
+    --num-attention-heads -1 \
+    --seq-length -1  \
+    --max-position-embeddings -1 \
+"
+
+
+ZERO_STAGE=0
+
+config_json="./ds_config.json"
+
+# Deepspeed figures out GAS dynamically from dynamic GBS via set_train_batch_size()
+cat <<EOT > $config_json
+{
+  "train_micro_batch_size_per_gpu": 1,
+  "train_batch_size": 1,
+  "gradient_clipping": 1.0,
+  "zero_optimization": {
+    "stage": $ZERO_STAGE
+  },
+  "bf16": {
+    "enabled": true
+  },
+  "steps_per_print": 2000,
+  "wall_clock_breakdown": false
+}
+EOT
+
+
+CMD="./tasks/eval_harness/evaluate_bsevalharness.py  \
+    --load $CHECKPOINT_PATH \
+    --results_path $VARIANT-results.json \
+    --tensor-model-parallel-size $TP_SIZE  \
+    --pipeline-model-parallel-size $PP_SIZE \
+    --tokenizer-type PretrainedFromHF \
+    --tokenizer-name-or-path $TOKENIZER_NAME_OR_PATH \
+    --micro-batch-size $EVAL_MICRO_BATCH_SIZE \
+    --no-load-optim \
+    --no-load-rng \
+    --bf16 \
+    --inference \
+    --seq-length $SEQ_LEN \
+    --task_list wnli \
+    --deepspeed \
+    --deepspeed_config ds_config.json \
+    --intermed_results \
+    --adaptive_seq_len \
+    --micro_bs_multiplier 16 \
+    --offloadearly \
+    $MEGATRON_REQUIRED_ARGS \
+    "
+
+GPUS_PER_NODE=8
+NNODES=$SLURM_NNODES
+MASTER_ADDR=$(scontrol show hostnames $SLURM_JOB_NODELIST | head -n 1)
+MASTER_PORT=6000
+export LAUNCHER="python -u -m torch.distributed.run \
+    --nproc_per_node $GPUS_PER_NODE \
+    --nnodes $NNODES \
+    --rdzv_endpoint $MASTER_ADDR:$MASTER_PORT \
+    --rdzv_backend c10d \
+    --max_restarts 0 \
+    --tee 3 \
+    "
+
+export CUDA_LAUNCH_BLOCKING=1
+
+echo $LAUNCHER $CMD
+
+export PYTHONPATH=$MEGATRON_DEEPSPEED_REPO
+
+$LAUNCHER $CMD 2>&1 | tee $VARIANT-eval-harness.log

evaluation/results/tr11/scripts/run_bsevalharness_tr11b-1b3-ml.slurm

@@ -0,0 +1,122 @@
+#!/bin/bash
+#SBATCH --job-name=run_bsevalharness-tr11b-1b3-ml
+#SBATCH --partition=gpu_p5
+#SBATCH --constraint=a100
+#SBATCH --nodes=1
+#SBATCH --ntasks-per-node=1          # crucial - only 1 task per dist per node!
+#SBATCH --cpus-per-task=8           # number of cores per tasks
+#SBATCH --hint=nomultithread         # we get physical cores not logical
+#SBATCH --gres=gpu:1                 # number of gpus
+#SBATCH --time 20:00:00              # maximum execution time (HH:MM:SS)
+#SBATCH --output=%x-%j.out           # output file name
+#SBATCH --account=six@a100
+#SBATCH --reservation=hug
+
+
+set -x -e
+
+source $six_ALL_CCFRWORK/start-muennighofflmeval
+
+echo "START TIME: $(date)"
+
+# a unique identifier for the current eval ideally correspnding to the modelname
+VARIANT="tr11b-1b3-ml-bsevalharness"
+
+
+CHECKPOINT_PATH=$six_ALL_CCFRSCRATCH/checkpoints/tr11b-1B3-ml/checkpoints/main/global_step340500
+MEGATRON_DEEPSPEED_REPO=$six_ALL_CCFRSCRATCH/commun/experiments/muennighoff/megdsbslmeval/Megatron-DeepSpeed
+export HF_DATASETS_OFFLINE=1
+export TRANSFORMERS_OFFLINE=1
+
+export TRANSFORMERS_CACHE=$six_ALL_CCFRWORK/models
+export HF_DATASETS_CACHE=$six_ALL_CCFRWORK/datasetseval
+export HF_MODULES_CACHE=$six_ALL_CCFRWORK/modules
+export HF_METRICS_CACHE=$six_ALL_CCFRWORK/metrics
+export TOKENIZERS_PARALLELISM=false
+
+cd $MEGATRON_DEEPSPEED_REPO
+
+TOKENIZER_NAME_OR_PATH=bigscience-catalogue-data-dev/byte-level-bpe-tokenizer-no-norm-250k-whitespace-and-eos-regex-alpha-v3-dedup-lines-articles
+
+PP_SIZE=1
+TP_SIZE=1
+SEQ_LEN=2048
+
+# different from the training MICRO_BATCH_SIZE - no optim memory, so can do bigger BS
+# make as big as it can fit into gpu w/o OOM, but not too close to 100%
+EVAL_MICRO_BATCH_SIZE=1
+
+#dummy arguments to make megatron happy.
+MEGATRON_REQUIRED_ARGS=" \
+    --num-layers -1 \
+    --hidden-size -1 \
+    --num-attention-heads -1 \
+    --seq-length -1  \
+    --max-position-embeddings -1 \
+"
+
+
+ZERO_STAGE=0
+
+config_json="./ds_config.json"
+
+# Deepspeed figures out GAS dynamically from dynamic GBS via set_train_batch_size()
+cat <<EOT > $config_json
+{
+  "train_micro_batch_size_per_gpu": 1,
+  "train_batch_size": 1,
+  "gradient_clipping": 1.0,
+  "zero_optimization": {
+    "stage": $ZERO_STAGE
+  },
+  "bf16": {
+    "enabled": false
+  },
+  "steps_per_print": 2000,
+  "wall_clock_breakdown": false
+}
+EOT
+
+
+CMD="./tasks/eval_harness/evaluate_bsevalharness.py  \
+    --load $CHECKPOINT_PATH \
+    --results_path $VARIANT-results.json \
+    --tensor-model-parallel-size $TP_SIZE  \
+    --pipeline-model-parallel-size $PP_SIZE \
+    --tokenizer-type PretrainedFromHF \
+    --tokenizer-name-or-path $TOKENIZER_NAME_OR_PATH \
+    --micro-batch-size $EVAL_MICRO_BATCH_SIZE \
+    --no-load-optim \
+    --no-load-rng \
+    --inference \
+    --seq-length $SEQ_LEN \
+    --task_list axb,axg,boolq,cb,cola,copa,crows_pairs_english,crows_pairs_french,diabla,e2e_nlg_cleaned,mnli,mnli_mismatched,multirc,piaf,qqp,rte,sst,tydiqa_primary,tydiqa_secondary,wic,wsc,wnli,wino_bias_type1_anti,wino_bias_type1_pro,wino_bias_type2_anti,wino_bias_type2_pro,xquad_ar,xquad_en,gsarti/flores_101_afr,gsarti/flores_101_amh,gsarti/flores_101_ara,gsarti/flores_101_hye,gsarti/flores_101_asm,gsarti/flores_101_ast,gsarti/flores_101_azj,gsarti/flores_101_bel,gsarti/flores_101_ben,gsarti/flores_101_bos,gsarti/flores_101_bul,gsarti/flores_101_mya,gsarti/flores_101_cat,gsarti/flores_101_ceb,gsarti/flores_101_zho_simpl,gsarti/flores_101_zho_trad,gsarti/flores_101_hrv,gsarti/flores_101_ces,gsarti/flores_101_dan,gsarti/flores_101_nld,gsarti/flores_101_eng,gsarti/flores_101_est,gsarti/flores_101_tgl,gsarti/flores_101_fin,gsarti/flores_101_fra,gsarti/flores_101_ful,gsarti/flores_101_glg,gsarti/flores_101_lug,gsarti/flores_101_kat,gsarti/flores_101_deu,gsarti/flores_101_ell,gsarti/flores_101_guj,gsarti/flores_101_hau,gsarti/flores_101_heb,gsarti/flores_101_hin,gsarti/flores_101_hun,gsarti/flores_101_isl,gsarti/flores_101_ibo,gsarti/flores_101_ind,gsarti/flores_101_gle,gsarti/flores_101_ita,gsarti/flores_101_jpn,gsarti/flores_101_jav,gsarti/flores_101_kea,gsarti/flores_101_kam,gsarti/flores_101_kan,gsarti/flores_101_kaz,gsarti/flores_101_khm,gsarti/flores_101_kor,gsarti/flores_101_kir,gsarti/flores_101_lao,gsarti/flores_101_lav,gsarti/flores_101_lin,gsarti/flores_101_lit,gsarti/flores_101_luo,gsarti/flores_101_ltz,gsarti/flores_101_mkd,gsarti/flores_101_msa,gsarti/flores_101_mal,gsarti/flores_101_mlt,gsarti/flores_101_mri,gsarti/flores_101_mar,gsarti/flores_101_mon,gsarti/flores_101_npi,gsarti/flores_101_nso,gsarti/flores_101_nob,gsarti/flores_101_nya,gsarti/flores_101_oci,gsarti/flores_101_ory,gsarti/flores_101_orm,gsarti/flores_101_pus,gsarti/flores_101_fas,gsarti/flores_101_pol,gsarti/flores_101_por,gsarti/flores_101_pan,gsarti/flores_101_ron,gsarti/flores_101_rus,gsarti/flores_101_srp,gsarti/flores_101_sna,gsarti/flores_101_snd,gsarti/flores_101_slk,gsarti/flores_101_slv,gsarti/flores_101_som,gsarti/flores_101_ckb,gsarti/flores_101_spa,gsarti/flores_101_swh,gsarti/flores_101_swe,gsarti/flores_101_tgk,gsarti/flores_101_tam,gsarti/flores_101_tel,gsarti/flores_101_tha,gsarti/flores_101_tur,gsarti/flores_101_ukr,gsarti/flores_101_umb,gsarti/flores_101_urd,gsarti/flores_101_uzb,gsarti/flores_101_vie,gsarti/flores_101_cym,gsarti/flores_101_wol,gsarti/flores_101_xho,gsarti/flores_101_yor,gsarti/flores_101_zul \
+    --eval_fp32 \
+    --deepspeed \
+    --deepspeed_config ds_config.json \
+    --intermed_results \
+    --adaptive_seq_len \
+    --micro_bs_multiplier 8 \
+    $MEGATRON_REQUIRED_ARGS \
+    "
+
+GPUS_PER_NODE=1
+NNODES=$SLURM_NNODES
+MASTER_ADDR=$(scontrol show hostnames $SLURM_JOB_NODELIST | head -n 1)
+MASTER_PORT=6000
+export LAUNCHER="python -u -m torch.distributed.run \
+    --nproc_per_node $GPUS_PER_NODE \
+    --nnodes $NNODES \
+    --rdzv_endpoint $MASTER_ADDR:$MASTER_PORT \
+    --rdzv_backend c10d \
+    --max_restarts 0 \
+    --tee 3 \
+    "
+
+export CUDA_LAUNCH_BLOCKING=1
+
+echo $LAUNCHER $CMD
+
+export PYTHONPATH=$MEGATRON_DEEPSPEED_REPO
+
+$LAUNCHER $CMD 2>&1 | tee $VARIANT-eval-harness.log

evaluation/results/tr11/scripts/run_bsevalharness_tr11d-750m-ml.slurm

@@ -0,0 +1,120 @@
+#!/bin/bash
+#SBATCH --job-name=run_bsevalharness-tr11d-760m-ml
+#SBATCH --constraint=v100-32g
+#SBATCH --nodes=1
+#SBATCH --ntasks-per-node=1          # crucial - only 1 task per dist per node!
+#SBATCH --cpus-per-task=10           # number of cores per tasks
+#SBATCH --hint=nomultithread         # we get physical cores not logical
+#SBATCH --gres=gpu:1                 # number of gpus
+#SBATCH --time 20:00:00              # maximum execution time (HH:MM:SS)
+#SBATCH --output=%x-%j.out           # output file name
+#SBATCH --account=six@v100
+
+
+set -x -e
+
+source $six_ALL_CCFRWORK/start-muennighofflmeval
+
+echo "START TIME: $(date)"
+
+# a unique identifier for the current eval ideally correspnding to the modelname
+VARIANT="tr11d-760m-ml-bsevalharness"
+
+
+CHECKPOINT_PATH=$six_ALL_CCFRSCRATCH/checkpoints/tr11d-760M-ml/checkpoints/main/global_step660750
+MEGATRON_DEEPSPEED_REPO=$six_ALL_CCFRSCRATCH/commun/experiments/muennighoff/bslmeval/Megatron-DeepSpeed
+export HF_DATASETS_OFFLINE=1
+export TRANSFORMERS_OFFLINE=1
+
+export TRANSFORMERS_CACHE=$six_ALL_CCFRWORK/models
+export HF_DATASETS_CACHE=$six_ALL_CCFRWORK/datasets
+export HF_MODULES_CACHE=$six_ALL_CCFRWORK/modules
+export HF_METRICS_CACHE=$six_ALL_CCFRWORK/metrics
+export TOKENIZERS_PARALLELISM=false
+
+cd $MEGATRON_DEEPSPEED_REPO
+
+TOKENIZER_NAME_OR_PATH=bigscience-catalogue-data-dev/byte-level-bpe-tokenizer-no-norm-250k-whitespace-and-eos-regex-alpha-v3-dedup-lines-articles
+
+PP_SIZE=1
+TP_SIZE=1
+SEQ_LEN=2048
+
+# different from the training MICRO_BATCH_SIZE - no optim memory, so can do bigger BS
+# make as big as it can fit into gpu w/o OOM, but not too close to 100%
+EVAL_MICRO_BATCH_SIZE=1
+
+#dummy arguments to make megatron happy.
+MEGATRON_REQUIRED_ARGS=" \
+    --num-layers -1 \
+    --hidden-size -1 \
+    --num-attention-heads -1 \
+    --seq-length -1  \
+    --max-position-embeddings -1 \
+"
+
+
+ZERO_STAGE=0
+
+config_json="./ds_config.json"
+
+# Deepspeed figures out GAS dynamically from dynamic GBS via set_train_batch_size()
+cat <<EOT > $config_json
+{
+  "train_micro_batch_size_per_gpu": 1,
+  "train_batch_size": 1,
+  "gradient_clipping": 1.0,
+  "zero_optimization": {
+    "stage": $ZERO_STAGE
+  },
+  "bf16": {
+    "enabled": false
+  },
+  "steps_per_print": 2000,
+  "wall_clock_breakdown": false
+}
+EOT
+
+
+CMD="./tasks/eval_harness/evaluate_bsevalharness.py  \
+    --load $CHECKPOINT_PATH \
+    --results_path $VARIANT-results.json \
+    --tensor-model-parallel-size $TP_SIZE  \
+    --pipeline-model-parallel-size $PP_SIZE \
+    --tokenizer-type PretrainedFromHF \
+    --tokenizer-name-or-path $TOKENIZER_NAME_OR_PATH \
+    --micro-batch-size $EVAL_MICRO_BATCH_SIZE \
+    --no-load-optim \
+    --no-load-rng \
+    --inference \
+    --seq-length $SEQ_LEN \
+    --task_list axb,axg,boolq,cb,cola,copa,crows_pairs_english,crows_pairs_french,diabla,e2e_nlg_cleaned,mnli,mnli_mismatched,multirc,piaf,qqp,rte,sst,tydiqa_primary,tydiqa_secondary,wic,wsc,wnli,wino_bias_type1_anti,wino_bias_type1_pro,wino_bias_type2_anti,wino_bias_type2_pro,xquad_ar,xquad_en,gsarti/flores_101_afr,gsarti/flores_101_amh,gsarti/flores_101_ara,gsarti/flores_101_hye,gsarti/flores_101_asm,gsarti/flores_101_ast,gsarti/flores_101_azj,gsarti/flores_101_bel,gsarti/flores_101_ben,gsarti/flores_101_bos,gsarti/flores_101_bul,gsarti/flores_101_mya,gsarti/flores_101_cat,gsarti/flores_101_ceb,gsarti/flores_101_zho_simpl,gsarti/flores_101_zho_trad,gsarti/flores_101_hrv,gsarti/flores_101_ces,gsarti/flores_101_dan,gsarti/flores_101_nld,gsarti/flores_101_eng,gsarti/flores_101_est,gsarti/flores_101_tgl,gsarti/flores_101_fin,gsarti/flores_101_fra,gsarti/flores_101_ful,gsarti/flores_101_glg,gsarti/flores_101_lug,gsarti/flores_101_kat,gsarti/flores_101_deu,gsarti/flores_101_ell,gsarti/flores_101_guj,gsarti/flores_101_hau,gsarti/flores_101_heb,gsarti/flores_101_hin,gsarti/flores_101_hun,gsarti/flores_101_isl,gsarti/flores_101_ibo,gsarti/flores_101_ind,gsarti/flores_101_gle,gsarti/flores_101_ita,gsarti/flores_101_jpn,gsarti/flores_101_jav,gsarti/flores_101_kea,gsarti/flores_101_kam,gsarti/flores_101_kan,gsarti/flores_101_kaz,gsarti/flores_101_khm,gsarti/flores_101_kor,gsarti/flores_101_kir,gsarti/flores_101_lao,gsarti/flores_101_lav,gsarti/flores_101_lin,gsarti/flores_101_lit,gsarti/flores_101_luo,gsarti/flores_101_ltz,gsarti/flores_101_mkd,gsarti/flores_101_msa,gsarti/flores_101_mal,gsarti/flores_101_mlt,gsarti/flores_101_mri,gsarti/flores_101_mar,gsarti/flores_101_mon,gsarti/flores_101_npi,gsarti/flores_101_nso,gsarti/flores_101_nob,gsarti/flores_101_nya,gsarti/flores_101_oci,gsarti/flores_101_ory,gsarti/flores_101_orm,gsarti/flores_101_pus,gsarti/flores_101_fas,gsarti/flores_101_pol,gsarti/flores_101_por,gsarti/flores_101_pan,gsarti/flores_101_ron,gsarti/flores_101_rus,gsarti/flores_101_srp,gsarti/flores_101_sna,gsarti/flores_101_snd,gsarti/flores_101_slk,gsarti/flores_101_slv,gsarti/flores_101_som,gsarti/flores_101_ckb,gsarti/flores_101_spa,gsarti/flores_101_swh,gsarti/flores_101_swe,gsarti/flores_101_tgk,gsarti/flores_101_tam,gsarti/flores_101_tel,gsarti/flores_101_tha,gsarti/flores_101_tur,gsarti/flores_101_ukr,gsarti/flores_101_umb,gsarti/flores_101_urd,gsarti/flores_101_uzb,gsarti/flores_101_vie,gsarti/flores_101_cym,gsarti/flores_101_wol,gsarti/flores_101_xho,gsarti/flores_101_yor,gsarti/flores_101_zul \
+    --eval_fp32 \
+    --deepspeed \
+    --deepspeed_config ds_config.json \
+    --intermed_results \
+    --adaptive_seq_len \
+    --micro_bs_multiplier 4 \
+    $MEGATRON_REQUIRED_ARGS \
+    "
+
+GPUS_PER_NODE=1
+NNODES=$SLURM_NNODES
+MASTER_ADDR=$(scontrol show hostnames $SLURM_JOB_NODELIST | head -n 1)
+MASTER_PORT=6002
+export LAUNCHER="python -u -m torch.distributed.run \
+    --nproc_per_node $GPUS_PER_NODE \
+    --nnodes $NNODES \
+    --rdzv_endpoint $MASTER_ADDR:$MASTER_PORT \
+    --rdzv_backend c10d \
+    --max_restarts 0 \
+    --tee 3 \
+    "
+
+export CUDA_LAUNCH_BLOCKING=1
+
+echo $LAUNCHER $CMD
+
+export PYTHONPATH=$MEGATRON_DEEPSPEED_REPO
+
+$LAUNCHER $CMD 2>&1 | tee $VARIANT-eval-harness.log

evaluation/results/tr11/scripts/run_trevalharness_176b.slurm

@@ -0,0 +1,60 @@
+#!/bin/bash
+#SBATCH --job-name=run_trevalharness-176b
+#SBATCH --nodes=1
+#SBATCH --ntasks-per-node=1          # crucial - only 1 task per dist per node!
+#SBATCH --cpus-per-task=64           # number of cores per tasks
+#SBATCH --hint=nomultithread         # we get physical cores not logical
+#SBATCH --gres=gpu:8                 # number of gpus
+#SBATCH --constraint=a100
+#SBATCH --reservation=hug
+#SBATCH --time 20:00:00             # maximum execution time (HH:MM:SS)
+#SBATCH --output=%x-%j.out           # output file name
+#SBATCH --account=six@a100
+
+set -x -e
+
+source $six_ALL_CCFRWORK/start-tr13f-6B3-ml-t0
+#conda activate muennighofflmevalgen
+conda activate thomas_t_zero_evaluation
+
+echo "START TIME: $(date)"
+
+# defining the right environment variables
+export TRANSFORMERS_CACHE=$six_ALL_CCFRWORK/models
+export HF_DATASETS_CACHE=$six_ALL_CCFRWORK/datasets
+export HF_MODULES_CACHE=$six_ALL_CCFRWORK/modules
+export HF_METRICS_CACHE=$six_ALL_CCFRWORK/metrics
+export HF_DATASETS_OFFLINE=1
+export TRANSFORMERS_OFFLINE=1
+export TOKENIZERS_PARALLELISM=false
+
+# Converted transformer checkpoint
+MODEL_CKPT=/gpfsscratch/rech/six/commun/uan68tv-model-conversion/bloom
+
+cd /gpfsscratch/rech/six/commun/commun/experiments/muennighoff/bslmevaltransformers/lm-evaluation-harness
+
+
+DATASETS_AND_CONFIGS=(
+arc_challenge
+arc_easy
+)
+#,arc_easy,boolq,copa,headqa,hellaswag,lambada,logiqa,mathqa,mc_taco,mrpc,multirc,openbookqa,piqa,prost,pubmedqa,qnli,qqp,race,rte,sciq,sst,triviaqa,webqs,wic,winogrande,wnli,wsc
+
+DATASET_AND_CONFIG=${DATASETS_AND_CONFIGS[$SLURM_ARRAY_TASK_ID]}
+echo $ARGUMENT
+IFS=',' read dataset_name <<< "${DATASET_AND_CONFIG}"
+
+# Use this fork of lm-eval: https://github.com/bigscience-workshop/lm-evaluation-harness/pull/109
+python main.py \
+    --model gpt2 \
+    --model_args pretrained=$MODEL_CKPT \
+    --use_accelerate \
+    --max_memory_per_gpu "50GB" \
+    --batch_size 16 \
+    --tasks $dataset_name \
+    --output_path $dataset_name.json \
+    --skip_tokenizer \
+    --no_cache \
+    --dtype=bfloat16
+
+echo "END TIME: $(date)"

evaluation/results/tr13/merge_all_json.py

@@ -0,0 +1,97 @@
+"""
+Saves a merged.json file in the provided directory
+python merge_all_json.py DIRECTORY
+"""
+
+import json
+import os
+from pathlib import Path
+import sys
+from typing import Dict
+
+
+def find_all_json(root_dir: Path):
+    if root_dir.is_file():
+        if root_dir.name.endswith(".json"):
+            return [root_dir]
+        else:
+            return []
+
+    all_jsons = []
+    for path in root_dir.iterdir():
+        all_jsons += find_all_json(path)
+    return all_jsons
+
+def sort_dict(dictionary: Dict) -> Dict:
+    results = {}
+
+    for key, value in sorted(dictionary.items(), key=lambda item: item[0]):
+        new_value = value
+
+        if isinstance(value, dict):
+            new_value = sort_dict(new_value)
+        elif isinstance(value, list):
+            new_value = sorted(value)
+
+        results[key] = new_value
+
+    return results
+
+def main():
+    # find all json file in directory
+    root_dir = Path(sys.argv[1])
+    out_path = os.path.join(root_dir, "merged.json")
+    if os.path.exists(out_path):
+        os.remove(out_path)
+
+    all_jsons = find_all_json(root_dir)
+    # merge
+    results = {}
+    for json_file in all_jsons:
+        with open(json_file, "r") as fi:
+            data = json.load(fi)
+
+        if str(json_file.name).startswith("slim"):
+            print(f"Parsing {json_file} as bigscience/lm-eval-harness file.")
+            for dic in data["results"]:
+                key = dic["task_name"]
+                # Same dataset but not really comparable
+                if "en-fr" in dic["prompt_name"]:
+                    key += "_en-fr"
+                elif "fr-en" in dic["prompt_name"]:
+                    key += "_fr-en"
+                elif "hi-en" in dic["prompt_name"]:
+                    key += "_hi-en"
+                elif "en-hi" in dic["prompt_name"]:
+                    key += "_en-hi"
+                sub_key = dic["prompt_name"]
+                results.setdefault(key, {})
+                results[key].setdefault(sub_key, {})
+                results[key][sub_key] = {
+                    **results[key][sub_key],
+                    **{subk: subv for subk, subv in dic.items() if type(subv) in [int, float]}
+                }
+        elif str(json_file.name).startswith("agg"):
+            print(f"Skipping {json_file} from bigscience/lm-eval-harness.")
+            continue
+        else:
+            print(f"Parsing {json_file} as bigscience/t-zero file.")
+            key = f"{data['dataset_name']}_{data['dataset_config_name']}"
+            if key in results:
+                assert data["template_name"] not in results
+                results[key][data["template_name"]] = data
+            else:
+                results[key] = {
+                    data["template_name"]: data
+                }
+
+    # sort
+    sorted_results = sort_dict(results)
+
+    # write
+    with open(out_path, "w") as fo:
+        json.dump(sorted_results, fo)
+
+
+if __name__ == "__main__":
+    main()

evaluation/results/tr13/plot_results.py

@@ -0,0 +1,230 @@
+import csv
+import json
+import re
+import subprocess
+from argparse import ArgumentParser
+
+import matplotlib.pyplot as plt
+from pathlib import Path
+
+import numpy as np
+
+"""
+Plot results per (dataset_name, dataset_config_name).
+"""
+
+
+def get_args():
+    parser = ArgumentParser()
+    parser.add_argument("--json_paths", nargs="+", type=str, help="Json files to plot together", required=True)
+    parser.add_argument("--t0_csv_path", type=str, help="T0 eval results path")
+    args = parser.parse_args()
+
+    return args
+
+def load_t0_results(csv_path):
+    with open(csv_path, "r") as f:
+        return list(csv.DictReader(f))
+
+def load_json(json_path):
+    with open(json_path, "r") as fi:
+        return json.load(fi)
+
+def get_experiment_name(filename: str):
+    name = re.sub(r"_([0-9]*)$", r" [\1]", filename)
+    name = name.replace("span_corruption", "SC")
+    name = re.sub(r"^enc_dec", "ED", name)
+    name = re.sub(r"^nc_dec", "NCD", name)
+    name = re.sub(r"^c_dec", 'CD', name)
+    name = name.replace("full_lm", "FLM")
+    name = name.replace("prefix_lm", "PLM")
+    name = re.sub(r"t0_adapt_([0-9]+)", r"T0(\1)", name)
+    if name[:3] == "CD_":
+        name = re.sub(r"lm_adapt_([0-9]+)", r"FLM(\1)", name)
+        name = re.sub(r"t0_adapt_nc_([0-9]+)", r"T0 AS NC (\1)", name)
+        name = re.sub(r"nc_sc_([0-9]+)", r"SC as NC(\1)", name)
+        name = re.sub(r"nc_t0_([0-9]+)", r"T0 as NC(\1)", name)
+    elif name[:4] == "NCD_" or name[:3] == "ED_":
+        if "flm_adapt" in name:
+            name = re.sub(r"flm_adapt_([0-9]+)", r"FLM AS CD(\1)", name)
+        else:
+            name = re.sub(r"lm_adapt_([0-9]+)", r"PLM(\1)", name)
+    else:
+        raise NotImplementedError
+    name = name.replace("_", " + ")
+    return name
+
+TASKS = {
+    # T0  evaluation
+    "super_glue_copa": ("COPA", 0.5),
+    "anli_dev_r1": ("ANLI R1", 1/3),
+    "anli_dev_r2": ("ANLI R2", 1/3),
+    "anli_dev_r3": ("ANLI R3", 1/3),
+    "super_glue_cb": ("CB", 1/3),
+    "super_glue_rte": ("RTE", 0.5),
+    "super_glue_wsc.fixed": ("WSC", 0.5),
+    "winogrande_winogrande_xl": ("Winogrande", 0.5),
+    "super_glue_wic": ("WiC", 0.5),
+    "hellaswag": ("HellaSwag", 0.25),
+    "story_cloze_2016": ("StoryCloze", 0.5),
+
+    # XNLI evaluation
+    "xnli_ar": ("XNLI ar (en prompts)", 1/3),
+    "xnli_bg": ("XNLI bg (en prompts)", 1/3),
+    "xnli_de": ("XNLI de (en prompts)", 1/3),
+    "xnli_el": ("XNLI el (en prompts)", 1/3),
+    "xnli_en": ("XNLI en (en prompts)", 1/3),
+    "xnli_es": ("XNLI es (en prompts)", 1/3),
+    "xnli_fr": ("XNLI fr (en prompts)", 1/3),
+    "xnli_hi": ("XNLI hi (en prompts)", 1/3),
+    "xnli_ru": ("XNLI ru (en prompts)", 1/3),
+    "xnli_sw": ("XNLI sw (en prompts)", 1/3),
+    "xnli_th": ("XNLI th (en prompts)", 1/3),
+    "xnli_tr": ("XNLI tr (en prompts)", 1/3),
+    "xnli_ur": ("XNLI ur (en prompts)", 1/3),
+    "xnli_vi": ("XNLI vi (en prompts)", 1/3),
+    "xnli_zh": ("XNLI zh (en prompts)", 1/3),
+}
+
+def plot(mtf_data, t0_data):
+    args = get_args()
+
+    assert len(TASKS) == 26
+    fig, axs = plt.subplots(3, 9, figsize=(20, 5))
+    axs = axs.flatten()
+
+    task_min_score = {}
+    task_max_score = {}
+    task_median_score = {}
+    for n, (task, (task_name, random_baseline)) in enumerate(TASKS.items()):
+        # Normalising names
+        mtf_task = task
+        t0_task = task
+        if task.startswith("anli_dev_r"):
+            t0_task = re.sub("dev_", "", task)
+        elif task == "hellaswag":
+            mtf_task = "hellaswag_None"
+
+        t5lm_scores = [float(r["score"]) for r in t0_data
+                       if r["runs"] == "xxl-lm-d4-091621"
+                       and r["dataset_name"] == t0_task
+                       and r["metric_name"] == "accuracy (Rank)"
+                       and r["score"]]
+        t0_scores = [float(r["score"]) for r in t0_data
+                     if r["runs"] == "xxl-lm-d4-091621-512"
+                     and r["dataset_name"] == t0_task
+                     and r["metric_name"] == "accuracy (Rank)"
+                     and r["score"]]
+
+        mtf_scores = [
+            (
+                name,
+                [100 * value["evaluation"]["accuracy"] for prompt, value in data[mtf_task].items()]
+                if mtf_task in data else
+                []
+            )
+            for name, data in mtf_data.items()
+        ]
+
+        all_experiment_scores_with_name = [("T5 + LM", t5lm_scores), ("T0", t0_scores), *mtf_scores]
+        # Plot
+        axs[n].axhline(100 * random_baseline, 0, len(all_experiment_scores_with_name), label="Random")
+        for i, (exp_name, scores) in enumerate(all_experiment_scores_with_name):
+            axs[n].scatter([i] * len(scores), scores, s=50, alpha=0.4, label=exp_name)
+        axs[n].set_title(task_name, fontsize=8)
+
+        # # Gather median values
+        # task_min_score[task] = [("Random", 100 * random_baseline)] + [(exp_name, np.min(scores)) for (exp_name, scores) in all_experiment_scores_with_name]
+        # task_max_score[task] = [("Random", 100 * random_baseline)] + [(exp_name, np.max(scores)) for (exp_name, scores) in all_experiment_scores_with_name]
+        # task_median_score[task] = [("Random", 100 * random_baseline)] + [(exp_name, np.median(scores)) for (exp_name, scores) in all_experiment_scores_with_name]
+
+    last_ax_id = len(TASKS) - 1
+    axs[last_ax_id].legend(bbox_to_anchor=(1, 1), loc="upper left")
+    for ax in axs[last_ax_id + 1:]:
+        ax.set_visible(False)
+
+    # if args.aggregated_results:
+    #     # ====== Plot agregated values =======
+    #     fig, axs = plt.subplots(1, 3, figsize=(20, 8))
+    #     axs = axs.flatten()
+    #     last_ax_id=0
+    #     experiment_names = [elt[0] for elt in next(iter(task_median_score.values()))]
+    #
+    #     def plot_scores_with_name(median_score_with_name, max_score, min_score, ax, title):
+    #         assert len(median_score_with_name) == len(max_score) and len(median_score_with_name) == len(min_score)
+    #         ax.axhline(
+    #             median_score_with_name[0][1],
+    #             0, len(median_score_with_name) - 1,
+    #             label=median_score_with_name[0][0]
+    #         )
+    #         for i, ((name, median_score), max_score, min_score) in enumerate(zip(median_score_with_name[1:], max_score[1:], min_score[1:])):
+    #             ax.errorbar(
+    #                 i, median_score, ((median_score - min_score,), (max_score - median_score,)),
+    #                 fmt="o", elinewidth=1, label=name)
+    #         ax.set_title(title)
+    #
+    #     def get_average_normalised_score(task_scores):
+    #         normalised_scores = []
+    #         for scores_with_name in task_scores.values():
+    #             random_name, random_baseline = scores_with_name[0]
+    #             assert random_name == "Random"
+    #             normalised_scores_per_task = [(scores - random_baseline) / (100 - random_baseline) for _, scores in
+    #                                           scores_with_name]
+    #             normalised_scores.append(normalised_scores_per_task)
+    #         return np.mean(normalised_scores, axis=0)
+    #
+    #     def get_average_score(task_scores):
+    #         return np.mean(
+    #             [[scores for _, scores in scores_with_name] for scores_with_name in task_scores.values()], axis=0)
+    #
+    #     # Plot average task score
+    #     average_task_median_score = get_average_score(task_median_score)
+    #     assert len(experiment_names) == len(average_task_median_score)
+    #     average_task_media_score_with_name = list(zip(experiment_names, average_task_median_score))
+    #     del average_task_median_score
+    #     plot_scores_with_name(
+    #         median_score_with_name=average_task_media_score_with_name,
+    #         max_score=get_average_score(task_max_score),
+    #         min_score=get_average_score(task_min_score),
+    #         ax=axs[last_ax_id],
+    #         title=f"Average of task median scores"
+    #     )
+    #     last_ax_id += 1
+    #
+    #     # Plot average of task median normalised scores `normalised_score = (score - random) / (1 - random)`
+    #     average_task_normalised_median_score = get_average_normalised_score(task_median_score)
+    #     assert len(experiment_names) == len(average_task_normalised_median_score)
+    #     average_task_normalised_median_score_with_name = list(
+    #         zip(experiment_names, average_task_normalised_median_score))
+    #     del average_task_normalised_median_score
+    #     plot_scores_with_name(
+    #         median_score_with_name=average_task_normalised_median_score_with_name,
+    #         max_score=get_average_normalised_score(task_max_score),
+    #         min_score=get_average_normalised_score(task_min_score),
+    #         ax=axs[last_ax_id],
+    #         title=f"Average of task normalised median scores"
+    #     )
+    #     last_ax_id += 1
+    #
+    #     axs[last_ax_id -1].legend(bbox_to_anchor=(1, 1), loc="upper left")
+    #     for ax in axs[last_ax_id:]:
+    #         ax.set_visible(False)
+
+
+def main():
+    args = get_args()
+
+    # Load results
+    t0_data = load_t0_results(args.t0_csv_path)
+    mtf_data = {
+        re.sub(".json", "", json_path): load_json(json_path)
+        for json_path in args.json_paths
+    }
+
+    plot(mtf_data, t0_data)
+
+    plt.show()
+    print("Finished")
+
+if __name__ == "__main__":
+    main()

evaluation/results/tr13/results_to_csv.py

@@ -0,0 +1,72 @@
+#!/usr/bin/env python
+
+# this script converts results.json:
+#
+#   "results": {
+#     "arc_challenge": {
+#       "acc": 0.24232081911262798,
+#       "acc_stderr": 0.01252159329580012,
+#       "acc_norm": 0.2764505119453925,
+#       "acc_norm_stderr": 0.013069662474252425
+#     },
+#
+# into a format expected by a spreadsheet, which is:
+#
+#   task          metric   value    err
+#   arc_challenge acc      xxx      yyy
+#   arc_challenge acc_norm xxx      yyy
+#   arc_challenge f1       xxx      yyy
+#
+# usage:
+# report-to-csv.py results.json
+
+
+import sys
+import statistics
+import json
+import io
+import csv
+
+results_file = sys.argv[1]
+
+csv_file = results_file.replace("json", "csv")
+
+print(f"Converting {results_file} to {csv_file}")
+
+with io.open(results_file, 'r', encoding='utf-8') as f:
+    raw_results = json.load(f)
+
+results = {}
+for ds_name, v in sorted(raw_results.items()):
+    results[ds_name.split("/")[-1]] = v
+
+with io.open(csv_file, 'w', encoding='utf-8') as f:
+
+    writer = csv.writer(f)
+    writer.writerow(["dataset", "prompt", "metric", "value"])
+    medians = []
+    for ds_name, v in sorted(results.items()):
+        acc_scores, bleu_scores, rouge2_fmeasure = [], [], []
+        for prompt_name, res in sorted(v.items()):
+            # T0 Eval
+            if "evaluation" in res:
+                for metric, value in sorted(res["evaluation"].items()):
+                    writer.writerow([ds_name, prompt_name, metric, value])
+                    if metric == "accuracy":
+                        acc_scores.append(value)
+            # LM Eval Harness Generation
+            elif "bleu" in res:
+                # Make sure BLEU is 0-1 not 0-100
+                writer.writerow([ds_name, prompt_name, "bleu", res["bleu"] / 100])
+                bleu_scores.append(res["bleu"] / 100)
+
+        if acc_scores:
+            median = statistics.median(acc_scores)
+            medians.append(median)
+            writer.writerow([ds_name, "median", "accuracy", median])
+        elif bleu_scores:
+            median = statistics.median(bleu_scores)
+            medians.append(median)
+            writer.writerow([ds_name, "median", "bleu", median])
+    if medians:
+        writer.writerow(["multiple", "average", "multiple", statistics.mean(medians)])

evaluation/results/tr13/tzeroeval/evaluate_t0_v100.slurm

@@ -0,0 +1,751 @@
+#!/bin/bash
+#SBATCH --job-name=evaluate_t0
+#SBATCH --constraint=v100-32g
+#SBATCH --nodes=1
+#SBATCH --ntasks-per-node=1          # crucial - only 1 task per dist per node!
+#SBATCH --cpus-per-task=10           # number of cores per tasks
+#SBATCH --hint=nomultithread         # we get physical cores not logical
+#SBATCH --gres=gpu:1                 # number of gpus
+#SBATCH --time 20:00:00              # maximum execution time (HH:MM:SS)
+#SBATCH --output=%x-%j.out           # output file name
+#SBATCH --account=six@v100
+#SBATCH --array=0-164
+
+# VALIDATION:
+# --array=0-168
+
+# L1
+# --array=0-169
+
+# L2
+# --array=0-84
+
+# MT L1
+# --array=0-69
+
+# MT L2
+# --array=0-89
+
+# XNLIMTHT:
+# --array=0-79
+
+set -x -e
+
+source $six_ALL_CCFRWORK/start-py38-pt111
+conda activate thomas_t_zero_evaluation
+
+CHECKPOINT_PATH=/gpfsscratch/rech/six/commun/experiments/muennighoff/bloomckpt/350m/bloom-560m
+
+WORKDIR=/gpfswork/rech/six/commun/code/tr13f-6B3-ml-t0
+
+pushd $WORKDIR
+
+OUTPUT_DIR=$CHECKPOINT_PATH/evaluation
+mkdir -p $OUTPUT_DIR
+
+# Validation
+DATASETS_AND_CONFIGS_VAL=(
+head_qa,en,en,"multiple_choice_q_and_a_index_with_context_en",validation
+head_qa,en,en,"multiple_choice_q_and_a_en",validation
+head_qa,en,en,"multiple_choice_q_and_a_index_en",validation
+head_qa,en,en,"multiple_choice_a_and_q_with_context_en",validation
+head_qa,en,en,"multiple_choice_a_and_q_en",validation
+head_qa,es,en,"multiple_choice_q_and_a_index_with_context_en",validation
+head_qa,es,en,"multiple_choice_q_and_a_en",validation
+head_qa,es,en,"multiple_choice_q_and_a_index_en",validation
+head_qa,es,en,"multiple_choice_a_and_q_with_context_en",validation
+head_qa,es,en,"multiple_choice_a_and_q_en",validation
+climate_fever,None,None,"first_evidence_and_claim_itemization",test
+climate_fever,None,None,"claim_and_all_supporting_evidences",test
+climate_fever,None,None,"fifth_evidence_and_claim_itemization",test
+climate_fever,None,None,"third_evidence_claim_pair",test
+climate_fever,None,None,"second_evidence_and_claim_itemization",test
+codah,codah,None,"interrogative_instruction_after_sentence_and_choices",train
+codah,codah,None,"affirmative_instruction_before_sentence_and_choices",train
+codah,codah,None,"affirmative_instruction_after_sentence_and_choices",train
+aqua_rat,raw,None,"select_the_best_option",validation
+aqua_rat,raw,None,"answer_quiz",validation
+aqua_rat,raw,None,"Answer questions from options",validation
+commonsense_qa,None,None,"answer_given_question_without_options",validation
+commonsense_qa,None,None,"question_answering",validation
+commonsense_qa,None,None,"most_suitable_answer",validation
+amazon_reviews_multi,en,en,"prompt_title_to_star",validation
+amazon_reviews_multi,en,en,"prompt_review_to_star",validation
+amazon_reviews_multi,en,en,"prompt_body_title_to_star",validation
+amazon_reviews_multi,zh,en,"prompt_title_to_star",validation
+amazon_reviews_multi,zh,en,"prompt_review_to_star",validation
+amazon_reviews_multi,zh,en,"prompt_body_title_to_star",validation
+amazon_reviews_multi,fr,en,"prompt_title_to_star",validation
+amazon_reviews_multi,fr,en,"prompt_review_to_star",validation
+amazon_reviews_multi,fr,en,"prompt_body_title_to_star",validation
+amazon_reviews_multi,es,en,"prompt_title_to_star",validation
+amazon_reviews_multi,es,en,"prompt_review_to_star",validation
+amazon_reviews_multi,es,en,"prompt_body_title_to_star",validation
+art,None,None,"choose_hypothesis_options",validation
+art,None,None,"choose_hypothesis_believable",validation
+art,None,None,"choose_hypothesis",validation
+art,None,None,"choose_hypothesis_desc",validation
+art,None,None,"choose_hypothesis_likely",validation
+banking77,None,None,"help_page_topic",test
+banking77,None,None,"direct_to_which_department",test
+banking77,None,None,"rephrase_as_banking_term",test
+blbooksgenre,title_genre_classifiction,None,"multi-choice",train
+blbooksgenre,title_genre_classifiction,None,"premise_context_first",train
+blbooksgenre,title_genre_classifiction,None,"classify",train
+blimp,adjunct_island,None,"grammatical_between_1_2",train
+blimp,adjunct_island,None,"grammatical_between_A_B",train
+blimp,adjunct_island,None,"grammatical_which_one_1_2",train
+blimp,adjunct_island,None,"single_sentence_bad_yes_no",train
+blimp,adjunct_island,None,"single_sentence_good_yes_no",train
+conv_ai_3,None,None,"clarification_needed",validation
+conv_ai_3,None,None,"score_give_number",validation
+conv_ai_3,None,None,"ambiguous",validation
+conv_ai_3,None,None,"directly_answer",validation
+conv_ai_3,None,None,"score_how_much",validation
+craigslist_bargains,None,None,"good deal for seller no list price implicit",validation
+craigslist_bargains,None,None,"good deal for seller no list price",validation
+craigslist_bargains,None,None,"good deal for seller",validation
+craigslist_bargains,None,None,"best deal",validation
+ecthr_cases,alleged-violation-prediction,None,"implicit_advice_number",validation
+ecthr_cases,alleged-violation-prediction,None,"ecthr_alleged_articles_declaration_at_end",validation
+ecthr_cases,alleged-violation-prediction,None,"ecthr_alleged_articles_question_at_start",validation
+ecthr_cases,alleged-violation-prediction,None,"implicit_judgment_paragraph",validation
+ecthr_cases,alleged-violation-prediction,None,"confirm number of violated articles",validation
+emo,None,None,"persons_describe",validation
+emo,None,None,"final_message",validation
+emo,None,None,"what_emotion_do_you_think",validation
+emo,None,None,"emotional_state",validation
+emo,None,None,"dialogue_between",validation
+emotion,None,None,"choose_the_best_emotion_label",test
+emotion,None,None,"reply_with_emoation_label",test
+emotion,None,None,"answer_with_class_label",test
+emotion,None,None,"answer_question_with_emotion_label",test
+financial_phrasebank,sentences_allagree,None,"share_price_option",train
+financial_phrasebank,sentences_allagree,None,"sentiment",train
+financial_phrasebank,sentences_allagree,None,"word_comes_to_mind",train
+financial_phrasebank,sentences_allagree,None,"complementary_industries",train
+financial_phrasebank,sentences_allagree,None,"bullish_neutral_bearish",train
+glue,cola,None,"Make sense yes no",validation
+glue,cola,None,"is_this_correct",validation
+glue,cola,None,"editing",validation
+glue,cola,None,"Following sentence acceptable",validation
+glue,cola,None,"Previous sentence acceptable",validation
+glue,sst2,None,"positive negative after",validation
+glue,sst2,None,"review",validation
+glue,sst2,None,"said",validation
+glue,sst2,None,"following positive negative",validation
+glue,sst2,None,"happy or mad",validation
+health_fact,None,None,"claim_veracity_classification_after_reading_I_believe",validation
+health_fact,None,None,"claim_explanation_classification",validation
+health_fact,None,None,"claim_veracity_classification_tell_me",validation
+hlgd,None,None,"is_same_event_with_time_interrogative_related",validation
+hlgd,None,None,"is_same_event_interrogative_talk",validation
+hlgd,None,None,"is_same_event_with_time_interrogative_talk",validation
+hlgd,None,None,"is_same_event_refer",validation
+hlgd,None,None,"is_same_event_editor_asks",validation
+hyperpartisan_news_detection,byarticle,None,"consider_does_it_follow_a_hyperpartisan_argumentation",train
+hyperpartisan_news_detection,byarticle,None,"follows_hyperpartisan_argumentation",train
+hyperpartisan_news_detection,byarticle,None,"consume_with_caution",train
+hyperpartisan_news_detection,byarticle,None,"extreme_left_wing_or_right_wing",train
+hyperpartisan_news_detection,byarticle,None,"consider_it_exhibits_extreme_one_sidedness",train
+liar,None,None,"Given statement guess category",validation
+lince,sa_spaeng,None,"original poster expressed sentiment",validation
+lince,sa_spaeng,None,"sentiment trying to express",validation
+lince,sa_spaeng,None,"express sentiment",validation
+lince,sa_spaeng,None,"negation template",validation
+lince,sa_spaeng,None,"the author seem",validation
+math_qa,None,None,"choose_correct_og",test
+math_qa,None,None,"pick_the_correct",test
+math_qa,None,None,"first_choice_then_problem",test
+math_qa,None,None,"problem_set_type",test
+math_qa,None,None,"gre_problem",test
+movie_rationales,None,None,"Standard binary sentiment analysis",validation
+movie_rationales,None,None,"Evidences sentiment classification",validation
+movie_rationales,None,None,"Evidences + review",validation
+movie_rationales,None,None,"Generate evidences and sentiment",validation
+mwsc,None,None,"in-the-sentence-question-first",validation
+mwsc,None,None,"what-think",validation
+mwsc,None,None,"in-the-sentence",validation
+mwsc,None,None,"options-or",validation
+mwsc,None,None,"is-correct",validation
+poem_sentiment,None,None,"positive_or_negative_sentiment_variation_2",validation
+poem_sentiment,None,None,"question_answer_format",validation
+poem_sentiment,None,None,"guess_sentiment_without_options_variation_1",validation
+poem_sentiment,None,None,"positive_or_negative_sentiment_variation_1",validation
+poem_sentiment,None,None,"most_appropriate_sentiment",validation
+onestop_english,None,None,"esl_context",train
+onestop_english,None,None,"ara_context",train
+onestop_english,None,None,"determine_reading_level_from_the_first_three_sentences",train
+onestop_english,None,None,"esl_variation",train
+onestop_english,None,None,"assess",train
+pubmed_qa,pqa_labeled,None,"Long Answer to Final Decision",train
+pubmed_qa,pqa_labeled,None,"Question Answering (Short)",train
+riddle_sense,None,None,"most_suitable_answer",validation
+riddle_sense,None,None,"answer_given_question_without_options",validation
+riddle_sense,None,None,"question_to_answer_index",validation
+riddle_sense,None,None,"question_answering",validation
+scicite,None,None,"Classify intent w/section (select choice)",validation
+scicite,None,None,"Classify intent (choices first)",validation
+scicite,None,None,"Classify intent (select choice)",validation
+scicite,None,None,"Classify intent",validation
+scicite,None,None,"can_describe",validation
+selqa,answer_selection_analysis,None,"is-he-talking-about",validation
+selqa,answer_selection_analysis,None,"would-make-sense-qu-rand",validation
+selqa,answer_selection_analysis,None,"make-sense-rand",validation
+selqa,answer_selection_analysis,None,"which-answer-1st-vs-random",validation
+snips_built_in_intents,None,None,"voice_intent",train
+snips_built_in_intents,None,None,"categorize_query",train
+snips_built_in_intents,None,None,"intent_query",train
+snips_built_in_intents,None,None,"categorize_query_brief",train
+snips_built_in_intents,None,None,"query_intent",train
+)
+
+DATASETS_AND_CONFIGS_L1=(
+super_glue,copa,None,"best_option",validation
+super_glue,copa,None,"C1 or C2? premise, so/because…",validation
+super_glue,copa,None,"i_am_hesitating",validation
+super_glue,copa,None,"cause_effect",validation
+super_glue,copa,None,"plausible_alternatives",validation
+super_glue,rte,None,"MNLI crowdsource",validation
+super_glue,rte,None,"GPT-3 style",validation
+super_glue,rte,None,"does it follow that",validation
+super_glue,rte,None,"should assume",validation
+super_glue,rte,None,"guaranteed true",validation
+anli,dev_r1,None,"guaranteed/possible/impossible",dev_r1
+anli,dev_r1,None,"MNLI crowdsource",dev_r1
+anli,dev_r1,None,"GPT-3 style",dev_r1
+anli,dev_r1,None,"justified in saying",dev_r1
+anli,dev_r1,None,"can we infer",dev_r1
+anli,dev_r2,None,"guaranteed/possible/impossible",dev_r2
+anli,dev_r2,None,"MNLI crowdsource",dev_r2
+anli,dev_r2,None,"GPT-3 style",dev_r2
+anli,dev_r2,None,"justified in saying",dev_r2
+anli,dev_r2,None,"can we infer",dev_r2
+anli,dev_r3,None,"guaranteed/possible/impossible",dev_r3
+anli,dev_r3,None,"MNLI crowdsource",dev_r3
+anli,dev_r3,None,"GPT-3 style",dev_r3
+anli,dev_r3,None,"justified in saying",dev_r3
+anli,dev_r3,None,"can we infer",dev_r3
+super_glue,cb,None,"guaranteed/possible/impossible",validation
+super_glue,cb,None,"MNLI crowdsource",validation
+super_glue,cb,None,"GPT-3 style",validation
+super_glue,cb,None,"justified in saying",validation
+super_glue,cb,None,"can we infer",validation
+winogrande,winogrande_xl,None,"underscore refer to",validation
+winogrande,winogrande_xl,None,"Replace",validation
+winogrande,winogrande_xl,None,"stand for",validation
+winogrande,winogrande_xl,None,"does underscore refer to",validation
+winogrande,winogrande_xl,None,"True or False",validation
+story_cloze,2016,None,"Story Continuation and Options",validation
+story_cloze,2016,None,"Answer Given options",validation
+story_cloze,2016,None,"Novel Correct Ending",validation
+story_cloze,2016,None,"Generate Ending",validation
+story_cloze,2016,None,"Choose Story Ending",validation
+Muennighoff/xstory_cloze,ar,en,"Story Continuation and Options",validation
+Muennighoff/xstory_cloze,ar,en,"Answer Given options",validation
+Muennighoff/xstory_cloze,ar,en,"Novel Correct Ending",validation
+Muennighoff/xstory_cloze,ar,en,"Generate Ending",validation
+Muennighoff/xstory_cloze,ar,en,"Choose Story Ending",validation
+Muennighoff/xstory_cloze,es,en,"Story Continuation and Options",validation
+Muennighoff/xstory_cloze,es,en,"Answer Given options",validation
+Muennighoff/xstory_cloze,es,en,"Novel Correct Ending",validation
+Muennighoff/xstory_cloze,es,en,"Generate Ending",validation
+Muennighoff/xstory_cloze,es,en,"Choose Story Ending",validation
+Muennighoff/xstory_cloze,eu,en,"Story Continuation and Options",validation
+Muennighoff/xstory_cloze,eu,en,"Answer Given options",validation
+Muennighoff/xstory_cloze,eu,en,"Novel Correct Ending",validation
+Muennighoff/xstory_cloze,eu,en,"Generate Ending",validation
+Muennighoff/xstory_cloze,eu,en,"Choose Story Ending",validation
+Muennighoff/xstory_cloze,id,en,"Story Continuation and Options",validation
+Muennighoff/xstory_cloze,id,en,"Answer Given options",validation
+Muennighoff/xstory_cloze,id,en,"Novel Correct Ending",validation
+Muennighoff/xstory_cloze,id,en,"Generate Ending",validation
+Muennighoff/xstory_cloze,id,en,"Choose Story Ending",validation
+Muennighoff/xstory_cloze,hi,en,"Story Continuation and Options",validation
+Muennighoff/xstory_cloze,hi,en,"Answer Given options",validation
+Muennighoff/xstory_cloze,hi,en,"Novel Correct Ending",validation
+Muennighoff/xstory_cloze,hi,en,"Generate Ending",validation
+Muennighoff/xstory_cloze,hi,en,"Choose Story Ending",validation
+Muennighoff/xstory_cloze,sw,en,"Story Continuation and Options",validation
+Muennighoff/xstory_cloze,sw,en,"Answer Given options",validation
+Muennighoff/xstory_cloze,sw,en,"Novel Correct Ending",validation
+Muennighoff/xstory_cloze,sw,en,"Generate Ending",validation
+Muennighoff/xstory_cloze,sw,en,"Choose Story Ending",validation
+Muennighoff/xstory_cloze,te,en,"Story Continuation and Options",validation
+Muennighoff/xstory_cloze,te,en,"Answer Given options",validation
+Muennighoff/xstory_cloze,te,en,"Novel Correct Ending",validation
+Muennighoff/xstory_cloze,te,en,"Generate Ending",validation
+Muennighoff/xstory_cloze,te,en,"Choose Story Ending",validation
+Muennighoff/xstory_cloze,zh,en,"Story Continuation and Options",validation
+Muennighoff/xstory_cloze,zh,en,"Answer Given options",validation
+Muennighoff/xstory_cloze,zh,en,"Novel Correct Ending",validation
+Muennighoff/xstory_cloze,zh,en,"Generate Ending",validation
+Muennighoff/xstory_cloze,zh,en,"Choose Story Ending",validation
+xnli,ar,en,"guaranteed/possible/impossible",validation
+xnli,ar,en,"MNLI crowdsource",validation
+xnli,ar,en,"GPT-3 style",validation
+xnli,ar,en,"justified in saying",validation
+xnli,ar,en,"can we infer",validation
+xnli,en,en,"guaranteed/possible/impossible",validation
+xnli,en,en,"MNLI crowdsource",validation
+xnli,en,en,"GPT-3 style",validation
+xnli,en,en,"justified in saying",validation
+xnli,en,en,"can we infer",validation
+xnli,es,en,"guaranteed/possible/impossible",validation
+xnli,es,en,"MNLI crowdsource",validation
+xnli,es,en,"GPT-3 style",validation
+xnli,es,en,"justified in saying",validation
+xnli,es,en,"can we infer",validation
+xnli,fr,en,"guaranteed/possible/impossible",validation
+xnli,fr,en,"MNLI crowdsource",validation
+xnli,fr,en,"GPT-3 style",validation
+xnli,fr,en,"justified in saying",validation
+xnli,fr,en,"can we infer",validation
+xnli,hi,en,"guaranteed/possible/impossible",validation
+xnli,hi,en,"MNLI crowdsource",validation
+xnli,hi,en,"GPT-3 style",validation
+xnli,hi,en,"justified in saying",validation
+xnli,hi,en,"can we infer",validation
+xnli,sw,en,"guaranteed/possible/impossible",validation
+xnli,sw,en,"MNLI crowdsource",validation
+xnli,sw,en,"GPT-3 style",validation
+xnli,sw,en,"justified in saying",validation
+xnli,sw,en,"can we infer",validation
+xnli,ur,en,"guaranteed/possible/impossible",validation
+xnli,ur,en,"MNLI crowdsource",validation
+xnli,ur,en,"GPT-3 style",validation
+xnli,ur,en,"justified in saying",validation
+xnli,ur,en,"can we infer",validation
+xnli,vi,en,"guaranteed/possible/impossible",validation
+xnli,vi,en,"MNLI crowdsource",validation
+xnli,vi,en,"GPT-3 style",validation
+xnli,vi,en,"justified in saying",validation
+xnli,vi,en,"can we infer",validation
+xnli,zh,en,"guaranteed/possible/impossible",validation
+xnli,zh,en,"MNLI crowdsource",validation
+xnli,zh,en,"GPT-3 style",validation
+xnli,zh,en,"justified in saying",validation
+xnli,zh,en,"can we infer",validation
+xcopa,id,en,"best_option",validation
+xcopa,id,en,"C1 or C2? premise, so/because…",validation
+xcopa,id,en,"i_am_hesitating",validation
+xcopa,id,en,"cause_effect",validation
+xcopa,id,en,"plausible_alternatives",validation
+xcopa,sw,en,"best_option",validation
+xcopa,sw,en,"C1 or C2? premise, so/because…",validation
+xcopa,sw,en,"i_am_hesitating",validation
+xcopa,sw,en,"cause_effect",validation
+xcopa,sw,en,"plausible_alternatives",validation
+xcopa,ta,en,"best_option",validation
+xcopa,ta,en,"C1 or C2? premise, so/because…",validation
+xcopa,ta,en,"i_am_hesitating",validation
+xcopa,ta,en,"cause_effect",validation
+xcopa,ta,en,"plausible_alternatives",validation
+xcopa,vi,en,"best_option",validation
+xcopa,vi,en,"C1 or C2? premise, so/because…",validation
+xcopa,vi,en,"i_am_hesitating",validation
+xcopa,vi,en,"cause_effect",validation
+xcopa,vi,en,"plausible_alternatives",validation
+xcopa,zh,en,"best_option",validation
+xcopa,zh,en,"C1 or C2? premise, so/because…",validation
+xcopa,zh,en,"i_am_hesitating",validation
+xcopa,zh,en,"cause_effect",validation
+xcopa,zh,en,"plausible_alternatives",validation
+Muennighoff/xwinograd,en,en,"underscore refer to",test
+Muennighoff/xwinograd,en,en,"Replace",test
+Muennighoff/xwinograd,en,en,"stand for",test
+Muennighoff/xwinograd,en,en,"does underscore refer to",test
+Muennighoff/xwinograd,en,en,"True or False",test
+Muennighoff/xwinograd,fr,en,"underscore refer to",test
+Muennighoff/xwinograd,fr,en,"Replace",test
+Muennighoff/xwinograd,fr,en,"stand for",test
+Muennighoff/xwinograd,fr,en,"does underscore refer to",test
+Muennighoff/xwinograd,fr,en,"True or False",test
+Muennighoff/xwinograd,pt,en,"underscore refer to",test
+Muennighoff/xwinograd,pt,en,"Replace",test
+Muennighoff/xwinograd,pt,en,"stand for",test
+Muennighoff/xwinograd,pt,en,"does underscore refer to",test
+Muennighoff/xwinograd,pt,en,"True or False",test
+Muennighoff/xwinograd,zh,en,"underscore refer to",test
+Muennighoff/xwinograd,zh,en,"Replace",test
+Muennighoff/xwinograd,zh,en,"stand for",test
+Muennighoff/xwinograd,zh,en,"does underscore refer to",test
+Muennighoff/xwinograd,zh,en,"True or False",test
+)
+
+DATASETS_AND_CONFIGS_L2=(
+Muennighoff/xstory_cloze,ru,en,"Story Continuation and Options",validation
+Muennighoff/xstory_cloze,ru,en,"Answer Given options",validation
+Muennighoff/xstory_cloze,ru,en,"Novel Correct Ending",validation
+Muennighoff/xstory_cloze,ru,en,"Generate Ending",validation
+Muennighoff/xstory_cloze,ru,en,"Choose Story Ending",validation
+Muennighoff/xstory_cloze,my,en,"Story Continuation and Options",validation
+Muennighoff/xstory_cloze,my,en,"Answer Given options",validation
+Muennighoff/xstory_cloze,my,en,"Novel Correct Ending",validation
+Muennighoff/xstory_cloze,my,en,"Generate Ending",validation
+Muennighoff/xstory_cloze,my,en,"Choose Story Ending",validation
+xnli,bg,en,"guaranteed/possible/impossible",validation
+xnli,bg,en,"MNLI crowdsource",validation
+xnli,bg,en,"GPT-3 style",validation
+xnli,bg,en,"justified in saying",validation
+xnli,bg,en,"can we infer",validation
+xnli,de,en,"guaranteed/possible/impossible",validation
+xnli,de,en,"MNLI crowdsource",validation
+xnli,de,en,"GPT-3 style",validation
+xnli,de,en,"justified in saying",validation
+xnli,de,en,"can we infer",validation
+xnli,el,en,"guaranteed/possible/impossible",validation
+xnli,el,en,"MNLI crowdsource",validation
+xnli,el,en,"GPT-3 style",validation
+xnli,el,en,"justified in saying",validation
+xnli,el,en,"can we infer",validation
+xnli,ru,en,"guaranteed/possible/impossible",validation
+xnli,ru,en,"MNLI crowdsource",validation
+xnli,ru,en,"GPT-3 style",validation
+xnli,ru,en,"justified in saying",validation
+xnli,ru,en,"can we infer",validation
+xnli,th,en,"guaranteed/possible/impossible",validation
+xnli,th,en,"MNLI crowdsource",validation
+xnli,th,en,"GPT-3 style",validation
+xnli,th,en,"justified in saying",validation
+xnli,th,en,"can we infer",validation
+xnli,tr,en,"guaranteed/possible/impossible",validation
+xnli,tr,en,"MNLI crowdsource",validation
+xnli,tr,en,"GPT-3 style",validation
+xnli,tr,en,"justified in saying",validation
+xnli,tr,en,"can we infer",validation
+Muennighoff/xwinograd,ru,en,"underscore refer to",test
+Muennighoff/xwinograd,ru,en,"Replace",test
+Muennighoff/xwinograd,ru,en,"stand for",test
+Muennighoff/xwinograd,ru,en,"does underscore refer to",test
+Muennighoff/xwinograd,ru,en,"True or False",test
+Muennighoff/xwinograd,jp,en,"underscore refer to",test
+Muennighoff/xwinograd,jp,en,"Replace",test
+Muennighoff/xwinograd,jp,en,"stand for",test
+Muennighoff/xwinograd,jp,en,"does underscore refer to",test
+Muennighoff/xwinograd,jp,en,"True or False",test
+xcopa,et,en,"best_option",validation
+xcopa,et,en,"C1 or C2? premise, so/because…",validation
+xcopa,et,en,"i_am_hesitating",validation
+xcopa,et,en,"cause_effect",validation
+xcopa,et,en,"plausible_alternatives",validation
+xcopa,ht,en,"best_option",validation
+xcopa,ht,en,"C1 or C2? premise, so/because…",validation
+xcopa,ht,en,"i_am_hesitating",validation
+xcopa,ht,en,"cause_effect",validation
+xcopa,ht,en,"plausible_alternatives",validation
+xcopa,it,en,"best_option",validation
+xcopa,it,en,"C1 or C2? premise, so/because…",validation
+xcopa,it,en,"i_am_hesitating",validation
+xcopa,it,en,"cause_effect",validation
+xcopa,it,en,"plausible_alternatives",validation
+xcopa,qu,en,"best_option",validation
+xcopa,qu,en,"C1 or C2? premise, so/because…",validation
+xcopa,qu,en,"i_am_hesitating",validation
+xcopa,qu,en,"cause_effect",validation
+xcopa,qu,en,"plausible_alternatives",validation
+xcopa,th,en,"best_option",validation
+xcopa,th,en,"C1 or C2? premise, so/because…",validation
+xcopa,th,en,"i_am_hesitating",validation
+xcopa,th,en,"cause_effect",validation
+xcopa,th,en,"plausible_alternatives",validation
+xcopa,tr,en,"best_option",validation
+xcopa,tr,en,"C1 or C2? premise, so/because…",validation
+xcopa,tr,en,"i_am_hesitating",validation
+xcopa,tr,en,"cause_effect",validation
+xcopa,tr,en,"plausible_alternatives",validation
+)
+
+DATASETS_AND_CONFIGS_MT_L1=(
+Muennighoff/xstory_cloze,ar,ar,"Story Continuation and Options_armt",validation
+Muennighoff/xstory_cloze,ar,ar,"Answer Given options_armt",validation
+Muennighoff/xstory_cloze,ar,ar,"Novel Correct Ending_armt",validation
+Muennighoff/xstory_cloze,ar,ar,"Generate Ending_armt",validation
+Muennighoff/xstory_cloze,ar,ar,"Choose Story Ending_armt",validation
+Muennighoff/xstory_cloze,es,es,"Story Continuation and Options_esmt",validation
+Muennighoff/xstory_cloze,es,es,"Answer Given options_esmt",validation
+Muennighoff/xstory_cloze,es,es,"Novel Correct Ending_esmt",validation
+Muennighoff/xstory_cloze,es,es,"Generate Ending_esmt",validation
+Muennighoff/xstory_cloze,es,es,"Choose Story Ending_esmt",validation
+Muennighoff/xstory_cloze,eu,eu,"Story Continuation and Options_eumt",validation
+Muennighoff/xstory_cloze,eu,eu,"Answer Given options_eumt",validation
+Muennighoff/xstory_cloze,eu,eu,"Novel Correct Ending_eumt",validation
+Muennighoff/xstory_cloze,eu,eu,"Generate Ending_eumt",validation
+Muennighoff/xstory_cloze,eu,eu,"Choose Story Ending_eumt",validation
+Muennighoff/xstory_cloze,id,id,"Story Continuation and Options_idmt",validation
+Muennighoff/xstory_cloze,id,id,"Answer Given options_idmt",validation
+Muennighoff/xstory_cloze,id,id,"Novel Correct Ending_idmt",validation
+Muennighoff/xstory_cloze,id,id,"Generate Ending_idmt",validation
+Muennighoff/xstory_cloze,id,id,"Choose Story Ending_idmt",validation
+Muennighoff/xstory_cloze,hi,hi,"Story Continuation and Options_himt",validation
+Muennighoff/xstory_cloze,hi,hi,"Answer Given options_himt",validation
+Muennighoff/xstory_cloze,hi,hi,"Novel Correct Ending_himt",validation
+Muennighoff/xstory_cloze,hi,hi,"Generate Ending_himt",validation
+Muennighoff/xstory_cloze,hi,hi,"Choose Story Ending_himt",validation
+Muennighoff/xstory_cloze,sw,sw,"Story Continuation and Options_swmt",validation
+Muennighoff/xstory_cloze,sw,sw,"Answer Given options_swmt",validation
+Muennighoff/xstory_cloze,sw,sw,"Novel Correct Ending_swmt",validation
+Muennighoff/xstory_cloze,sw,sw,"Generate Ending_swmt",validation
+Muennighoff/xstory_cloze,sw,sw,"Choose Story Ending_swmt",validation
+Muennighoff/xstory_cloze,te,te,"Story Continuation and Options_temt",validation
+Muennighoff/xstory_cloze,te,te,"Answer Given options_temt",validation
+Muennighoff/xstory_cloze,te,te,"Novel Correct Ending_temt",validation
+Muennighoff/xstory_cloze,te,te,"Generate Ending_temt",validation
+Muennighoff/xstory_cloze,te,te,"Choose Story Ending_temt",validation
+Muennighoff/xstory_cloze,zh,zh,"Story Continuation and Options_zhmt",validation
+Muennighoff/xstory_cloze,zh,zh,"Answer Given options_zhmt",validation
+Muennighoff/xstory_cloze,zh,zh,"Novel Correct Ending_zhmt",validation
+Muennighoff/xstory_cloze,zh,zh,"Generate Ending_zhmt",validation
+Muennighoff/xstory_cloze,zh,zh,"Choose Story Ending_zhmt",validation
+Muennighoff/xwinograd,fr,fr,"underscore refer to_frmt",test
+Muennighoff/xwinograd,fr,fr,"Replace_frmt",test
+Muennighoff/xwinograd,fr,fr,"stand for_frmt",test
+Muennighoff/xwinograd,fr,fr,"does underscore refer to_frmt",test
+Muennighoff/xwinograd,fr,fr,"True or False_frmt",test
+Muennighoff/xwinograd,pt,pt,"underscore refer to_ptmt",test
+Muennighoff/xwinograd,pt,pt,"Replace_ptmt",test
+Muennighoff/xwinograd,pt,pt,"stand for_ptmt",test
+Muennighoff/xwinograd,pt,pt,"does underscore refer to_ptmt",test
+Muennighoff/xwinograd,pt,pt,"True or False_ptmt",test
+Muennighoff/xwinograd,zh,zh,"underscore refer to_zhmt",test
+Muennighoff/xwinograd,zh,zh,"Replace_zhmt",test
+Muennighoff/xwinograd,zh,zh,"stand for_zhmt",test
+Muennighoff/xwinograd,zh,zh,"does underscore refer to_zhmt",test
+Muennighoff/xwinograd,zh,zh,"True or False_zhmt",test
+xcopa,id,id,"best_option_idmt",validation
+xcopa,id,id,"C1 or C2? premise_idmt",validation
+xcopa,id,id,"i_am_hesitating_idmt",validation
+xcopa,id,id,"cause_effect_idmt",validation
+xcopa,id,id,"plausible_alternatives_idmt",validation
+xcopa,sw,sw,"best_option_swmt",validation
+xcopa,sw,sw,"C1 or C2? premise_swmt",validation
+xcopa,sw,sw,"i_am_hesitating_swmt",validation
+xcopa,sw,sw,"cause_effect_swmt",validation
+xcopa,sw,sw,"plausible_alternatives_swmt",validation
+xcopa,ta,ta,"best_option_tamt",validation
+xcopa,ta,ta,"C1 or C2? premise_tamt",validation
+xcopa,ta,ta,"i_am_hesitating_tamt",validation
+xcopa,ta,ta,"cause_effect_tamt",validation
+xcopa,ta,ta,"plausible_alternatives_tamt",validation
+xcopa,vi,vi,"best_option_vimt",validation
+xcopa,vi,vi,"C1 or C2? premise_vimt",validation
+xcopa,vi,vi,"i_am_hesitating_vimt",validation
+xcopa,vi,vi,"cause_effect_vimt",validation
+xcopa,vi,vi,"plausible_alternatives_vimt",validation
+xcopa,zh,zh,"best_option_zhmt",validation
+xcopa,zh,zh,"C1 or C2? premise_zhmt",validation
+xcopa,zh,zh,"i_am_hesitating_zhmt",validation
+xcopa,zh,zh,"cause_effect_zhmt",validation
+xcopa,zh,zh,"plausible_alternatives_zhmt",validation   
+)
+
+DATASETS_AND_CONFIGS_ZHHT=(
+Muennighoff/xstory_cloze,zh,zh,"Story Continuation and Options_zhht",validation
+Muennighoff/xstory_cloze,zh,zh,"Answer Given options_zhht",validation
+Muennighoff/xstory_cloze,zh,zh,"Novel Correct Ending_zhht",validation
+Muennighoff/xstory_cloze,zh,zh,"Generate Ending_zhht",validation
+Muennighoff/xstory_cloze,zh,zh,"Choose Story Ending_zhht",validation
+Muennighoff/xwinograd,zh,zh,"underscore refer to_zhht",test
+Muennighoff/xwinograd,zh,zh,"Replace_zhht",test
+Muennighoff/xwinograd,zh,zh,"stand for_zhht",test
+Muennighoff/xwinograd,zh,zh,"does underscore refer to_zhht",test
+Muennighoff/xwinograd,zh,zh,"True or False_zhht",test
+xcopa,zh,zh,"best_option_zhht",validation
+xcopa,zh,zh,"C1 or C2? premise_zhht",validation
+xcopa,zh,zh,"i_am_hesitating_zhht",validation
+xcopa,zh,zh,"cause_effect_zhht",validation
+xcopa,zh,zh,"plausible_alternatives_zhht",validation  
+)
+
+DATASETS_AND_CONFIGS_XNLIHTMT=(
+xnli,ar,ar,"guaranteed/possible/impossible_arht",validation
+xnli,ar,ar,"MNLI crowdsource_arht",validation
+xnli,ar,ar,"GPT-3 style_arht",validation
+xnli,ar,ar,"justified in saying_arht",validation
+xnli,ar,ar,"can we infer_arht",validation
+xnli,ar,ar,"guaranteed/possible/impossible_armt",validation
+xnli,ar,ar,"MNLI crowdsource_armt",validation
+xnli,ar,ar,"GPT-3 style_armt",validation
+xnli,ar,ar,"justified in saying_armt",validation
+xnli,ar,ar,"can we infer_armt",validation
+xnli,es,es,"guaranteed/possible/impossible_esht",validation
+xnli,es,es,"MNLI crowdsource_esht",validation
+xnli,es,es,"GPT-3 style_esht",validation
+xnli,es,es,"justified in saying_esht",validation
+xnli,es,es,"can we infer_esht",validation
+xnli,es,es,"guaranteed/possible/impossible_esmt",validation
+xnli,es,es,"MNLI crowdsource_esmt",validation
+xnli,es,es,"GPT-3 style_esmt",validation
+xnli,es,es,"justified in saying_esmt",validation
+xnli,es,es,"can we infer_esmt",validation
+xnli,fr,fr,"guaranteed/possible/impossible_frht",validation
+xnli,fr,fr,"MNLI crowdsource_frht",validation
+xnli,fr,fr,"GPT-3 style_frht",validation
+xnli,fr,fr,"justified in saying_frht",validation
+xnli,fr,fr,"can we infer_frht",validation
+xnli,fr,fr,"guaranteed/possible/impossible_frmt",validation
+xnli,fr,fr,"MNLI crowdsource_frmt",validation
+xnli,fr,fr,"GPT-3 style_frmt",validation
+xnli,fr,fr,"justified in saying_frmt",validation
+xnli,fr,fr,"can we infer_frmt",validation
+xnli,hi,hi,"guaranteed/possible/impossible_hiht",validation
+xnli,hi,hi,"MNLI crowdsource_hiht",validation
+xnli,hi,hi,"GPT-3 style_hiht",validation
+xnli,hi,hi,"justified in saying_hiht",validation
+xnli,hi,hi,"can we infer_hiht",validation
+xnli,hi,hi,"guaranteed/possible/impossible_himt",validation
+xnli,hi,hi,"MNLI crowdsource_himt",validation
+xnli,hi,hi,"GPT-3 style_himt",validation
+xnli,hi,hi,"justified in saying_himt",validation
+xnli,hi,hi,"can we infer_himt",validation
+xnli,ur,ur,"guaranteed/possible/impossible_urht",validation
+xnli,ur,ur,"MNLI crowdsource_urht",validation
+xnli,ur,ur,"GPT-3 style_urht",validation
+xnli,ur,ur,"justified in saying_urht",validation
+xnli,ur,ur,"can we infer_urht",validation
+xnli,ur,ur,"guaranteed/possible/impossible_urmt",validation
+xnli,ur,ur,"MNLI crowdsource_urmt",validation
+xnli,ur,ur,"GPT-3 style_urmt",validation
+xnli,ur,ur,"justified in saying_urmt",validation
+xnli,ur,ur,"can we infer_urmt",validation
+xnli,sw,sw,"guaranteed/possible/impossible_swht",validation
+xnli,sw,sw,"MNLI crowdsource_swht",validation
+xnli,sw,sw,"GPT-3 style_swht",validation
+xnli,sw,sw,"justified in saying_swht",validation
+xnli,sw,sw,"can we infer_swht",validation
+xnli,sw,sw,"guaranteed/possible/impossible_swmt",validation
+xnli,sw,sw,"MNLI crowdsource_swmt",validation
+xnli,sw,sw,"GPT-3 style_swmt",validation
+xnli,sw,sw,"justified in saying_swmt",validation
+xnli,sw,sw,"can we infer_swmt",validation
+xnli,vi,vi,"guaranteed/possible/impossible_viht",validation
+xnli,vi,vi,"MNLI crowdsource_viht",validation
+xnli,vi,vi,"GPT-3 style_viht",validation
+xnli,vi,vi,"justified in saying_viht",validation
+xnli,vi,vi,"can we infer_viht",validation
+xnli,vi,vi,"guaranteed/possible/impossible_vimt",validation
+xnli,vi,vi,"MNLI crowdsource_vimt",validation
+xnli,vi,vi,"GPT-3 style_vimt",validation
+xnli,vi,vi,"justified in saying_vimt",validation
+xnli,vi,vi,"can we infer_vimt",validation
+xnli,zh,zh,"guaranteed/possible/impossible_zhht",validation
+xnli,zh,zh,"MNLI crowdsource_zhht",validation
+xnli,zh,zh,"GPT-3 style_zhht",validation
+xnli,zh,zh,"justified in saying_zhht",validation
+xnli,zh,zh,"can we infer_zhht",validation
+xnli,zh,zh,"guaranteed/possible/impossible_zhmt",validation
+xnli,zh,zh,"MNLI crowdsource_zhmt",validation
+xnli,zh,zh,"GPT-3 style_zhmt",validation
+xnli,zh,zh,"justified in saying_zhmt",validation
+xnli,zh,zh,"can we infer_zhmt",validation
+)
+
+DATASETS_AND_CONFIGS_MT_L2=(
+Muennighoff/xstory_cloze,my,my,"Story Continuation and Options_mymt",validation
+Muennighoff/xstory_cloze,my,my,"Answer Given options_mymt",validation
+Muennighoff/xstory_cloze,my,my,"Novel Correct Ending_mymt",validation
+Muennighoff/xstory_cloze,my,my,"Generate Ending_mymt",validation
+Muennighoff/xstory_cloze,my,my,"Choose Story Ending_mymt",validation
+Muennighoff/xstory_cloze,ru,ru,"Story Continuation and Options_rumt",validation
+Muennighoff/xstory_cloze,ru,ru,"Answer Given options_rumt",validation
+Muennighoff/xstory_cloze,ru,ru,"Novel Correct Ending_rumt",validation
+Muennighoff/xstory_cloze,ru,ru,"Generate Ending_rumt",validation
+Muennighoff/xstory_cloze,ru,ru,"Choose Story Ending_rumt",validation
+Muennighoff/xstory_cloze,sw,sw,"Story Continuation and Options_swmt",validation
+Muennighoff/xstory_cloze,sw,sw,"Answer Given options_swmt",validation
+Muennighoff/xstory_cloze,sw,sw,"Novel Correct Ending_swmt",validation
+Muennighoff/xstory_cloze,sw,sw,"Generate Ending_swmt",validation
+Muennighoff/xstory_cloze,sw,sw,"Choose Story Ending_swmt",validation
+Muennighoff/xstory_cloze,te,te,"Story Continuation and Options_temt",validation
+Muennighoff/xstory_cloze,te,te,"Answer Given options_temt",validation
+Muennighoff/xstory_cloze,te,te,"Novel Correct Ending_temt",validation
+Muennighoff/xstory_cloze,te,te,"Generate Ending_temt",validation
+Muennighoff/xstory_cloze,te,te,"Choose Story Ending_temt",validation
+Muennighoff/xwinograd,jp,jp,"underscore refer to_jpmt",test
+Muennighoff/xwinograd,jp,jp,"Replace_jpmt",test
+Muennighoff/xwinograd,jp,jp,"stand for_jpmt",test
+Muennighoff/xwinograd,jp,jp,"does underscore refer to_jpmt",test
+Muennighoff/xwinograd,jp,jp,"True or False_jpmt",test
+Muennighoff/xwinograd,ru,ru,"underscore refer to_rumt",test
+Muennighoff/xwinograd,ru,ru,"Replace_rumt",test
+Muennighoff/xwinograd,ru,ru,"stand for_rumt",test
+Muennighoff/xwinograd,ru,ru,"does underscore refer to_rumt",test
+Muennighoff/xwinograd,ru,ru,"True or False_rumt",test
+xcopa,et,et,"best_option_etmt",validation
+xcopa,et,et,"C1 or C2? premise_etmt",validation
+xcopa,et,et,"i_am_hesitating_etmt",validation
+xcopa,et,et,"cause_effect_etmt",validation
+xcopa,et,et,"plausible_alternatives_etmt",validation
+xcopa,ht,ht,"best_option_htmt",validation
+xcopa,ht,ht,"C1 or C2? premise_htmt",validation
+xcopa,ht,ht,"i_am_hesitating_htmt",validation
+xcopa,ht,ht,"cause_effect_htmt",validation
+xcopa,ht,ht,"plausible_alternatives_htmt",validation
+xcopa,it,it,"best_option_itmt",validation
+xcopa,it,it,"C1 or C2? premise_itmt",validation
+xcopa,it,it,"i_am_hesitating_itmt",validation
+xcopa,it,it,"cause_effect_itmt",validation
+xcopa,it,it,"plausible_alternatives_itmt",validation
+xcopa,qu,qu,"best_option_qumt",validation
+xcopa,qu,qu,"C1 or C2? premise_qumt",validation
+xcopa,qu,qu,"i_am_hesitating_qumt",validation
+xcopa,qu,qu,"cause_effect_qumt",validation
+xcopa,qu,qu,"plausible_alternatives_qumt",validation
+xcopa,th,th,"best_option_thmt",validation
+xcopa,th,th,"C1 or C2? premise_thmt",validation
+xcopa,th,th,"i_am_hesitating_thmt",validation
+xcopa,th,th,"cause_effect_thmt",validation
+xcopa,th,th,"plausible_alternatives_thmt",validation
+xcopa,tr,tr,"best_option_trmt",validation
+xcopa,tr,tr,"C1 or C2? premise_trmt",validation
+xcopa,tr,tr,"i_am_hesitating_trmt",validation
+xcopa,tr,tr,"cause_effect_trmt",validation
+xcopa,tr,tr,"plausible_alternatives_trmt",validation
+xnli,bg,bg,"guaranteed/possible/impossible_bgmt",validation
+xnli,bg,bg,"MNLI crowdsource_bgmt",validation
+xnli,bg,bg,"GPT-3 style_bgmt",validation
+xnli,bg,bg,"justified in saying_bgmt",validation
+xnli,bg,bg,"can we infer_bgmt",validation
+xnli,de,de,"guaranteed/possible/impossible_demt",validation
+xnli,de,de,"MNLI crowdsource_demt",validation
+xnli,de,de,"GPT-3 style_demt",validation
+xnli,de,de,"justified in saying_demt",validation
+xnli,de,de,"can we infer_demt",validation
+xnli,el,el,"guaranteed/possible/impossible_elmt",validation
+xnli,el,el,"MNLI crowdsource_elmt",validation
+xnli,el,el,"GPT-3 style_elmt",validation
+xnli,el,el,"justified in saying_elmt",validation
+xnli,el,el,"can we infer_elmt",validation
+xnli,ru,ru,"guaranteed/possible/impossible_rumt",validation
+xnli,ru,ru,"MNLI crowdsource_rumt",validation
+xnli,ru,ru,"GPT-3 style_rumt",validation
+xnli,ru,ru,"justified in saying_rumt",validation
+xnli,ru,ru,"can we infer_rumt",validation
+xnli,th,th,"guaranteed/possible/impossible_thmt",validation
+xnli,th,th,"MNLI crowdsource_thmt",validation
+xnli,th,th,"GPT-3 style_thmt",validation
+xnli,th,th,"justified in saying_thmt",validation
+xnli,th,th,"can we infer_thmt",validation
+xnli,tr,tr,"guaranteed/possible/impossible_trmt",validation
+xnli,tr,tr,"MNLI crowdsource_trmt",validation
+xnli,tr,tr,"GPT-3 style_trmt",validation
+xnli,tr,tr,"justified in saying_trmt",validation
+xnli,tr,tr,"can we infer_trmt",validation
+)
+
+DATASET_AND_CONFIG=${DATASETS_AND_CONFIGS_L1[$SLURM_ARRAY_TASK_ID]}
+echo $ARGUMENT
+
+# Run T0 evaluation
+# For PrefixLM add --prefixlm
+IFS=',' read dataset_name dataset_config_name template_config_name template_name <<< "${DATASET_AND_CONFIG}"
+python t-zero/evaluation/run_eval.py \
+        --dataset_name $dataset_name \
+        --dataset_config_name $dataset_config_name \
+        --template_config_name $template_config_name \
+        --template_name "$template_name" \
+        --model_name_or_path $CHECKPOINT_PATH \
+        --output_dir $OUTPUT_DIR \
+        --per_device_eval_batch_size 8 \
+        --max_length 2048 \
+        --dtype float16

evaluation/results/tr3/README.md

@@ -0,0 +1 @@
+We're interested in understanding when zero shot capabilities appear.

evaluation/results/tr3/plot_task_solve_graph.py

@@ -0,0 +1,133 @@
+import json
+import os
+from argparse import ArgumentParser
+
+import numpy as np
+from matplotlib import pyplot as plt
+
+
+def get_args():
+    parser = ArgumentParser()
+    parser.add_argument('--input-files', type=lambda s: s.split(','), required=True, help='Input file that hold all evaluation metrics')
+    return parser.parse_args()
+
+# TODO: fill it up
+RANDOM_BASELINE={
+    "arc_challenge_acc": 0.2502, # Source: https://arxiv.org/pdf/1803.05457.pdf table 6
+    "arc_easy_acc": 0.2502, # Source: https://arxiv.org/pdf/1803.05457.pdf table 6
+    "boolq_acc": 0.5,
+    "copa_acc": 0.5,
+    "headqa_acc": 0.25, # TODO: That's a pain as some have 4, some have 5 and nobody reports random baseline
+    "hellaswag_acc": 0.25,
+    "lambada_acc": 0., # Safe to say that random models won't perform well at all.
+    "logiqa_acc": 0.25,
+    "mathqa_acc": 0.25, # TODO: That's a pain as some have 4, some have 5 and nobody reports random baseline
+    "mrpc_acc": 0.5,
+    "multirc_acc": 0., # TODO: I couldn't figure it out
+    "openbookqa_acc": 0.25,
+    "piqa_acc": 0.5,
+    "prost_acc": 0.25,
+    "pubmedqa_acc": 1/3,
+    "qnli_acc": 0.5,
+    "qqp_acc": 0.5,
+    "race_acc": 0.25, # Source: https://arxiv.org/pdf/1704.04683.pdf table 5
+    "rte_acc": 0.5,
+    "sciq_acc": 0.25,
+    "sst_acc": 0.5,
+    "triviaqa_acc": 0.,
+    "webqs_acc": 0.,
+    "wic_acc": 0.5,
+    "winogrande_acc": 0.5,
+    "wnli_acc": 0.5,
+    "wsc_acc": 0.5
+}
+def normalise_scores(scores_per_task):
+    normalised_scores = {}
+    for key,value in scores_per_task.items():
+        # We assume it exists, otherwise we need to figure out what the random baseline is
+        normalised_scores[key] = (value - RANDOM_BASELINE[key]) / (1. - RANDOM_BASELINE[key])
+    # TODO: we need to substract the random baseline.
+    return scores_per_task
+
+def main():
+    args = get_args()
+
+    final = {}
+    for input_file in args.input_files:
+        assert os.path.basename(input_file).endswith("_agg.json")
+        experiment_name = os.path.basename(input_file).split("_agg.json")[0]
+        with open(input_file, "r") as fi:
+            final[experiment_name] = json.load(fi)
+
+    # We search for matching tokens
+    matching_tokens = set(next(iter(final.values()))["tokens"])
+    for experiment_name, experiment in final.items():
+        tokens = experiment["tokens"]
+        matching_tokens = matching_tokens & set(tokens)
+        # Make sure we don't override existing data
+        assert "token2checkpoint_step" not in experiment
+        experiment["token2checkpoint_step"] = {token: ckpt_step for token, ckpt_step in zip(tokens, experiment["checkpoints"])}
+        # Make sure we don't override existing data
+        assert "token2id" not in experiment
+        experiment["token2id"] = {token: _id for _id, token in enumerate(tokens)}
+    matching_tokens = sorted(matching_tokens)
+    print(f"Plotting only for tokens in {matching_tokens}")
+
+    plots_per_keys = {}
+
+    for token in matching_tokens:
+        for experiment_name, experiment in final.items():
+            _id = experiment["token2id"][token]
+            scores_per_task = {
+                "Average_acc": {
+                    f"{evaluation_name}_{metric_name}": metric[_id]
+                    for evaluation_name, evaluation in experiment["results"].items()
+                    for metric_name, metric in evaluation.items()
+                    if metric_name == "acc"
+                },
+                # "Average": {
+                #     metric_name: values[i]
+                #     for evaluation_name in final["results"][experiment_name]
+                #     for metric_name, values in final["results"][experiment_name][evaluation_name].items()
+                #     if metric_name[-7:] != "_stderr"
+                # }
+            }
+
+            # Build plot graphs
+            for key in scores_per_task:
+                if key not in plots_per_keys:
+                    plots_per_keys[key] = {}
+
+                plot_per_token = plots_per_keys[key]
+                if token in plot_per_token:
+                    continue
+
+                plot = plt.figure()
+                plot = plot.add_subplot(1, 1, 1)
+                plot.set_title(f"{key} - Number of tokens seen: {token}")
+                plot_per_token[token] = plot
+
+            # Plot per steps
+            for key in plots_per_keys:
+                scores = scores_per_task[key]
+                plot = plots_per_keys[key][token]
+
+                # Normalize score
+                normalised_scores = normalise_scores(scores)
+
+                # Sort scores, we order them from smalles to biggest
+                sorted_scores = sorted(normalised_scores.values())
+
+                # Compute the number of task over that sorted_scores.
+                y = np.arange(len(sorted_scores), 0, -1) / len(sorted_scores)
+
+                plot.step(x=sorted_scores, y=y, label=experiment_name)
+
+    for plots in plots_per_keys.values():
+        assert len(plots) == len(matching_tokens)
+        for plot in plots.values():
+            plot.legend()
+    plt.show()
+
+if __name__ == "__main__":
+    main()

evaluation/results/tr3/switch_tokenizer_to_t5_for_tr3e.sh

@@ -0,0 +1,6 @@
+export GIT_LFS_SKIP_SMUDGE=1
+git clone https://huggingface.co/bigscience/tr3e-1B3-c4-checkpoints
+cd tr3e-1B3-c4-checkpoints
+$six_ALL_CCFRWORK/code/bigscience/tools/hub-sync.py --repo-path . --patterns '*bogus*'
+git branch -a | sort -V | perl -lne 'm|(global_step\d+)| && print qx[git checkout $1; perl -pi -e "s|\\"tokenizer_class\\": null|\\"tokenizer_class\\": \\"T5Tokenizer\\"|" config.json; git commit -m "Fix tokenizer_class to use T5 tokenizer" .; git push --set-upstream origin $1]'
+export GIT_LFS_SKIP_SMUDGE=0

evaluation/results/tr3/tr3e-1B3-c4-checkpoints_agg.json

@@ -0,0 +1,3084 @@
+{
+  "tokens": [
+    10044178432,
+    11617042432,
+    14762770432,
+    16335634432,
+    17908498432,
+    21054226432,
+    22627090432,
+    25772818432,
+    30491410432,
+    35210002432,
+    36782866432,
+    41501458432,
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+    49365778432,
+    50938642432,
+    54084370432,
+    55657234432,
+    57230098432,
+    65094418432,
+    66667282432,
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+    99697426432,
+    101270290432,
+    105988882432,
+    110707474432,
+    112280338432
+  ],
+  "checkpoints": [
+    19500,
+    21000,
+    24000,
+    25500,
+    27000,
+    30000,
+    31500,
+    34500,
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+    43500,
+    45000,
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+    54000,
+    57000,
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+    93000,
+    97500,
+    103500,
+    105000,
+    106500,
+    111000,
+    115500,
+    117000
+  ],
+  "results": {
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+}

evaluation/utilities/convert_results_to_json.py

@@ -0,0 +1,111 @@
+import json
+import math
+import os
+from argparse import ArgumentParser
+from os import listdir
+from os.path import isfile
+
+def get_args():
+    parser = ArgumentParser()
+    # --experiments tr3d-1B3-oscar-checkpoints,tr3e-1B3-c4-checkpoints,tr3m-1B3-pile-checkpoints
+    parser.add_argument('--experiment', type=str, required=True,
+                        help='Experiment we want to download.')
+    parser.add_argument('--result-dir', type=str, required=True,
+                        help='Result directory containing all results, and to store aggregated json results.')
+    parser.add_argument('--batch-size', type=int, default=512,
+                        help='Experiment training batch size.')
+    parser.add_argument('--sequence_length', type=int, default=2048,
+                        help='Experiment training sequence length.')
+    parser.add_argument('--rampup-batch-size', type=lambda s: tuple(int(item) for item in s.split(',')), default=(32, 32, 2_000_000),
+                        help='Experiment training batch size rampup.')
+    return parser.parse_args()
+
+def checkpoint_step_to_tokens(checkpoint_step, args) -> int:
+    def fn(checkpoint_step) -> int:
+        if not hasattr(checkpoint_step_to_tokens, "CACHE"):
+            checkpoint_step_to_tokens.CACHE = {}
+
+        BATCH_SIZE=args.batch_size
+        SEQUENCE_LENGTH=args.sequence_length
+        # Linear increase in terms of samples.
+        RAMPUP_BATCH_SIZE = args.rampup_batch_size
+
+        # Compute RAMPUP checkpoint_step
+        if not hasattr(checkpoint_step_to_tokens, "RAMPUP_OFFSET"):
+            initial_batch_size, increment_batch_size, sample_limit_for_rampup = RAMPUP_BATCH_SIZE
+            number_of_increments = (BATCH_SIZE - initial_batch_size) // increment_batch_size
+            assert (BATCH_SIZE - initial_batch_size) % increment_batch_size == 0
+
+            offset_step = 0
+            start_sample = 0
+            for incr in range(number_of_increments):
+                batch_size = initial_batch_size + incr * increment_batch_size
+                end_sample = int(math.ceil((incr + 1) * sample_limit_for_rampup / number_of_increments))
+                number_of_step_per_increment = int(math.ceil((end_sample - start_sample) / batch_size))
+                checkpoint_step_to_tokens.CACHE.update({
+                    offset_step + i: (start_sample + i * batch_size) * SEQUENCE_LENGTH
+                    for i in range(number_of_step_per_increment)
+                })
+                offset_step += number_of_step_per_increment
+                start_sample += number_of_step_per_increment * batch_size
+
+            checkpoint_step_to_tokens.CACHE[offset_step] = start_sample * SEQUENCE_LENGTH
+            checkpoint_step_to_tokens.RAMPUP_OFFSET = offset_step
+
+        if checkpoint_step in checkpoint_step_to_tokens.CACHE:
+            return checkpoint_step_to_tokens.CACHE[checkpoint_step]
+
+        number_steps_after_rampup = checkpoint_step - checkpoint_step_to_tokens.RAMPUP_OFFSET
+        assert number_steps_after_rampup >= 0
+
+        slope = BATCH_SIZE * SEQUENCE_LENGTH
+
+        checkpoint_step_to_tokens.CACHE[checkpoint_step] = \
+            checkpoint_step_to_tokens.CACHE[checkpoint_step_to_tokens.RAMPUP_OFFSET] + \
+            slope * number_steps_after_rampup
+        return checkpoint_step_to_tokens.CACHE[checkpoint_step]
+    return fn(checkpoint_step)
+
+def main():
+    args = get_args()
+    result_dir = args.result_dir
+    experiment = args.experiment
+
+    results_file_per_checkpoint = [
+        file
+        for file in listdir(result_dir)
+        if isfile(os.path.join(result_dir, file)) and file.startswith(experiment)
+    ]
+    checkpoint_steps = sorted([int(file.split("_")[-1].split(".json")[0]) for file in results_file_per_checkpoint])
+    absolute_paths = [f"{result_dir}/{experiment}_{checkpoint_step}.json" for checkpoint_step in checkpoint_steps]
+    # format = "{EXPERIMENT_NAME}_{CHECKPOINT_STEP}.json"
+    tokens = [checkpoint_step_to_tokens(checkpoint_step, args) for checkpoint_step in checkpoint_steps]
+
+    result_json = {}
+    for absolute_path in absolute_paths:
+        with open(absolute_path, 'r') as fi:
+            results = json.load(fi)["results"]
+
+        for task in results:
+            if task not in result_json:
+                result_json[task] = {}
+
+            for metric in results[task]:
+                if metric not in result_json[task]:
+                    result_json[task][metric] = []
+
+                result_json[task][metric].append(results[task][metric])
+
+    # check
+    for task in result_json:
+        assert len(tokens) == len(checkpoint_steps)
+        for metric in result_json[task]:
+            assert len(result_json[task][metric]) == len(checkpoint_steps)
+
+    output_path = os.path.join(result_dir, f"{experiment}_agg.json")
+    print(f"Printing results to {output_path}")
+    with open(output_path, 'w') as fo:
+        json.dump({"tokens": tokens, "checkpoints": checkpoint_steps, "results": result_json}, fo, indent=2)
+
+if __name__ == "__main__":
+    main()

evaluation/utilities/download_all_models.py

@@ -0,0 +1,47 @@
+from argparse import ArgumentParser
+from multiprocessing import Pool
+
+from requests import HTTPError
+from transformers import AutoModel, AutoTokenizer
+
+def get_args():
+    parser = ArgumentParser()
+    # --experiments bigscience/tr3d-1B3-oscar-checkpoints,bigscience/tr3e-1B3-c4-checkpoints,bigscience/tr3m-1B3-pile-checkpoints
+    parser.add_argument('--experiments', type=lambda s: s.split(','), required=True, help='Experiments we want to download.')
+    # --steps 19500,28500,37500,48000,57000,66000,76500,85500,94500,105000,114000
+    parser.add_argument('--steps', type=lambda s: [int(item) for item in s.split(',')], required=True, help='Steps we should download the model checkpoints')
+    return parser.parse_args()
+
+def _load_model(pretrain:str, revision: str):
+    try:
+        AutoModel.from_pretrained(pretrain, revision=revision)
+        AutoTokenizer.from_pretrained(pretrain, revision=revision)
+        return f"Loaded: {{pretrain:{pretrain}, revision:{revision}}}"
+    except HTTPError:
+        return f"Failed to load: {{pretrain:{pretrain}, revision:{revision}}}"
+
+def load_model(kwargs):
+    return _load_model(**kwargs)
+
+def main():
+    args = get_args()
+    pretrains = args.experiments
+    steps = args.steps
+    revisions = [f"global_step{step}" for step in steps]
+
+    # with Pool(10) as pool:
+    #     results = pool.imap(
+    #         load_model,
+    #         [{"pretrain": pretrain, "revision": revision} for pretrain in pretrains for revision in revisions],
+    #         chunksize=1
+    #     )
+    #
+    #     for result in results:
+    #         print(result)
+
+
+    for kwargs in [{"pretrain": pretrain, "revision": revision} for pretrain in pretrains for revision in revisions]:
+        print(load_model(kwargs))
+
+if __name__ == "__main__":
+    main()

evaluation/utilities/download_all_models.slurm

@@ -0,0 +1,26 @@
+#!/bin/bash
+#SBATCH --job-name=download_all_models
+#SBATCH --nodes=1
+#SBATCH --ntasks-per-node=1          # crucial - only 1 task per dist per node!
+#SBATCH --cpus-per-task=10           # number of cores per tasks
+#SBATCH --hint=nomultithread         # we get physical cores not logical
+#SBATCH --time 10:00:00              # maximum execution time (HH:MM:SS)
+#SBATCH --output=logs/%x.out           # output file name
+#SBATCH --account=six@gpu
+#SBATCH --partition=compil
+
+set -x -e
+
+source $six_ALL_CCFRWORK/start-prod
+conda activate thomas_lm_eval
+
+# TODO: replace with local fork of bigscience
+BIGSCIENCE_REPO=$WORK/code/big_science/bigscience/evaluation/results/tr3
+
+pushd $BIGSCIENCE_REPO
+
+# TODO: replace with experiment / steps
+EXPERIMENTS=bigscience/tr3d-1B3-oscar-checkpoints,bigscience/tr3e-1B3-c4-checkpoints,bigscience/tr3m-1B3-pile-checkpoints
+STEPS=$(python -c "print(\",\".join([str(i) for i in range(19500, 118500, 1500)]))")
+
+python download_all_models.py --experiments $EXPERIMENTS --steps $STEPS

evaluation/utilities/export_results_through_training_to_wandb.py

@@ -0,0 +1,86 @@
+import os
+
+import numpy as np
+import wandb
+import json
+import argparse
+
+RANDOM_BASELINE={
+    "arc_challenge": 0.2502, # Source: https://arxiv.org/pdf/1803.05457.pdf table 6
+    "arc_easy": 0.2502, # Source: https://arxiv.org/pdf/1803.05457.pdf table 6
+    "boolq": 0.5,
+    "copa": 0.5,
+    "headqa_en": 0.25,
+    "hellaswag": 0.25,
+    "lambada": 0., # Safe to say that random models won't perform well at all.
+    "logiqa": 0.25,
+    "mathqa": (4360 * 1/ 5 - (4475 - 4360) * 1/ 4) / 4475,
+    "mrpc": 0.5,
+    "multirc": 0., # TODO: I couldn't figure it out
+    "openbookqa": 0.25,
+    "piqa": 0.5,
+    "prost": 0.25,
+    "pubmedqa": 1/3,
+    "qnli": 0.5,
+    "qqp": 0.5,
+    "race": 0.25, # Source: https://arxiv.org/pdf/1704.04683.pdf table 5
+    "rte": 0.5,
+    "sciq": 0.25,
+    "sst": 0.5,
+    "triviaqa": 0.,
+    "webqs": 0.,
+    "wic": 0.5,
+    "winogrande": 0.5,
+    "wnli": 0.5,
+    "wsc": 0.5
+}
+
+def normalise(score, task):
+    return (score - RANDOM_BASELINE[task]) / (1. - RANDOM_BASELINE[task])
+
+def parse_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--input_files", type=lambda s: s.split(','), required=True)
+    parser.add_argument("--all_tasks", action="store_true")
+    parser.add_argument("--naive_average", action="store_true")
+    parser.add_argument("--acc_average", action="store_true")
+    parser.add_argument("--normalised_acc_average", action="store_true")
+    return parser.parse_args()
+
+def main():
+    args = parse_args()
+    for input_file in args.input_files:
+        assert os.path.basename(input_file).endswith("_agg.json")
+        experiment_name = os.path.basename(input_file).split("_agg.json")[0]
+        with open(input_file, "r") as fi:
+            experiment = json.load(fi)
+
+        results = experiment["results"]
+        tokens = experiment["tokens"]
+        run = wandb.init(project="bigscience-tr3-evaluation-through-training", entity="timerobber", name=experiment_name,
+                         reinit=True)
+        for i, n_tokens in enumerate(tokens):
+            all_values = []
+            acc_average = []
+            normalised_acc_average = []
+            for task, task_results in results.items():
+                values = None
+                for metric, values in task_results.items():
+                    if args.all_tasks:
+                        wandb.log({f"{task}_{metric}": values[i], "tokens": tokens[i]})
+                    if "stderr" not in metric and "ppl" not in metric:
+                        all_values.append(values[i])
+                        if metric == "acc":
+                            acc_average.append(values[i])
+                            normalised_acc_average.append(normalise(values[i], task))
+            if args.naive_average:
+                wandb.log({f"naive_average": np.mean(all_values), "tokens": tokens[i]})
+            if args.acc_average:
+                wandb.log({f"acc_average": np.mean(acc_average), "tokens": tokens[i]})
+            if args.normalised_acc_average:
+                wandb.log({f"normalised_acc_average": np.mean(normalised_acc_average), "tokens": tokens[i]})
+
+        run.finish()
+
+if __name__ == "__main__":
+    main()

evaluation/utilities/find_checkpoints_at_token_intervals.py

@@ -0,0 +1,27 @@
+import datasets
+import json
+
+steps_vs_samples = datasets.load_dataset("csv", data_files="run-.-tag-steps-vs-samples_y=steps,x=samples.csv")["train"]
+
+slope = (steps_vs_samples[-1]["Step"] - steps_vs_samples[-2]["Step"]) / (
+        steps_vs_samples[-1]["Value"] - steps_vs_samples[-2]["Value"])
+offset = steps_vs_samples[-1]["Step"] - steps_vs_samples[-1]["Value"] * slope
+
+token_interval = 1e10
+step_interval = 1500
+tokens_per_sample = 2048
+token_count = token_interval
+
+output_checkpoints = []
+
+for item in steps_vs_samples:
+    if item["Step"] * tokens_per_sample > token_count:
+        token_count += token_interval
+        step = step_interval * (item['Value'] // step_interval)
+        tokens = tokens_per_sample * (slope * (step_interval * (item['Value'] // step_interval)) + offset)
+        print(f"step: {step}")
+        print(f"tokens at that step: {tokens}")
+        output_checkpoints.append({"step": step, "tokens": tokens})
+
+
+json.dump(output_checkpoints, open("steps_to_evaluate_with_tokens.json", "w"))

evaluation/utilities/plot_all_eval.py

@@ -0,0 +1,45 @@
+import json
+import os
+from argparse import ArgumentParser
+
+from matplotlib import pyplot as plt
+
+
+def get_args():
+    parser = ArgumentParser()
+    parser.add_argument('--input-files', type=lambda s: s.split(','), required=True, help='Input files that hold all evaluation metrics')
+    return parser.parse_args()
+
+def main():
+    args = get_args()
+
+    plots = {} # {"{EVALUATION}_{METRIC}": plt.figure}
+    for input_file in args.input_files:
+        assert os.path.basename(input_file).endswith("_agg.json")
+        experiment_name = os.path.basename(input_file).split("_agg.json")[0]
+        with open(input_file, "r") as fi:
+            experiment = json.load(fi)
+
+        tokens = experiment["tokens"]
+        for evaluation_name, evaluation in experiment["results"].items():
+            for metric_name, metric in evaluation.items():
+                key = f"{evaluation_name}_{metric_name}"
+                if key[-7:] == "_stderr":
+                    continue
+
+                if key not in plots:
+                    plot = plt.figure(len(plots))
+                    plot = plot.add_subplot(1,1,1)
+                    plot.set_title(key)
+                    plots[key] = plot
+
+                plot = plots[key]
+
+                plot.plot(tokens, metric, label=experiment_name)
+
+    for plot in plots.values():
+        plot.legend()
+    plt.show()
+
+if __name__ == "__main__":
+    main()

jz/.gitignore

@@ -0,0 +1,133 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# Slurm job output and error
+*.err
+*.out

jz/.gitmodules

@@ -0,0 +1,3 @@
+[submodule "lm-evaluation-harness"]
+	path = lm-evaluation-harness
+	url = https://github.com/huggingface/lm-evaluation-harness.git

jz/README.md

@@ -0,0 +1,27 @@
+# jay-z
+
+Jean Zay aka JZ pronounced "Jay-Z"
+
+This section of the repo is all about how things are done on JZ.
+
+Main documents:
+
+- [Compute Resources](./compute-resources.md)
+- [JZ Specs](./hpc-specs.md)
+- [Framework-specific notes](./frameworks/)
+- [Model-specific Instructions](./archs/)
+
+Code:
+- [Work Env and Setup](./envs/README.md)
+- [SLURM scripts](./scripts/)
+- [Config files](./configs/)
+
+Tools:
+- [SLURM HowTo](./slurm/)
+- [Various Tools](./tools/)
+
+General JZ Docs:
+
+- HF Internal: https://github.com/huggingface/conf/wiki/JZ
+- Official: http://www.idris.fr/eng/jean-zay/
+- Collaborative doc: https://jean-zay-doc.readthedocs.io/en/latest/