Add files using upload-large-folder tool

.gitignore

@@ -0,0 +1,111 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+env/
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# 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/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# dotenv
+.env
+
+# virtualenv
+.venv
+venv/
+ENV/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+
+# IDE settings
+.vscode/
+.idea/
+
+# WanDB
+wandb
+
+*DS_Store

CODEOWNERS

@@ -0,0 +1 @@
+* @bigscience/bigscience-codeowners

LICENSE

@@ -0,0 +1,16 @@
+Apache Software License 2.0
+
+Copyright (c) 2021, Stas Bekman
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+

MANIFEST.in

@@ -0,0 +1,9 @@
+include CONTRIBUTING.md
+include LICENSE
+include README.md
+
+recursive-include tests *
+recursive-exclude * __pycache__
+recursive-exclude * *.py[co]
+
+recursive-include docs *.rst conf.py Makefile make.bat *.jpg *.png *.gif

Makefile

@@ -0,0 +1,59 @@
+.PHONY: clean clean-test clean-pyc clean-build docs help
+.DEFAULT_GOAL := help
+
+define BROWSER_PYSCRIPT
+import os, webbrowser, sys
+
+from urllib.request import pathname2url
+
+webbrowser.open("file://" + pathname2url(os.path.abspath(sys.argv[1])))
+endef
+export BROWSER_PYSCRIPT
+
+define PRINT_HELP_PYSCRIPT
+import re, sys
+
+for line in sys.stdin:
+	match = re.match(r'^([a-zA-Z_-]+):.*?## (.*)$$', line)
+	if match:
+		target, help = match.groups()
+		print("%-20s %s" % (target, help))
+endef
+export PRINT_HELP_PYSCRIPT
+
+BROWSER := python -c "$$BROWSER_PYSCRIPT"
+
+help:
+	@python -c "$$PRINT_HELP_PYSCRIPT" < $(MAKEFILE_LIST)
+
+clean: clean-build clean-pyc clean-test ## remove all build, test, coverage and Python artifacts
+
+clean-build: ## remove build artifacts
+	rm -fr build/
+	rm -fr dist/
+	rm -fr .eggs/
+	find . -name '*.egg-info' -exec rm -fr {} +
+	find . -name '*.egg' -exec rm -f {} +
+
+clean-pyc: ## remove Python file artifacts
+	find . -name '*.pyc' -exec rm -f {} +
+	find . -name '*.pyo' -exec rm -f {} +
+	find . -name '*~' -exec rm -f {} +
+	find . -name '__pycache__' -exec rm -fr {} +
+
+clean-test: ## remove test and coverage artifacts
+	rm -fr .pytest_cache
+
+lint: ## check style with flake8
+	flake8 bigscience tests
+
+test: ## run tests quickly with the default Python
+	pytest
+
+dist: clean ## builds source and wheel package
+	python setup.py sdist
+	python setup.py bdist_wheel
+	ls -l dist
+
+install: clean ## install the package to the active Python's site-packages
+	python setup.py install

TODO.md

@@ -0,0 +1,54 @@
+# Things to do
+
+
+## CI
+
+- replace CI with constantly running GCP instance
+
+
+
+## TODO
+
+general:
+
+- check if --jobid=$SLURM_JOB is actually needed in the slurm script - especially when doing it interactively
+
+- add alerts for loss spikes
+
+- check that my syncing script doesn't sync deleted files, should SCRATCH wipe something out that is already on the hub!
+
+- update deepspeed_to_transformers.py to require a specific version once a new version of transformers is released and then update the doc https://github.com/bigscience-workshop/bigscience/tree/master/train/tr1-13B-base#checkpoint-conversion-and-upload
+
+- see if can speed up the meg cuda kernels building
+https://huggingface.slack.com/archives/C01NHER1JLS/p1630520151064500?thread_ts=1630473623.060700&cid=C01NHER1JLS
+
+- since we are starting to tweak the seed, we should start logging the ranges of iteration for each seed, so that down the road we could reproduce the data.
+
+
+- test 1.3b with final config using tr7d as a base line: https://github.com/bigscience-workshop/bigscience/blob/cfdd69b89118a77567ee87b5a181c233fffef377/train/tr7-alibi/tr7d-1B3-modeling-alibi.slurm
+
+
+## sysadmin
+
+
+
+### conda packages
+
+currently each one of us has a copy of the same conda packages:
+
+```
+conda config --show pkgs_dirs envs_dirs and the output is:
+pkgs_dirs:
+  - /gpfslocalsup/pub/anaconda-py3/2020.02/pkgs
+  - /linkhome/rech/genhug01/uue59kq/.conda/pkgs
+envs_dirs:
+  - /gpfswork/rech/six/commun/conda
+  - /linkhome/rech/genhug01/uue59kq/.conda/envs
+  - /gpfslocalsup/pub/anaconda-py3/2020.02/envs
+```
+
+we should aggregate them under the same dir.
+
+probably need to find out the right env var (best) or ~/.condarc (less good) and point it to the shared conda env.
+
+- also document in the getting started docs to make sure new users don't end up with ~/.conda dir which uses up their HOME dir to 100%.

evaluation/results/tr11/bloom/mdmeta.txt

@@ -0,0 +1,1595 @@
+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: 0.4112627986348123
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: arc_easy
+      type: arc_easy
+    metrics:
+    - name: acc
+      type: acc
+      value: 0.726010101010101
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: axb
+      type: axb
+    metrics:
+    - name: acc
+      type: acc
+      value: 0.5751811594202898
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: axg
+      type: axg
+    metrics:
+    - name: acc
+      type: acc
+      value: 0.5252808988764045
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: boolq
+      type: boolq
+    metrics:
+    - name: acc
+      type: acc
+      value: 0.6345565749235474
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: cb
+      type: cb
+    metrics:
+    - name: acc
+      type: acc
+      value: 0.3392857142857143
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: cola
+      type: cola
+    metrics:
+    - name: acc
+      type: acc
+      value: 0.39022051773729627
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: copa
+      type: copa
+    metrics:
+    - name: acc
+      type: acc
+      value: 0.56
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: crows_pairs_english
+      type: crows_pairs_english
+    metrics:
+    - name: acc
+      type: acc
+      value: 0.5
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: crows_pairs_french
+      type: crows_pairs_french
+    metrics:
+    - name: acc
+      type: acc
+      value: 0.505664877757901
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: diabla
+      type: diabla
+    metrics:
+    - name: acc
+      type: acc
+      value: 0.2947981906750174
+      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: 4.25431550058444
+      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: 3.716877477347089
+      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: 1.7049030137120964
+      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: 6.576581380404954
+      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: 2.8562364775797944
+      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: 4.80721528624391
+      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: 2.7312177406635065
+      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: 5.993409478990023
+      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: 3.5936169095529493
+      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: 2.159035321398085
+      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: 2.167873680006659
+      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: 5.286975089885673
+      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: 3.4516208322236017
+      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: 3.7051034724765612
+      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: 7.0889312398688125
+      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: 3.4300748208111838
+      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: 2.3380585896268107
+      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: 1.9595604725375586
+      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: 1.8819637649637901
+      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: 5.773850600380297
+      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: 2.4306140728294086
+      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: 4.304305536244342
+      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: 1.9374688438541796
+      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: 9.740353097219378
+      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: 6.035269765075012
+      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: 2.365451129546636
+      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: 5.70676742569154
+      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: 8.855204288260023
+      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: 2.920943798471208
+      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: 5.452028001573195
+      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: 3.7056829077179225
+      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: 4.058579478967854
+      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: 3.127237816041562
+      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: 3.9500357969906683
+      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: 1.976163584180101
+      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: 5.500542085165231
+      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: 2.314465100752677
+      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: 4.942322446550142
+      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: 2.259421750521777
+      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: 9.743025325635475
+      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: 6.233724699944989
+      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: 2.0508893415872107
+      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: 3.0390148516287927
+      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: 7.147132270533836
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: gsarti/flores_101_khm
+      type: gsarti/flores_101_khm
+    metrics:
+    - name: byte_perplexity
+      type: byte_perplexity
+      value: 3.366514710252477
+      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: 3.2413845359487885
+      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: 2.9023196482741027
+      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: 2.331446855837494
+      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: 5.223609016485348
+      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: 4.847471204107301
+      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: 4.5432035498036765
+      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: 5.5910516978201015
+      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: 5.4301049946044175
+      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: 12.031029857399394
+      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: 4.794302548141229
+      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: 6.856682255407709
+      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: 2.3354144607382983
+      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: 9.04135227904975
+      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: 3.094907723618666
+      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: 5.2659698341456505
+      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: 2.2220779892820985
+      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: 2.5229159853414433
+      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: 2.799153089002766
+      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: 3.628942049758715
+      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: 6.666236527803879
+      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: 5.015319074943932
+      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: 4.938044040751036
+      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: 3.607440766288032
+      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: 11.31585044916705
+      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: 5.981891184515959
+      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: 4.7716086841502685
+      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: 3.01200174157614
+      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: 1.8411472115156693
+      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: 4.623872921169341
+      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: 3.049829411973529
+      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: 1.7083443875791493
+      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: 4.037719650548048
+      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: 4.141036287764831
+      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: 4.7109183690601295
+      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: 4.206170931541356
+      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: 9.154342083821405
+      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: 1.7955816311143258
+      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: 2.241096141430147
+      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: 3.344977179674293
+      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: 2.6844272218041634
+      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: 5.1645951632801745
+      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: 6.8098996634099445
+      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: 3.785457016715163
+      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: 3.7498953645610875
+      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: 2.104151663233468
+      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: 3.3178240103796037
+      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: 2.088543437159643
+      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: 11.766013385445124
+      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: 1.7788699847612357
+      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: 8.499879863290486
+      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: 1.65901207387262
+      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: 6.141703791276928
+      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: 4.690199677955254
+      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: 4.360585696242932
+      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: 2.1183545781883515
+      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: 2.273787884962656
+      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: 6.016954767729589
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: headqa
+      type: headqa
+    metrics:
+    - name: acc
+      type: acc
+      value: 0.3464624361779723
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: hellaswag
+      type: hellaswag
+    metrics:
+    - name: acc
+      type: acc
+      value: 0.5353515236008763
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: lambada_mt_de
+      type: lambada_mt_de
+    metrics:
+    - name: acc
+      type: acc
+      value: 0.3291286629148069
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: lambada_mt_en
+      type: lambada_mt_en
+    metrics:
+    - name: acc
+      type: acc
+      value: 0.6720357073549389
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: lambada_mt_es
+      type: lambada_mt_es
+    metrics:
+    - name: acc
+      type: acc
+      value: 0.476421502037648
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: lambada_mt_it
+      type: lambada_mt_it
+    metrics:
+    - name: acc
+      type: acc
+      value: 0.4061711624296526
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: logiqa
+      type: logiqa
+    metrics:
+    - name: acc
+      type: acc
+      value: 0.2350230414746544
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: mathqa
+      type: mathqa
+    metrics:
+    - name: acc
+      type: acc
+      value: 0.27671691792294806
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: mc_taco
+      type: mc_taco
+    metrics:
+    - name: em
+      type: em
+      value: 0.13063063063063063
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: mnli
+      type: mnli
+    metrics:
+    - name: acc
+      type: acc
+      value: 0.3545565500406835
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: mnli_mismatched
+      type: mnli_mismatched
+    metrics:
+    - name: acc
+      type: acc
+      value: 0.3545565500406835
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: mrpc
+      type: mrpc
+    metrics:
+    - name: acc
+      type: acc
+      value: 0.3872549019607843
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: multirc
+      type: multirc
+    metrics:
+    - name: acc
+      type: acc
+      value: 0.570957095709571
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: openbookqa
+      type: openbookqa
+    metrics:
+    - name: acc
+      type: acc
+      value: 0.312
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: piqa
+      type: piqa
+    metrics:
+    - name: acc
+      type: acc
+      value: 0.7812840043525572
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: prost
+      type: prost
+    metrics:
+    - name: acc
+      type: acc
+      value: 0.2977156276686593
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: pubmedqa
+      type: pubmedqa
+    metrics:
+    - name: acc
+      type: acc
+      value: 0.741
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: qnli
+      type: qnli
+    metrics:
+    - name: acc
+      type: acc
+      value: 0.5172981878088962
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: qqp
+      type: qqp
+    metrics:
+    - name: acc
+      type: acc
+      value: 0.5883007667573584
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: race
+      type: race
+    metrics:
+    - name: acc
+      type: acc
+      value: 0.39043062200956935
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: rte
+      type: rte
+    metrics:
+    - name: acc
+      type: acc
+      value: 0.5198555956678701
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: sciq
+      type: sciq
+    metrics:
+    - name: acc
+      type: acc
+      value: 0.936
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: sst
+      type: sst
+    metrics:
+    - name: acc
+      type: acc
+      value: 0.6043577981651376
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: triviaqa
+      type: triviaqa
+    metrics:
+    - name: acc
+      type: acc
+      value: 0.18332891363917617
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: tydiqa_primary
+      type: tydiqa_primary
+    metrics:
+    - name: acc
+      type: acc
+      value: 0.2809817301342725
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: webqs
+      type: webqs
+    metrics:
+    - name: acc
+      type: acc
+      value: 0.061515748031496065
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: wic
+      type: wic
+    metrics:
+    - name: acc
+      type: acc
+      value: 0.5062695924764891
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: winogrande
+      type: winogrande
+    metrics:
+    - name: acc
+      type: acc
+      value: 0.7095501183898973
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: wnli
+      type: wnli
+    metrics:
+    - name: acc
+      type: acc
+      value: 0.5704225352112676
+      verified: false
+  - task:
+      type: text-generation
+      name: text generation
+    dataset:
+      name: wsc
+      type: wsc
+    metrics:
+    - name: acc
+      type: acc
+      value: 0.5192307692307693
+      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/bloom1b3/bslmeval.json

@@ -0,0 +1,2938 @@
+{
+  "results": {
+    "arc_challenge": {
+      "2022-07-13-11-29-13": {
+        "acc": 0.23464163822525597,
+        "acc_norm": 0.26791808873720135,
+        "acc_norm_stderr": 0.012942030195136423,
+        "acc_stderr": 0.012383873560768673
+      }
+    },
+    "arc_easy": {
+      "2022-07-13-11-29-13": {
+        "acc": 0.5631313131313131,
+        "acc_norm": 0.4810606060606061,
+        "acc_norm_stderr": 0.010252420496894487,
+        "acc_stderr": 0.010177672928157678
+      }
+    },
+    "axb+GPT-3 style": {
+      "2022-07-15-11-47-34": {
+        "acc": 0.4855072463768116,
+        "acc_norm": 0.5878623188405797,
+        "acc_norm_stderr": 0.014820785339690506,
+        "acc_stderr": 0.015048725939283577,
+        "prompt_name": "GPT-3 style",
+        "task_name": "axb"
+      }
+    },
+    "axb+MNLI crowdsource": {
+      "2022-07-15-11-47-34": {
+        "acc": 0.447463768115942,
+        "acc_norm": 0.4166666666666667,
+        "acc_norm_stderr": 0.014844481058991162,
+        "acc_stderr": 0.0149717153798021,
+        "prompt_name": "MNLI crowdsource",
+        "task_name": "axb"
+      }
+    },
+    "axb+based on the previous passage": {
+      "2022-07-15-11-47-34": {
+        "acc": 0.4846014492753623,
+        "acc_norm": 0.4166666666666667,
+        "acc_norm_stderr": 0.014844481058991162,
+        "acc_stderr": 0.015047910329698355,
+        "prompt_name": "based on the previous passage",
+        "task_name": "axb"
+      }
+    },
+    "axb+can we infer": {
+      "2022-07-15-11-47-34": {
+        "acc": 0.421195652173913,
+        "acc_norm": 0.4166666666666667,
+        "acc_norm_stderr": 0.014844481058991162,
+        "acc_stderr": 0.014866888213508284,
+        "prompt_name": "can we infer",
+        "task_name": "axb"
+      }
+    },
+    "axb+does it follow that": {
+      "2022-07-15-11-47-34": {
+        "acc": 0.4375,
+        "acc_norm": 0.4166666666666667,
+        "acc_norm_stderr": 0.014844481058991162,
+        "acc_stderr": 0.014936970932375573,
+        "prompt_name": "does it follow that",
+        "task_name": "axb"
+      }
+    },
+    "axb+does this imply": {
+      "2022-07-15-11-47-34": {
+        "acc": 0.5353260869565217,
+        "acc_norm": 0.4166666666666667,
+        "acc_norm_stderr": 0.014844481058991162,
+        "acc_stderr": 0.015017429208641943,
+        "prompt_name": "does this imply",
+        "task_name": "axb"
+      }
+    },
+    "axb+guaranteed true": {
+      "2022-07-15-11-47-34": {
+        "acc": 0.44655797101449274,
+        "acc_norm": 0.4166666666666667,
+        "acc_norm_stderr": 0.014844481058991162,
+        "acc_stderr": 0.014968808595500557,
+        "prompt_name": "guaranteed true",
+        "task_name": "axb"
+      }
+    },
+    "axb+justified in saying": {
+      "2022-07-15-11-47-34": {
+        "acc": 0.4365942028985507,
+        "acc_norm": 0.4166666666666667,
+        "acc_norm_stderr": 0.014844481058991162,
+        "acc_stderr": 0.014933509475434285,
+        "prompt_name": "justified in saying",
+        "task_name": "axb"
+      }
+    },
+    "axb+must be true": {
+      "2022-07-15-11-47-34": {
+        "acc": 0.4266304347826087,
+        "acc_norm": 0.4166666666666667,
+        "acc_norm_stderr": 0.014844481058991162,
+        "acc_stderr": 0.014892084059444173,
+        "prompt_name": "must be true",
+        "task_name": "axb"
+      }
+    },
+    "axb+should assume": {
+      "2022-07-15-11-47-34": {
+        "acc": 0.5163043478260869,
+        "acc_norm": 0.4157608695652174,
+        "acc_norm_stderr": 0.014839845193003246,
+        "acc_stderr": 0.015047045240919796,
+        "prompt_name": "should assume",
+        "task_name": "axb"
+      }
+    },
+    "axg+GPT-3 style": {
+      "2022-07-15-11-47-34": {
+        "acc": 0.4803370786516854,
+        "acc_norm": 0.5,
+        "acc_norm_stderr": 0.026537244621713762,
+        "acc_stderr": 0.02651671646679541,
+        "parity": 0.9606741573033708,
+        "parity_stderr": 0.01460967112412074,
+        "prompt_name": "GPT-3 style",
+        "task_name": "axg"
+      }
+    },
+    "axg+MNLI crowdsource": {
+      "2022-07-15-11-47-34": {
+        "acc": 0.5140449438202247,
+        "acc_norm": 0.5,
+        "acc_norm_stderr": 0.026537244621713762,
+        "acc_stderr": 0.026526773058212952,
+        "parity": 0.9719101123595506,
+        "parity_stderr": 0.012419422972302346,
+        "prompt_name": "MNLI crowdsource",
+        "task_name": "axg"
+      }
+    },
+    "axg+based on the previous passage": {
+      "2022-07-15-11-47-34": {
+        "acc": 0.5,
+        "acc_norm": 0.5,
+        "acc_norm_stderr": 0.026537244621713762,
+        "acc_stderr": 0.026537244621713762,
+        "parity": 0.9662921348314607,
+        "parity_stderr": 0.013565419020002358,
+        "prompt_name": "based on the previous passage",
+        "task_name": "axg"
+      }
+    },
+    "axg+can we infer": {
+      "2022-07-15-11-47-34": {
+        "acc": 0.5,
+        "acc_norm": 0.5,
+        "acc_norm_stderr": 0.026537244621713762,
+        "acc_stderr": 0.026537244621713762,
+        "parity": 0.9887640449438202,
+        "parity_stderr": 0.007922544664164389,
+        "prompt_name": "can we infer",
+        "task_name": "axg"
+      }
+    },
+    "axg+does it follow that": {
+      "2022-07-15-11-47-34": {
+        "acc": 0.5,
+        "acc_norm": 0.5,
+        "acc_norm_stderr": 0.026537244621713762,
+        "acc_stderr": 0.026537244621713762,
+        "parity": 1.0,
+        "parity_stderr": 0.0,
+        "prompt_name": "does it follow that",
+        "task_name": "axg"
+      }
+    },
+    "axg+does this imply": {
+      "2022-07-15-11-47-34": {
+        "acc": 0.49719101123595505,
+        "acc_norm": 0.5,
+        "acc_norm_stderr": 0.026537244621713762,
+        "acc_stderr": 0.026536825838510643,
+        "parity": 0.9831460674157303,
+        "parity_stderr": 0.009675491064988365,
+        "prompt_name": "does this imply",
+        "task_name": "axg"
+      }
+    },
+    "axg+guaranteed true": {
+      "2022-07-15-11-47-34": {
+        "acc": 0.48314606741573035,
+        "acc_norm": 0.5,
+        "acc_norm_stderr": 0.026537244621713762,
+        "acc_stderr": 0.026522164260489825,
+        "parity": 0.9887640449438202,
+        "parity_stderr": 0.007922544664164387,
+        "prompt_name": "guaranteed true",
+        "task_name": "axg"
+      }
+    },
+    "axg+justified in saying": {
+      "2022-07-15-11-47-34": {
+        "acc": 0.5,
+        "acc_norm": 0.5,
+        "acc_norm_stderr": 0.026537244621713762,
+        "acc_stderr": 0.026537244621713762,
+        "parity": 0.9887640449438202,
+        "parity_stderr": 0.007922544664164385,
+        "prompt_name": "justified in saying",
+        "task_name": "axg"
+      }
+    },
+    "axg+must be true": {
+      "2022-07-15-11-47-34": {
+        "acc": 0.4803370786516854,
+        "acc_norm": 0.5,
+        "acc_norm_stderr": 0.026537244621713762,
+        "acc_stderr": 0.026516716466795417,
+        "parity": 0.9719101123595506,
+        "parity_stderr": 0.012419422972302347,
+        "prompt_name": "must be true",
+        "task_name": "axg"
+      }
+    },
+    "axg+should assume": {
+      "2022-07-15-11-47-34": {
+        "acc": 0.49719101123595505,
+        "acc_norm": 0.5,
+        "acc_norm_stderr": 0.026537244621713762,
+        "acc_stderr": 0.026536825838510643,
+        "parity": 0.949438202247191,
+        "parity_stderr": 0.016468648928151884,
+        "prompt_name": "should assume",
+        "task_name": "axg"
+      }
+    },
+    "boolq": {
+      "2022-07-13-11-29-13": {
+        "acc": 0.617737003058104,
+        "acc_stderr": 0.008499149690449272
+      }
+    },
+    "boolq+GPT-3 Style": {
+      "2022-07-15-11-47-34": {
+        "acc": 0.5896024464831804,
+        "acc_norm": 0.6211009174311927,
+        "acc_norm_stderr": 0.008484678718565017,
+        "acc_stderr": 0.008603488048617526,
+        "prompt_name": "GPT-3 Style",
+        "task_name": "boolq"
+      }
+    },
+    "boolq+I wonder\u2026": {
+      "2022-07-15-11-47-34": {
+        "acc": 0.563914373088685,
+        "acc_norm": 0.6217125382262997,
+        "acc_norm_stderr": 0.00848200113393099,
+        "acc_stderr": 0.008673312776324934,
+        "prompt_name": "I wonder\u2026",
+        "task_name": "boolq"
+      }
+    },
+    "boolq+after_reading": {
+      "2022-07-15-11-47-34": {
+        "acc": 0.6217125382262997,
+        "acc_norm": 0.3804281345565749,
+        "acc_norm_stderr": 0.008491310027059626,
+        "acc_stderr": 0.00848200113393099,
+        "prompt_name": "after_reading",
+        "task_name": "boolq"
+      }
+    },
+    "boolq+based on the following passage": {
+      "2022-07-15-11-47-34": {
+        "acc": 0.3798165137614679,
+        "acc_norm": 0.6012232415902141,
+        "acc_norm_stderr": 0.008563973987729906,
+        "acc_stderr": 0.008488668235778644,
+        "prompt_name": "based on the following passage",
+        "task_name": "boolq"
+      }
+    },
+    "boolq+based on the previous passage": {
+      "2022-07-15-11-47-34": {
+        "acc": 0.6146788990825688,
+        "acc_norm": 0.6217125382262997,
+        "acc_norm_stderr": 0.00848200113393099,
+        "acc_stderr": 0.008511930879680635,
+        "prompt_name": "based on the previous passage",
+        "task_name": "boolq"
+      }
+    },
+    "boolq+could you tell me\u2026": {
+      "2022-07-15-11-47-34": {
+        "acc": 0.5840978593272171,
+        "acc_norm": 0.6217125382262997,
+        "acc_norm_stderr": 0.00848200113393099,
+        "acc_stderr": 0.008620469604001,
+        "prompt_name": "could you tell me\u2026",
+        "task_name": "boolq"
+      }
+    },
+    "boolq+exam": {
+      "2022-07-15-11-47-34": {
+        "acc": 0.6220183486238532,
+        "acc_norm": 0.6217125382262997,
+        "acc_norm_stderr": 0.00848200113393099,
+        "acc_stderr": 0.008480656964585267,
+        "prompt_name": "exam",
+        "task_name": "boolq"
+      }
+    },
+    "boolq+exercise": {
+      "2022-07-15-11-47-34": {
+        "acc": 0.6217125382262997,
+        "acc_norm": 0.46788990825688076,
+        "acc_norm_stderr": 0.0087270030269178,
+        "acc_stderr": 0.00848200113393099,
+        "prompt_name": "exercise",
+        "task_name": "boolq"
+      }
+    },
+    "boolq+valid_binary": {
+      "2022-07-15-11-47-34": {
+        "acc": 0.491131498470948,
+        "acc_norm": 0.37370030581039754,
+        "acc_norm_stderr": 0.008461461177104003,
+        "acc_stderr": 0.008743679265456042,
+        "prompt_name": "valid_binary",
+        "task_name": "boolq"
+      }
+    },
+    "boolq+yes_no_question": {
+      "2022-07-15-11-47-34": {
+        "acc": 0.5951070336391437,
+        "acc_norm": 0.6217125382262997,
+        "acc_norm_stderr": 0.00848200113393099,
+        "acc_stderr": 0.008585393347962319,
+        "prompt_name": "yes_no_question",
+        "task_name": "boolq"
+      }
+    },
+    "cb+GPT-3 style": {
+      "2022-07-15-11-47-34": {
+        "acc": 0.42857142857142855,
+        "acc_stderr": 0.06672848092813057,
+        "f1": 0.21956970232832299,
+        "prompt_name": "GPT-3 style",
+        "task_name": "cb"
+      }
+    },
+    "cb+MNLI crowdsource": {
+      "2022-07-15-11-47-34": {
+        "acc": 0.42857142857142855,
+        "acc_stderr": 0.06672848092813057,
+        "f1": 0.21956970232832299,
+        "prompt_name": "MNLI crowdsource",
+        "task_name": "cb"
+      }
+    },
+    "cb+always/sometimes/never": {
+      "2022-07-15-11-47-34": {
+        "acc": 0.08928571428571429,
+        "acc_stderr": 0.038450387280282494,
+        "f1": 0.054644808743169404,
+        "prompt_name": "always/sometimes/never",
+        "task_name": "cb"
+      }
+    },
+    "cb+based on the previous passage": {
+      "2022-07-15-11-47-34": {
+        "acc": 0.35714285714285715,
+        "acc_stderr": 0.06460957383809221,
+        "f1": 0.2094181249110827,
+        "prompt_name": "based on the previous passage",
+        "task_name": "cb"
+      }
+    },
+    "cb+can we infer": {
+      "2022-07-15-11-47-34": {
+        "acc": 0.25,
+        "acc_stderr": 0.058387420812114225,
+        "f1": 0.15483870967741933,
+        "prompt_name": "can we infer",
+        "task_name": "cb"
+      }
+    },
+    "cb+claim true/false/inconclusive": {
+      "2022-07-15-11-47-34": {
+        "acc": 0.42857142857142855,
+        "acc_stderr": 0.06672848092813057,
+        "f1": 0.21956970232832299,
+        "prompt_name": "claim true/false/inconclusive",
+        "task_name": "cb"
+      }
+    },
+    "cb+consider always/sometimes/never": {
+      "2022-07-15-11-47-34": {
+        "acc": 0.08928571428571429,
+        "acc_stderr": 0.038450387280282494,
+        "f1": 0.054644808743169404,
+        "prompt_name": "consider always/sometimes/never",
+        "task_name": "cb"
+      }
+    },
+    "cb+does it follow that": {
+      "2022-07-15-11-47-34": {
+        "acc": 0.30357142857142855,
+        "acc_stderr": 0.06199938655510754,
+        "f1": 0.2613574165298303,
+        "prompt_name": "does it follow that",
+        "task_name": "cb"
+      }
+    },
+    "cb+does this imply": {
+      "2022-07-15-11-47-34": {
+        "acc": 0.10714285714285714,
+        "acc_stderr": 0.0417053005800816,
+        "f1": 0.11222753854332802,
+        "prompt_name": "does this imply",
+        "task_name": "cb"
+      }
+    },
+    "cb+guaranteed true": {
+      "2022-07-15-11-47-34": {
+        "acc": 0.21428571428571427,
+        "acc_stderr": 0.055328333517248834,
+        "f1": 0.15883777239709443,
+        "prompt_name": "guaranteed true",
+        "task_name": "cb"
+      }
+    },
+    "cb+guaranteed/possible/impossible": {
+      "2022-07-15-11-47-34": {
+        "acc": 0.10714285714285714,
+        "acc_stderr": 0.0417053005800816,
+        "f1": 0.07871939736346516,
+        "prompt_name": "guaranteed/possible/impossible",
+        "task_name": "cb"
+      }
+    },
+    "cb+justified in saying": {
+      "2022-07-15-11-47-34": {
+        "acc": 0.21428571428571427,
+        "acc_stderr": 0.055328333517248834,
+        "f1": 0.1623009758602979,
+        "prompt_name": "justified in saying",
+        "task_name": "cb"
+      }
+    },
+    "cb+must be true": {
+      "2022-07-15-11-47-34": {
+        "acc": 0.19642857142857142,
+        "acc_stderr": 0.05357142857142859,
+        "f1": 0.1384656508954825,
+        "prompt_name": "must be true",
+        "task_name": "cb"
+      }
+    },
+    "cb+should assume": {
+      "2022-07-15-11-47-34": {
+        "acc": 0.19642857142857142,
+        "acc_stderr": 0.05357142857142858,
+        "f1": 0.14613935969868175,
+        "prompt_name": "should assume",
+        "task_name": "cb"
+      }
+    },
+    "cb+take the following as truth": {
+      "2022-07-15-11-47-34": {
+        "acc": 0.4107142857142857,
+        "acc_stderr": 0.06633634150359538,
+        "f1": 0.1940928270042194,
+        "prompt_name": "take the following as truth",
+        "task_name": "cb"
+      }
+    },
+    "cola+Following sentence acceptable": {
+      "2022-07-15-11-47-34": {
+        "acc": 0.6625119846596357,
+        "acc_norm": 0.31064237775647174,
+        "acc_norm_stderr": 0.014335695984672221,
+        "acc_stderr": 0.014648467353878477,
+        "prompt_name": "Following sentence acceptable",
+        "task_name": "cola"
+      }
+    },
+    "cola+Make sense yes no": {
+      "2022-07-15-11-47-34": {
+        "acc": 0.3710450623202301,
+        "acc_norm": 0.6903163950143816,
+        "acc_norm_stderr": 0.014323506235950028,
+        "acc_stderr": 0.01496543118537874,
+        "prompt_name": "Make sense yes no",
+        "task_name": "cola"
+      }
+    },
+    "cola+Previous sentence acceptable": {
+      "2022-07-15-11-47-34": {
+        "acc": 0.6864813039309684,
+        "acc_norm": 0.6912751677852349,
+        "acc_norm_stderr": 0.014311244461311299,
+        "acc_stderr": 0.014371834902632595,
+        "prompt_name": "Previous sentence acceptable",
+        "task_name": "cola"
+      }
+    },
+    "cola+editing": {
+      "2022-07-15-11-47-34": {
+        "acc": 0.46596356663470756,
+        "acc_norm": 0.6912751677852349,
+        "acc_norm_stderr": 0.014311244461311299,
+        "acc_stderr": 0.015453525186655532,
+        "prompt_name": "editing",
+        "task_name": "cola"
+      }
+    },
+    "cola+is_this_correct": {
+      "2022-07-15-11-47-34": {
+        "acc": 0.6893576222435283,
+        "acc_norm": 0.6912751677852349,
+        "acc_norm_stderr": 0.014311244461311299,
+        "acc_stderr": 0.014335695984672223,
+        "prompt_name": "is_this_correct",
+        "task_name": "cola"
+      }
+    },
+    "copa": {
+      "2022-07-13-11-29-13": {
+        "acc": 0.7,
+        "acc_stderr": 0.046056618647183814
+      }
+    },
+    "copa+C1 or C2? premise, so/because\u2026": {
+      "2022-07-15-11-47-34": {
+        "acc": 0.65,
+        "acc_norm": 0.57,
+        "acc_norm_stderr": 0.049756985195624284,
+        "acc_stderr": 0.047937248544110196,
+        "prompt_name": "C1 or C2? premise, so/because\u2026",
+        "task_name": "copa"
+      }
+    },
+    "copa+best_option": {
+      "2022-07-15-11-47-34": {
+        "acc": 0.52,
+        "acc_norm": 0.49,
+        "acc_norm_stderr": 0.05024183937956911,
+        "acc_stderr": 0.050211673156867795,
+        "prompt_name": "best_option",
+        "task_name": "copa"
+      }
+    },
+    "copa+cause_effect": {
+      "2022-07-15-11-47-34": {
+        "acc": 0.56,
+        "acc_norm": 0.45,
+        "acc_norm_stderr": 0.05,
+        "acc_stderr": 0.04988876515698589,
+        "prompt_name": "cause_effect",
+        "task_name": "copa"
+      }
+    },
+    "copa+choose": {
+      "2022-07-15-11-47-34": {
+        "acc": 0.53,
+        "acc_norm": 0.46,
+        "acc_norm_stderr": 0.05009082659620333,
+        "acc_stderr": 0.05016135580465919,
+        "prompt_name": "choose",
+        "task_name": "copa"
+      }
+    },
+    "copa+exercise": {
+      "2022-07-15-11-47-34": {
+        "acc": 0.54,
+        "acc_norm": 0.48,
+        "acc_norm_stderr": 0.050211673156867795,
+        "acc_stderr": 0.05009082659620332,
+        "prompt_name": "exercise",
+        "task_name": "copa"
+      }
+    },
+    "copa+i_am_hesitating": {
+      "2022-07-15-11-47-34": {
+        "acc": 0.56,
+        "acc_norm": 0.48,
+        "acc_norm_stderr": 0.050211673156867795,
+        "acc_stderr": 0.04988876515698589,
+        "prompt_name": "i_am_hesitating",
+        "task_name": "copa"
+      }
+    },
+    "copa+more likely": {
+      "2022-07-15-11-47-34": {
+        "acc": 0.53,
+        "acc_norm": 0.49,
+        "acc_norm_stderr": 0.05024183937956911,
+        "acc_stderr": 0.05016135580465919,
+        "prompt_name": "more likely",
+        "task_name": "copa"
+      }
+    },
+    "copa+plausible_alternatives": {
+      "2022-07-15-11-47-34": {
+        "acc": 0.56,
+        "acc_norm": 0.53,
+        "acc_norm_stderr": 0.05016135580465919,
+        "acc_stderr": 0.04988876515698589,
+        "prompt_name": "plausible_alternatives",
+        "task_name": "copa"
+      }
+    },
+    "crows_pairs_english+1": {
+      "2022-07-12-22-45-57": {
+        "acc": 0.49552772808586765,
+        "acc_norm": 0.49552772808586765,
+        "acc_norm_stderr": 0.012212810647205384,
+        "acc_stderr": 0.012212810647205384,
+        "prompt_name": "1",
+        "task_name": "crows_pairs_english"
+      }
+    },
+    "crows_pairs_english+2": {
+      "2022-07-12-22-45-57": {
+        "acc": 0.4883720930232558,
+        "acc_norm": 0.4883720930232558,
+        "acc_norm_stderr": 0.012209996095069646,
+        "acc_stderr": 0.012209996095069646,
+        "prompt_name": "2",
+        "task_name": "crows_pairs_english"
+      }
+    },
+    "crows_pairs_english+3": {
+      "2022-07-12-22-45-57": {
+        "acc": 0.5163983303518187,
+        "acc_norm": 0.4836016696481813,
+        "acc_norm_stderr": 0.012206729011137944,
+        "acc_stderr": 0.012206729011137944,
+        "prompt_name": "3",
+        "task_name": "crows_pairs_english"
+      }
+    },
+    "crows_pairs_english+4": {
+      "2022-07-12-22-45-57": {
+        "acc": 0.4919499105545617,
+        "acc_norm": 0.4919499105545617,
+        "acc_norm_stderr": 0.01221171617623539,
+        "acc_stderr": 0.01221171617623539,
+        "prompt_name": "4",
+        "task_name": "crows_pairs_english"
+      }
+    },
+    "crows_pairs_english+A_preference": {
+      "2022-07-12-22-45-57": {
+        "acc": 0.5104353011329755,
+        "acc_norm": 0.5104353011329755,
+        "acc_norm_stderr": 0.012210638982043397,
+        "acc_stderr": 0.012210638982043397,
+        "prompt_name": "A_preference",
+        "task_name": "crows_pairs_english"
+      }
+    },
+    "crows_pairs_english+A_stereotype_true": {
+      "2022-07-12-22-45-57": {
+        "acc": 0.4907573047107931,
+        "acc_norm": 0.5062611806797853,
+        "acc_norm_stderr": 0.012212341600228735,
+        "acc_stderr": 0.012211212339167695,
+        "prompt_name": "A_stereotype_true",
+        "task_name": "crows_pairs_english"
+      }
+    },
+    "crows_pairs_french+1_fr": {
+      "2022-07-12-22-45-57": {
+        "acc": 0.48598688133571855,
+        "acc_norm": 0.48598688133571855,
+        "acc_norm_stderr": 0.012208501686447066,
+        "acc_stderr": 0.012208501686447066,
+        "prompt_name": "1_fr",
+        "task_name": "crows_pairs_french"
+      }
+    },
+    "crows_pairs_french+2_fr": {
+      "2022-07-12-22-45-57": {
+        "acc": 0.49850924269528923,
+        "acc_norm": 0.49850924269528923,
+        "acc_norm_stderr": 0.01221324493389968,
+        "acc_stderr": 0.01221324493389968,
+        "prompt_name": "2_fr",
+        "task_name": "crows_pairs_french"
+      }
+    },
+    "crows_pairs_french+3_fr": {
+      "2022-07-12-22-45-57": {
+        "acc": 0.49612403100775193,
+        "acc_norm": 0.49612403100775193,
+        "acc_norm_stderr": 0.012212932249036454,
+        "acc_stderr": 0.012212932249036454,
+        "prompt_name": "3_fr",
+        "task_name": "crows_pairs_french"
+      }
+    },
+    "crows_pairs_french+4_fr": {
+      "2022-07-12-22-45-57": {
+        "acc": 0.5313059033989267,
+        "acc_norm": 0.5313059033989267,
+        "acc_norm_stderr": 0.012189336188399829,
+        "acc_stderr": 0.012189336188399829,
+        "prompt_name": "4_fr",
+        "task_name": "crows_pairs_french"
+      }
+    },
+    "crows_pairs_french+A_preference_fr": {
+      "2022-07-12-22-45-57": {
+        "acc": 0.4847942754919499,
+        "acc_norm": 0.4847942754919499,
+        "acc_norm_stderr": 0.01220765013925874,
+        "acc_stderr": 0.01220765013925874,
+        "prompt_name": "A_preference_fr",
+        "task_name": "crows_pairs_french"
+      }
+    },
+    "crows_pairs_french+A_reality_check_fr": {
+      "2022-07-12-22-45-57": {
+        "acc": 0.505664877757901,
+        "acc_norm": 0.505664877757901,
+        "acc_norm_stderr": 0.012212515323431717,
+        "acc_stderr": 0.012212515323431717,
+        "prompt_name": "A_reality_check_fr",
+        "task_name": "crows_pairs_french"
+      }
+    },
+    "crows_pairs_french+A_stereotype_true_fr": {
+      "2022-07-12-22-45-57": {
+        "acc": 0.5020870602265951,
+        "acc_norm": 0.5020870602265951,
+        "acc_norm_stderr": 0.012213192820312026,
+        "acc_stderr": 0.012213192820312026,
+        "prompt_name": "A_stereotype_true_fr",
+        "task_name": "crows_pairs_french"
+      }
+    },
+    "diabla+Is the error present? (same lang)": {
+      "2022-07-12-22-45-57": {
+        "acc": 0.07741823242867084,
+        "acc_norm": 0.07741823242867084,
+        "acc_norm_stderr": 0.0035253599064790993,
+        "acc_stderr": 0.0035253599064790993,
+        "prompt_name": "Is the error present? (same lang)",
+        "task_name": "diabla"
+      }
+    },
+    "diabla+Which is automatic?": {
+      "2022-07-12-22-45-57": {
+        "acc": 0.4966945024356298,
+        "acc_norm": 0.4966945024356298,
+        "acc_norm_stderr": 0.0065953813991735995,
+        "acc_stderr": 0.0065953813991735995,
+        "prompt_name": "Which is automatic?",
+        "task_name": "diabla"
+      }
+    },
+    "gsarti/flores_101_afr+null": {
+      "2022-07-14-10-03-25": {
+        "bits_per_byte": 2.8175051369933213,
+        "byte_perplexity": 7.049422805555328,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_afr",
+        "word_perplexity": 139324.0466654445
+      }
+    },
+    "gsarti/flores_101_amh+null": {
+      "2022-07-14-10-03-25": {
+        "bits_per_byte": 2.0608666814101815,
+        "byte_perplexity": 4.172368790188039,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_amh",
+        "word_perplexity": 105036774.30501972
+      }
+    },
+    "gsarti/flores_101_ara+null": {
+      "2022-07-14-10-03-25": {
+        "bits_per_byte": 0.8797352167688847,
+        "byte_perplexity": 1.8400375612633983,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_ara",
+        "word_perplexity": 674.8640314665696
+      }
+    },
+    "gsarti/flores_101_asm+null": {
+      "2022-07-14-10-03-25": {
+        "bits_per_byte": 2.458711333673663,
+        "byte_perplexity": 5.497254736157445,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_asm",
+        "word_perplexity": 6763188828222.085
+      }
+    },
+    "gsarti/flores_101_ast+null": {
+      "2022-07-14-10-03-25": {
+        "bits_per_byte": 2.0909386784329675,
+        "byte_perplexity": 4.260251728273795,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_ast",
+        "word_perplexity": 10657.272913539553
+      }
+    },
+    "gsarti/flores_101_azj+null": {
+      "2022-07-14-10-03-25": {
+        "bits_per_byte": 2.9432455349850195,
+        "byte_perplexity": 7.691396328945705,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_azj",
+        "word_perplexity": 45923924.18878753
+      }
+    },
+    "gsarti/flores_101_bel+null": {
+      "2022-07-14-10-03-25": {
+        "bits_per_byte": 1.914816732584341,
+        "byte_perplexity": 3.7706591215465943,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_bel",
+        "word_perplexity": 23935692.781315073
+      }
+    },
+    "gsarti/flores_101_ben+null": {
+      "2022-07-14-10-03-25": {
+        "bits_per_byte": 2.3432036318231058,
+        "byte_perplexity": 5.074281765515423,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_ben",
+        "word_perplexity": 2480418685142.412
+      }
+    },
+    "gsarti/flores_101_bos+null": {
+      "2022-07-14-10-03-25": {
+        "bits_per_byte": 2.665248069942796,
+        "byte_perplexity": 6.343363734045183,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_bos",
+        "word_perplexity": 229622.13691086147
+      }
+    },
+    "gsarti/flores_101_bul+null": {
+      "2022-07-14-10-03-25": {
+        "bits_per_byte": 1.5136770683283687,
+        "byte_perplexity": 2.8553687444403257,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_bul",
+        "word_perplexity": 194851.13344620814
+      }
+    },
+    "gsarti/flores_101_cat+null": {
+      "2022-07-14-10-03-25": {
+        "bits_per_byte": 1.2376904653775254,
+        "byte_perplexity": 2.358207169698056,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_cat",
+        "word_perplexity": 179.13123174533087
+      }
+    },
+    "gsarti/flores_101_ceb+null": {
+      "2022-07-14-10-03-25": {
+        "bits_per_byte": 2.7858604115174295,
+        "byte_perplexity": 6.896481056329736,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_ceb",
+        "word_perplexity": 113330.67154113152
+      }
+    },
+    "gsarti/flores_101_ces+null": {
+      "2022-07-14-10-03-25": {
+        "bits_per_byte": 2.6150694333085327,
+        "byte_perplexity": 6.126526835715164,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_ces",
+        "word_perplexity": 625101.1441414964
+      }
+    },
+    "gsarti/flores_101_ckb+null": {
+      "2022-07-14-12-00-55": {
+        "bits_per_byte": 1.9421776126623524,
+        "byte_perplexity": 3.842852526862475,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_ckb",
+        "word_perplexity": 11104497.438038943
+      }
+    },
+    "gsarti/flores_101_cym+null": {
+      "2022-07-14-12-00-55": {
+        "bits_per_byte": 3.8470317241534553,
+        "byte_perplexity": 14.390369428021707,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_cym",
+        "word_perplexity": 5900331.966242436
+      }
+    },
+    "gsarti/flores_101_dan+null": {
+      "2022-07-14-10-03-25": {
+        "bits_per_byte": 2.5307665257708245,
+        "byte_perplexity": 5.778786323448377,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_dan",
+        "word_perplexity": 71695.50336412797
+      }
+    },
+    "gsarti/flores_101_deu+null": {
+      "2022-07-14-10-03-25": {
+        "bits_per_byte": 1.7492158999678582,
+        "byte_perplexity": 3.361758059911202,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_deu",
+        "word_perplexity": 5647.282599404732
+      }
+    },
+    "gsarti/flores_101_ell+null": {
+      "2022-07-14-10-03-25": {
+        "bits_per_byte": 1.3862374641150543,
+        "byte_perplexity": 2.6139607239932805,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_ell",
+        "word_perplexity": 102751.5248402687
+      }
+    },
+    "gsarti/flores_101_eng+null": {
+      "2022-07-14-10-03-25": {
+        "bits_per_byte": 1.0435427545613876,
+        "byte_perplexity": 2.061283234268159,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_eng",
+        "word_perplexity": 75.56480997823662
+      }
+    },
+    "gsarti/flores_101_est+null": {
+      "2022-07-14-10-03-25": {
+        "bits_per_byte": 3.340809503762674,
+        "byte_perplexity": 10.131736127467489,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_est",
+        "word_perplexity": 92602633.82439691
+      }
+    },
+    "gsarti/flores_101_fas+null": {
+      "2022-07-14-12-00-55": {
+        "bits_per_byte": 1.6586730625582675,
+        "byte_perplexity": 3.1572599808371367,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_fas",
+        "word_perplexity": 59965.98383842629
+      }
+    },
+    "gsarti/flores_101_fin+null": {
+      "2022-07-14-10-03-25": {
+        "bits_per_byte": 2.9093822743068216,
+        "byte_perplexity": 7.5129644427067355,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_fin",
+        "word_perplexity": 91621886.60145952
+      }
+    },
+    "gsarti/flores_101_fra+null": {
+      "2022-07-14-10-03-25": {
+        "bits_per_byte": 1.0127395726746855,
+        "byte_perplexity": 2.0177390037335385,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_fra",
+        "word_perplexity": 89.45884576931464
+      }
+    },
+    "gsarti/flores_101_ful+null": {
+      "2022-07-14-10-03-25": {
+        "bits_per_byte": 3.561969238361191,
+        "byte_perplexity": 11.810263420287875,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_ful",
+        "word_perplexity": 908715.1423017589
+      }
+    },
+    "gsarti/flores_101_gle+null": {
+      "2022-07-14-10-03-25": {
+        "bits_per_byte": 3.2798070331865063,
+        "byte_perplexity": 9.712259930753122,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_gle",
+        "word_perplexity": 1548851.5929806433
+      }
+    },
+    "gsarti/flores_101_glg+null": {
+      "2022-07-14-10-03-25": {
+        "bits_per_byte": 1.6877168009728167,
+        "byte_perplexity": 3.2214647330840154,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_glg",
+        "word_perplexity": 1537.3193913761668
+      }
+    },
+    "gsarti/flores_101_guj+null": {
+      "2022-07-14-10-03-25": {
+        "bits_per_byte": 2.357806609400009,
+        "byte_perplexity": 5.125904532570054,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_guj",
+        "word_perplexity": 133216198508.6925
+      }
+    },
+    "gsarti/flores_101_hau+null": {
+      "2022-07-14-10-03-25": {
+        "bits_per_byte": 3.4659038057537184,
+        "byte_perplexity": 11.049458818357667,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_hau",
+        "word_perplexity": 730749.6449046461
+      }
+    },
+    "gsarti/flores_101_heb+null": {
+      "2022-07-14-10-03-25": {
+        "bits_per_byte": 1.8889611054621571,
+        "byte_perplexity": 3.7036842387723694,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_heb",
+        "word_perplexity": 880255.4148832298
+      }
+    },
+    "gsarti/flores_101_hin+null": {
+      "2022-07-14-10-03-25": {
+        "bits_per_byte": 2.195760704215568,
+        "byte_perplexity": 4.581311639568996,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_hin",
+        "word_perplexity": 453226793.5348556
+      }
+    },
+    "gsarti/flores_101_hrv+null": {
+      "2022-07-14-10-03-25": {
+        "bits_per_byte": 2.7016816564307984,
+        "byte_perplexity": 6.50559790827845,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_hrv",
+        "word_perplexity": 307789.1462790266
+      }
+    },
+    "gsarti/flores_101_hun+null": {
+      "2022-07-14-10-03-25": {
+        "bits_per_byte": 2.8470581600253615,
+        "byte_perplexity": 7.19531655942431,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_hun",
+        "word_perplexity": 8545882.19823639
+      }
+    },
+    "gsarti/flores_101_hye+null": {
+      "2022-07-14-10-03-25": {
+        "bits_per_byte": 1.906169044483402,
+        "byte_perplexity": 3.7481249397064547,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_hye",
+        "word_perplexity": 99262887.01092263
+      }
+    },
+    "gsarti/flores_101_ibo+null": {
+      "2022-07-14-10-03-25": {
+        "bits_per_byte": 2.6012385649422316,
+        "byte_perplexity": 6.06807351892086,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_ibo",
+        "word_perplexity": 99576.38125028457
+      }
+    },
+    "gsarti/flores_101_ind+null": {
+      "2022-07-14-10-03-25": {
+        "bits_per_byte": 1.1501325666473412,
+        "byte_perplexity": 2.2193428661828962,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_ind",
+        "word_perplexity": 299.41864562936706
+      }
+    },
+    "gsarti/flores_101_isl+null": {
+      "2022-07-14-10-03-25": {
+        "bits_per_byte": 3.1394769822824644,
+        "byte_perplexity": 8.812045732299993,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_isl",
+        "word_perplexity": 3947458.536983725
+      }
+    },
+    "gsarti/flores_101_ita+null": {
+      "2022-07-14-10-03-25": {
+        "bits_per_byte": 1.695253347487448,
+        "byte_perplexity": 3.238337491305615,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_ita",
+        "word_perplexity": 1951.0663459405935
+      }
+    },
+    "gsarti/flores_101_jav+null": {
+      "2022-07-14-10-03-25": {
+        "bits_per_byte": 2.899297993680408,
+        "byte_perplexity": 7.460632752007581,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_jav",
+        "word_perplexity": 956961.3940329206
+      }
+    },
+    "gsarti/flores_101_jpn+null": {
+      "2022-07-14-10-03-25": {
+        "bits_per_byte": 1.539549942005635,
+        "byte_perplexity": 2.907038023970581,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_jpn",
+        "word_perplexity": 6.0024027118732196e+69
+      }
+    },
+    "gsarti/flores_101_kam+null": {
+      "2022-07-14-10-03-25": {
+        "bits_per_byte": 3.515626316920499,
+        "byte_perplexity": 11.436917146974627,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_kam",
+        "word_perplexity": 4288601.196402131
+      }
+    },
+    "gsarti/flores_101_kan+null": {
+      "2022-07-14-10-03-25": {
+        "bits_per_byte": 2.3991591199422513,
+        "byte_perplexity": 5.274956219477929,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_kan",
+        "word_perplexity": 5.3861539364992216e+16
+      }
+    },
+    "gsarti/flores_101_kat+null": {
+      "2022-07-14-10-03-25": {
+        "bits_per_byte": 1.3325401608568794,
+        "byte_perplexity": 2.5184571084900518,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_kat",
+        "word_perplexity": 1133105340.614723
+      }
+    },
+    "gsarti/flores_101_kaz+null": {
+      "2022-07-14-10-03-25": {
+        "bits_per_byte": 1.845791322405974,
+        "byte_perplexity": 3.5945005448756477,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_kaz",
+        "word_perplexity": 89537342.10068764
+      }
+    },
+    "gsarti/flores_101_kea+null": {
+      "2022-07-14-10-03-25": {
+        "bits_per_byte": 3.2143692668645976,
+        "byte_perplexity": 9.281572608888562,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_kea",
+        "word_perplexity": 438558.0012817139
+      }
+    },
+    "gsarti/flores_101_kir+null": {
+      "2022-07-14-12-00-55": {
+        "bits_per_byte": 1.9511242166700078,
+        "byte_perplexity": 3.8667573034119127,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_kir",
+        "word_perplexity": 235337758.18519488
+      }
+    },
+    "gsarti/flores_101_kor+null": {
+      "2022-07-14-12-00-55": {
+        "bits_per_byte": 2.023500324792833,
+        "byte_perplexity": 4.065690303705374,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_kor",
+        "word_perplexity": 1684949.6449262113
+      }
+    },
+    "gsarti/flores_101_lao+null": {
+      "2022-07-14-12-00-55": {
+        "bits_per_byte": 1.6376750107826055,
+        "byte_perplexity": 3.1116396826339545,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_lao",
+        "word_perplexity": 3.0817754157127624e+28
+      }
+    },
+    "gsarti/flores_101_lav+null": {
+      "2022-07-14-12-00-55": {
+        "bits_per_byte": 3.075865182775687,
+        "byte_perplexity": 8.431943399753028,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_lav",
+        "word_perplexity": 20692036.880855087
+      }
+    },
+    "gsarti/flores_101_lin+null": {
+      "2022-07-14-12-00-55": {
+        "bits_per_byte": 3.018221991102226,
+        "byte_perplexity": 8.10168498947524,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_lin",
+        "word_perplexity": 259077.7174090486
+      }
+    },
+    "gsarti/flores_101_lit+null": {
+      "2022-07-14-12-00-55": {
+        "bits_per_byte": 3.0526165270213905,
+        "byte_perplexity": 8.297153789252596,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_lit",
+        "word_perplexity": 22011900.13997282
+      }
+    },
+    "gsarti/flores_101_ltz+null": {
+      "2022-07-14-12-00-55": {
+        "bits_per_byte": 3.2407955989852377,
+        "byte_perplexity": 9.453152958003827,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_ltz",
+        "word_perplexity": 6731220.931729273
+      }
+    },
+    "gsarti/flores_101_lug+null": {
+      "2022-07-14-10-03-25": {
+        "bits_per_byte": 3.2150119431528754,
+        "byte_perplexity": 9.285708185212261,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_lug",
+        "word_perplexity": 32046806.791237485
+      }
+    },
+    "gsarti/flores_101_luo+null": {
+      "2022-07-14-12-00-55": {
+        "bits_per_byte": 3.609093857404177,
+        "byte_perplexity": 12.202407052163576,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_luo",
+        "word_perplexity": 1485111.1306447538
+      }
+    },
+    "gsarti/flores_101_mal+null": {
+      "2022-07-14-12-00-55": {
+        "bits_per_byte": 2.1588237245178132,
+        "byte_perplexity": 4.465506197375413,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_mal",
+        "word_perplexity": 4.8990954217696134e+17
+      }
+    },
+    "gsarti/flores_101_mar+null": {
+      "2022-07-14-12-00-55": {
+        "bits_per_byte": 2.454064685835334,
+        "byte_perplexity": 5.479577601103449,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_mar",
+        "word_perplexity": 53348101396468.1
+      }
+    },
+    "gsarti/flores_101_mkd+null": {
+      "2022-07-14-12-00-55": {
+        "bits_per_byte": 1.6388651004482695,
+        "byte_perplexity": 3.11420755589491,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_mkd",
+        "word_perplexity": 513306.31562258815
+      }
+    },
+    "gsarti/flores_101_mlt+null": {
+      "2022-07-14-12-00-55": {
+        "bits_per_byte": 4.014730236310589,
+        "byte_perplexity": 16.164200382975334,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_mlt",
+        "word_perplexity": 3271065298.9525104
+      }
+    },
+    "gsarti/flores_101_mon+null": {
+      "2022-07-14-12-00-55": {
+        "bits_per_byte": 1.8368760183021453,
+        "byte_perplexity": 3.5723563966116956,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_mon",
+        "word_perplexity": 11967156.496346941
+      }
+    },
+    "gsarti/flores_101_mri+null": {
+      "2022-07-14-12-00-55": {
+        "bits_per_byte": 3.037967287223778,
+        "byte_perplexity": 8.213330128288407,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_mri",
+        "word_perplexity": 42667.84366725716
+      }
+    },
+    "gsarti/flores_101_msa+null": {
+      "2022-07-14-12-00-55": {
+        "bits_per_byte": 1.4109363519680242,
+        "byte_perplexity": 2.659096901190639,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_msa",
+        "word_perplexity": 1188.7251531670374
+      }
+    },
+    "gsarti/flores_101_mya+null": {
+      "2022-07-14-10-03-25": {
+        "bits_per_byte": 1.4101030557435918,
+        "byte_perplexity": 2.657561458464019,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_mya",
+        "word_perplexity": 5.887577237013639e+18
+      }
+    },
+    "gsarti/flores_101_nld+null": {
+      "2022-07-14-10-03-25": {
+        "bits_per_byte": 2.1813098607926804,
+        "byte_perplexity": 4.535651709856251,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_nld",
+        "word_perplexity": 13951.877058430618
+      }
+    },
+    "gsarti/flores_101_nob+null": {
+      "2022-07-14-12-00-55": {
+        "bits_per_byte": 2.561165630453858,
+        "byte_perplexity": 5.901843358131797,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_nob",
+        "word_perplexity": 64134.3587194621
+      }
+    },
+    "gsarti/flores_101_npi+null": {
+      "2022-07-14-12-00-55": {
+        "bits_per_byte": 2.361386302448311,
+        "byte_perplexity": 5.138638996619111,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_npi",
+        "word_perplexity": 7452421298650.788
+      }
+    },
+    "gsarti/flores_101_nso+null": {
+      "2022-07-14-12-00-55": {
+        "bits_per_byte": 3.150046187635368,
+        "byte_perplexity": 8.876839962509171,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_nso",
+        "word_perplexity": 133251.3907730927
+      }
+    },
+    "gsarti/flores_101_nya+null": {
+      "2022-07-14-12-00-55": {
+        "bits_per_byte": 3.166160871838487,
+        "byte_perplexity": 8.97654874419086,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_nya",
+        "word_perplexity": 13237249.320560299
+      }
+    },
+    "gsarti/flores_101_oci+null": {
+      "2022-07-14-12-00-55": {
+        "bits_per_byte": 2.3544826611123932,
+        "byte_perplexity": 5.114108118049416,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_oci",
+        "word_perplexity": 29786.57326210068
+      }
+    },
+    "gsarti/flores_101_orm+null": {
+      "2022-07-14-12-00-55": {
+        "bits_per_byte": 3.7457001993717243,
+        "byte_perplexity": 13.414303089263644,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_orm",
+        "word_perplexity": 1286222337.8393624
+      }
+    },
+    "gsarti/flores_101_ory+null": {
+      "2022-07-14-12-00-55": {
+        "bits_per_byte": 2.3466784891528936,
+        "byte_perplexity": 5.086518347981296,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_ory",
+        "word_perplexity": 8232620282886.167
+      }
+    },
+    "gsarti/flores_101_pan+null": {
+      "2022-07-14-12-00-55": {
+        "bits_per_byte": 2.3255600077385723,
+        "byte_perplexity": 5.012603107956229,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_pan",
+        "word_perplexity": 2003582065.835696
+      }
+    },
+    "gsarti/flores_101_pol+null": {
+      "2022-07-14-12-00-55": {
+        "bits_per_byte": 2.3688414865658434,
+        "byte_perplexity": 5.165261846492578,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_pol",
+        "word_perplexity": 239703.75452947227
+      }
+    },
+    "gsarti/flores_101_por+null": {
+      "2022-07-14-12-00-55": {
+        "bits_per_byte": 1.0087385096181816,
+        "byte_perplexity": 2.012150908931838,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_por",
+        "word_perplexity": 78.66129921108659
+      }
+    },
+    "gsarti/flores_101_pus+null": {
+      "2022-07-14-12-00-55": {
+        "bits_per_byte": 2.2173729850313615,
+        "byte_perplexity": 4.650458574106675,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_pus",
+        "word_perplexity": 200303.57214724104
+      }
+    },
+    "gsarti/flores_101_ron+null": {
+      "2022-07-14-12-00-55": {
+        "bits_per_byte": 2.486356022105963,
+        "byte_perplexity": 5.603607947317877,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_ron",
+        "word_perplexity": 80490.92705368399
+      }
+    },
+    "gsarti/flores_101_rus+null": {
+      "2022-07-14-12-00-55": {
+        "bits_per_byte": 1.095728414417906,
+        "byte_perplexity": 2.1372096174466697,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_rus",
+        "word_perplexity": 22038.65288574451
+      }
+    },
+    "gsarti/flores_101_slk+null": {
+      "2022-07-14-12-00-55": {
+        "bits_per_byte": 2.8667803584469502,
+        "byte_perplexity": 7.294354718439043,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_slk",
+        "word_perplexity": 1873211.2703176092
+      }
+    },
+    "gsarti/flores_101_slv+null": {
+      "2022-07-14-12-00-55": {
+        "bits_per_byte": 2.894935550489075,
+        "byte_perplexity": 7.438107250941839,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_slv",
+        "word_perplexity": 609965.8362492598
+      }
+    },
+    "gsarti/flores_101_sna+null": {
+      "2022-07-14-12-00-55": {
+        "bits_per_byte": 3.226698783453375,
+        "byte_perplexity": 9.361234419948593,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_sna",
+        "word_perplexity": 151658287.08006003
+      }
+    },
+    "gsarti/flores_101_snd+null": {
+      "2022-07-14-12-00-55": {
+        "bits_per_byte": 2.505484320885354,
+        "byte_perplexity": 5.678399375652783,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_snd",
+        "word_perplexity": 2195879.0537875695
+      }
+    },
+    "gsarti/flores_101_som+null": {
+      "2022-07-14-12-00-55": {
+        "bits_per_byte": 3.6579492747174616,
+        "byte_perplexity": 12.622705630414286,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_som",
+        "word_perplexity": 12921970.127169678
+      }
+    },
+    "gsarti/flores_101_spa+null": {
+      "2022-07-14-12-00-55": {
+        "bits_per_byte": 0.9441289779054047,
+        "byte_perplexity": 1.9240269109386998,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_spa",
+        "word_perplexity": 55.14408503293887
+      }
+    },
+    "gsarti/flores_101_srp+null": {
+      "2022-07-14-12-00-55": {
+        "bits_per_byte": 1.6091583939601046,
+        "byte_perplexity": 3.050738229673983,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_srp",
+        "word_perplexity": 359037.4163692842
+      }
+    },
+    "gsarti/flores_101_swe+null": {
+      "2022-07-14-12-00-55": {
+        "bits_per_byte": 2.4943222333483153,
+        "byte_perplexity": 5.634635291846611,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_swe",
+        "word_perplexity": 104567.9891705103
+      }
+    },
+    "gsarti/flores_101_swh+null": {
+      "2022-07-14-12-00-55": {
+        "bits_per_byte": 1.9721156771582438,
+        "byte_perplexity": 3.923430589092355,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_swh",
+        "word_perplexity": 6985.646204087442
+      }
+    },
+    "gsarti/flores_101_tam+null": {
+      "2022-07-14-12-00-55": {
+        "bits_per_byte": 2.0999329236632325,
+        "byte_perplexity": 4.286894531607389,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_tam",
+        "word_perplexity": 4220234444737767.0
+      }
+    },
+    "gsarti/flores_101_tel+null": {
+      "2022-07-14-12-00-55": {
+        "bits_per_byte": 2.549014618212334,
+        "byte_perplexity": 5.852344181819556,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_tel",
+        "word_perplexity": 7315913985648022.0
+      }
+    },
+    "gsarti/flores_101_tgk+null": {
+      "2022-07-14-12-00-55": {
+        "bits_per_byte": 1.9399053923480125,
+        "byte_perplexity": 3.836804862794101,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_tgk",
+        "word_perplexity": 10003619.893239152
+      }
+    },
+    "gsarti/flores_101_tgl+null": {
+      "2022-07-14-10-03-25": {
+        "bits_per_byte": 2.645461413001105,
+        "byte_perplexity": 6.256957969905079,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_tgl",
+        "word_perplexity": 87554.31770184237
+      }
+    },
+    "gsarti/flores_101_tha+null": {
+      "2022-07-14-12-00-55": {
+        "bits_per_byte": 1.2979178211163922,
+        "byte_perplexity": 2.458737675753546,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_tha",
+        "word_perplexity": 6.85384626099906e+32
+      }
+    },
+    "gsarti/flores_101_tur+null": {
+      "2022-07-14-12-00-55": {
+        "bits_per_byte": 2.4123830232149,
+        "byte_perplexity": 5.323529328304652,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_tur",
+        "word_perplexity": 1230000.8194755162
+      }
+    },
+    "gsarti/flores_101_ukr+null": {
+      "2022-07-14-12-00-55": {
+        "bits_per_byte": 1.5282644195953918,
+        "byte_perplexity": 2.8843863497020608,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_ukr",
+        "word_perplexity": 780615.9486315987
+      }
+    },
+    "gsarti/flores_101_umb+null": {
+      "2022-07-14-12-00-55": {
+        "bits_per_byte": 3.710219475046473,
+        "byte_perplexity": 13.088423907901921,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_umb",
+        "word_perplexity": 346118506.64866126
+      }
+    },
+    "gsarti/flores_101_urd+null": {
+      "2022-07-14-12-00-55": {
+        "bits_per_byte": 1.0075988539165108,
+        "byte_perplexity": 2.010562039704537,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_urd",
+        "word_perplexity": 335.1943886252716
+      }
+    },
+    "gsarti/flores_101_uzb+null": {
+      "2022-07-14-12-00-55": {
+        "bits_per_byte": 3.69831120498359,
+        "byte_perplexity": 12.980834294137205,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_uzb",
+        "word_perplexity": 1248263505.2751954
+      }
+    },
+    "gsarti/flores_101_vie+null": {
+      "2022-07-14-12-00-55": {
+        "bits_per_byte": 0.8461114961807352,
+        "byte_perplexity": 1.7976491760484148,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_vie",
+        "word_perplexity": 33.51752264232948
+      }
+    },
+    "gsarti/flores_101_wol+null": {
+      "2022-07-14-12-00-55": {
+        "bits_per_byte": 3.332383415073327,
+        "byte_perplexity": 10.072733993132132,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_wol",
+        "word_perplexity": 199684.7010180392
+      }
+    },
+    "gsarti/flores_101_xho+null": {
+      "2022-07-14-12-00-55": {
+        "bits_per_byte": 3.0428982143908727,
+        "byte_perplexity": 8.241450154294917,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_xho",
+        "word_perplexity": 141017733.33017766
+      }
+    },
+    "gsarti/flores_101_yor+null": {
+      "2022-07-14-12-00-55": {
+        "bits_per_byte": 2.62429549091613,
+        "byte_perplexity": 6.165831615133067,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_yor",
+        "word_perplexity": 171980.641422536
+      }
+    },
+    "gsarti/flores_101_zho_simpl+null": {
+      "2022-07-14-10-03-25": {
+        "bits_per_byte": 1.2156521449449949,
+        "byte_perplexity": 2.322457417595381,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_zho_simpl",
+        "word_perplexity": 1.0554528210220222e+21
+      }
+    },
+    "gsarti/flores_101_zho_trad+null": {
+      "2022-07-14-10-03-25": {
+        "bits_per_byte": 1.3622834584784203,
+        "byte_perplexity": 2.5709177552415134,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_zho_trad",
+        "word_perplexity": 4.787781515987923e+24
+      }
+    },
+    "gsarti/flores_101_zul+null": {
+      "2022-07-14-12-00-55": {
+        "bits_per_byte": 3.2020451216662975,
+        "byte_perplexity": 9.202622963132773,
+        "prompt_name": "null",
+        "task_name": "gsarti/flores_101_zul",
+        "word_perplexity": 998742068.9481835
+      }
+    },
+    "headqa": {
+      "2022-07-13-11-29-13": {
+        "acc": 0.25419401896425964,
+        "acc_norm": 0.29576951130561635,
+        "acc_norm_stderr": 0.008717251898361422,
+        "acc_stderr": 0.008316509290190668
+      }
+    },
+    "hellaswag": {
+      "2022-07-13-11-29-13": {
+        "acc": 0.37621987651862177,
+        "acc_norm": 0.46564429396534557,
+        "acc_norm_stderr": 0.004977988452502641,
+        "acc_stderr": 0.004834461997944872
+      }
+    },
+    "lambada": {
+      "2022-07-13-11-29-13": {
+        "acc": 0.46322530564719583,
+        "acc_stderr": 0.006947110835634445,
+        "ppl": 12.583447597222621,
+        "ppl_stderr": 0.4021518609838198
+      }
+    },
+    "logiqa": {
+      "2022-07-13-11-29-13": {
+        "acc": 0.21658986175115208,
+        "acc_norm": 0.28110599078341014,
+        "acc_norm_stderr": 0.017632374626460005,
+        "acc_stderr": 0.016156860583178303
+      }
+    },
+    "mathqa": {
+      "2022-07-13-11-29-13": {
+        "acc": 0.2489112227805695,
+        "acc_norm": 0.2422110552763819,
+        "acc_norm_stderr": 0.007842810183504986,
+        "acc_stderr": 0.007915319798861361
+      }
+    },
+    "mc_taco": {
+      "2022-07-13-11-29-13": {
+        "em": 0.12537537537537538,
+        "f1": 0.4747075325110886
+      }
+    },
+    "mnli+GPT-3 style": {
+      "2022-07-12-22-45-57": {
+        "acc": 0.3564951604686704,
+        "acc_norm": 0.335303107488538,
+        "acc_norm_stderr": 0.004765490263584639,
+        "acc_stderr": 0.004834813222301984,
+        "prompt_name": "GPT-3 style",
+        "task_name": "mnli"
+      }
+    },
+    "mnli+MNLI crowdsource": {
+      "2022-07-12-22-45-57": {
+        "acc": 0.3548650025471218,
+        "acc_norm": 0.37982679572083544,
+        "acc_norm_stderr": 0.004899212442097964,
+        "acc_stderr": 0.004829852406948984,
+        "prompt_name": "MNLI crowdsource",
+        "task_name": "mnli"
+      }
+    },
+    "mnli+always/sometimes/never": {
+      "2022-07-12-22-45-57": {
+        "acc": 0.31920529801324504,
+        "acc_norm": 0.31818644931227713,
+        "acc_norm_stderr": 0.004701653585969693,
+        "acc_stderr": 0.004705655206722177,
+        "prompt_name": "always/sometimes/never",
+        "task_name": "mnli"
+      }
+    },
+    "mnli+based on the previous passage": {
+      "2022-07-12-22-45-57": {
+        "acc": 0.34070300560366784,
+        "acc_norm": 0.33245033112582784,
+        "acc_norm_stderr": 0.004755346314564714,
+        "acc_stderr": 0.004784157883834768,
+        "prompt_name": "based on the previous passage",
+        "task_name": "mnli"
+      }
+    },
+    "mnli+can we infer": {
+      "2022-07-12-22-45-57": {
+        "acc": 0.36271013754457465,
+        "acc_norm": 0.3392766174223128,
+        "acc_norm_stderr": 0.004779294320017342,
+        "acc_stderr": 0.004853167998709484,
+        "prompt_name": "can we infer",
+        "task_name": "mnli"
+      }
+    },
+    "mnli+claim true/false/inconclusive": {
+      "2022-07-12-22-45-57": {
+        "acc": 0.35384615384615387,
+        "acc_norm": 0.3169638308711156,
+        "acc_norm_stderr": 0.004696817414398099,
+        "acc_stderr": 0.004826720820135633,
+        "prompt_name": "claim true/false/inconclusive",
+        "task_name": "mnli"
+      }
+    },
+    "mnli+consider always/sometimes/never": {
+      "2022-07-12-22-45-57": {
+        "acc": 0.3183902190524707,
+        "acc_norm": 0.31818644931227713,
+        "acc_norm_stderr": 0.004701653585969693,
+        "acc_stderr": 0.004702455981984395,
+        "prompt_name": "consider always/sometimes/never",
+        "task_name": "mnli"
+      }
+    },
+    "mnli+does it follow that": {
+      "2022-07-12-22-45-57": {
+        "acc": 0.3784004075394804,
+        "acc_norm": 0.3499745287824758,
+        "acc_norm_stderr": 0.004814601860231488,
+        "acc_stderr": 0.00489562485968904,
+        "prompt_name": "does it follow that",
+        "task_name": "mnli"
+      }
+    },
+    "mnli+does this imply": {
+      "2022-07-12-22-45-57": {
+        "acc": 0.33224656138563424,
+        "acc_norm": 0.31920529801324504,
+        "acc_norm_stderr": 0.004705655206722178,
+        "acc_stderr": 0.004754614244749308,
+        "prompt_name": "does this imply",
+        "task_name": "mnli"
+      }
+    },
+    "mnli+guaranteed true": {
+      "2022-07-12-22-45-57": {
+        "acc": 0.35731023942944473,
+        "acc_norm": 0.3398879266428935,
+        "acc_norm_stderr": 0.004781384619510542,
+        "acc_stderr": 0.004837270730680468,
+        "prompt_name": "guaranteed true",
+        "task_name": "mnli"
+      }
+    },
+    "mnli+guaranteed/possible/impossible": {
+      "2022-07-12-22-45-57": {
+        "acc": 0.32317880794701986,
+        "acc_norm": 0.3390728476821192,
+        "acc_norm_stderr": 0.004778595579555236,
+        "acc_stderr": 0.004721015048648592,
+        "prompt_name": "guaranteed/possible/impossible",
+        "task_name": "mnli"
+      }
+    },
+    "mnli+justified in saying": {
+      "2022-07-12-22-45-57": {
+        "acc": 0.3611818644931228,
+        "acc_norm": 0.33438614365766683,
+        "acc_norm_stderr": 0.004762251055102503,
+        "acc_stderr": 0.0048487362318538965,
+        "prompt_name": "justified in saying",
+        "task_name": "mnli"
+      }
+    },
+    "mnli+must be true": {
+      "2022-07-12-22-45-57": {
+        "acc": 0.3532348446255731,
+        "acc_norm": 0.3400916963830871,
+        "acc_norm_stderr": 0.004782079413482068,
+        "acc_stderr": 0.004824830369595005,
+        "prompt_name": "must be true",
+        "task_name": "mnli"
+      }
+    },
+    "mnli+should assume": {
+      "2022-07-12-22-45-57": {
+        "acc": 0.3532348446255731,
+        "acc_norm": 0.32236372898624555,
+        "acc_norm_stderr": 0.004717896188851781,
+        "acc_stderr": 0.004824830369595005,
+        "prompt_name": "should assume",
+        "task_name": "mnli"
+      }
+    },
+    "mnli+take the following as truth": {
+      "2022-07-12-22-45-57": {
+        "acc": 0.3540499235863474,
+        "acc_norm": 0.32654100866021396,
+        "acc_norm_stderr": 0.004733707466562015,
+        "acc_stderr": 0.004827349052909375,
+        "prompt_name": "take the following as truth",
+        "task_name": "mnli"
+      }
+    },
+    "mnli_mismatched+GPT-3 style": {
+      "2022-07-12-22-45-57": {
+        "acc": 0.3558787632221318,
+        "acc_norm": 0.3365541090317331,
+        "acc_norm_stderr": 0.0047657510794410825,
+        "acc_stderr": 0.004828764189286043,
+        "prompt_name": "GPT-3 style",
+        "task_name": "mnli_mismatched"
+      }
+    },
+    "mnli_mismatched+MNLI crowdsource": {
+      "2022-07-12-22-45-57": {
+        "acc": 0.3524206672091131,
+        "acc_norm": 0.3876118795768918,
+        "acc_norm_stderr": 0.004913750149712027,
+        "acc_stderr": 0.004818127922877737,
+        "prompt_name": "MNLI crowdsource",
+        "task_name": "mnli_mismatched"
+      }
+    },
+    "mnli_mismatched+always/sometimes/never": {
+      "2022-07-12-22-45-57": {
+        "acc": 0.3187550854353133,
+        "acc_norm": 0.318246541903987,
+        "acc_norm_stderr": 0.004697823254367764,
+        "acc_stderr": 0.004699821349212815,
+        "prompt_name": "always/sometimes/never",
+        "task_name": "mnli_mismatched"
+      }
+    },
+    "mnli_mismatched+based on the previous passage": {
+      "2022-07-12-22-45-57": {
+        "acc": 0.3442839707078926,
+        "acc_norm": 0.3240439381611066,
+        "acc_norm_stderr": 0.00472022103875238,
+        "acc_stderr": 0.004792007109263922,
+        "prompt_name": "based on the previous passage",
+        "task_name": "mnli_mismatched"
+      }
+    },
+    "mnli_mismatched+can we infer": {
+      "2022-07-12-22-45-57": {
+        "acc": 0.3628966639544345,
+        "acc_norm": 0.33909682668836455,
+        "acc_norm_stderr": 0.0047745443668395,
+        "acc_stderr": 0.004849506876045877,
+        "prompt_name": "can we infer",
+        "task_name": "mnli_mismatched"
+      }
+    },
+    "mnli_mismatched+claim true/false/inconclusive": {
+      "2022-07-12-22-45-57": {
+        "acc": 0.3517087062652563,
+        "acc_norm": 0.31550040683482505,
+        "acc_norm_stderr": 0.004686921836958016,
+        "acc_stderr": 0.004815903833418159,
+        "prompt_name": "claim true/false/inconclusive",
+        "task_name": "mnli_mismatched"
+      }
+    },
+    "mnli_mismatched+consider always/sometimes/never": {
+      "2022-07-12-22-45-57": {
+        "acc": 0.318246541903987,
+        "acc_norm": 0.318246541903987,
+        "acc_norm_stderr": 0.004697823254367764,
+        "acc_stderr": 0.004697823254367764,
+        "prompt_name": "consider always/sometimes/never",
+        "task_name": "mnli_mismatched"
+      }
+    },
+    "mnli_mismatched+does it follow that": {
+      "2022-07-12-22-45-57": {
+        "acc": 0.38923921887713586,
+        "acc_norm": 0.34926769731489016,
+        "acc_norm_stderr": 0.004808189163919754,
+        "acc_stderr": 0.004917507365149974,
+        "prompt_name": "does it follow that",
+        "task_name": "mnli_mismatched"
+      }
+    },
+    "mnli_mismatched+does this imply": {
+      "2022-07-12-22-45-57": {
+        "acc": 0.3233319772172498,
+        "acc_norm": 0.3184499593165175,
+        "acc_norm_stderr": 0.0046986232661144,
+        "acc_stderr": 0.0047175151956513625,
+        "prompt_name": "does this imply",
+        "task_name": "mnli_mismatched"
+      }
+    },
+    "mnli_mismatched+guaranteed true": {
+      "2022-07-12-22-45-57": {
+        "acc": 0.36208299430431246,
+        "acc_norm": 0.3303498779495525,
+        "acc_norm_stderr": 0.004743645253038162,
+        "acc_stderr": 0.00484715944530685,
+        "prompt_name": "guaranteed true",
+        "task_name": "mnli_mismatched"
+      }
+    },
+    "mnli_mismatched+guaranteed/possible/impossible": {
+      "2022-07-12-22-45-57": {
+        "acc": 0.32048413344182264,
+        "acc_norm": 0.33848657445077296,
+        "acc_norm_stderr": 0.004772448023078353,
+        "acc_stderr": 0.004706566719294992,
+        "prompt_name": "guaranteed/possible/impossible",
+        "task_name": "mnli_mismatched"
+      }
+    },
+    "mnli_mismatched+justified in saying": {
+      "2022-07-12-22-45-57": {
+        "acc": 0.371033360455655,
+        "acc_norm": 0.32648494711147275,
+        "acc_norm_stderr": 0.004729403696523803,
+        "acc_stderr": 0.004872158826748743,
+        "prompt_name": "justified in saying",
+        "task_name": "mnli_mismatched"
+      }
+    },
+    "mnli_mismatched+must be true": {
+      "2022-07-12-22-45-57": {
+        "acc": 0.3565907241659886,
+        "acc_norm": 0.3373677786818552,
+        "acc_norm_stderr": 0.004768581700693004,
+        "acc_stderr": 0.004830919845456573,
+        "prompt_name": "must be true",
+        "task_name": "mnli_mismatched"
+      }
+    },
+    "mnli_mismatched+should assume": {
+      "2022-07-12-22-45-57": {
+        "acc": 0.35740439381611067,
+        "acc_norm": 0.32231489015459724,
+        "acc_norm_stderr": 0.0047136280360736155,
+        "acc_stderr": 0.0048333692129862065,
+        "prompt_name": "should assume",
+        "task_name": "mnli_mismatched"
+      }
+    },
+    "mnli_mismatched+take the following as truth": {
+      "2022-07-12-22-45-57": {
+        "acc": 0.3522172497965826,
+        "acc_norm": 0.3263832384052075,
+        "acc_norm_stderr": 0.004729024000627127,
+        "acc_stderr": 0.004817493665633715,
+        "prompt_name": "take the following as truth",
+        "task_name": "mnli_mismatched"
+      }
+    },
+    "mrpc": {
+      "2022-07-13-11-29-13": {
+        "acc": 0.6813725490196079,
+        "acc_stderr": 0.023095996571841474,
+        "f1": 0.8104956268221574,
+        "f1_stderr": 0.016329211455484924
+      }
+    },
+    "multirc": {
+      "2022-07-13-11-29-13": {
+        "acc": 0.011542497376705142,
+        "acc_stderr": 0.003461867320927179
+      }
+    },
+    "multirc+I was going to say\u2026": {
+      "2022-07-12-22-45-57": {
+        "acc": 0.5082508250825083,
+        "acc_norm": 0.4280115511551155,
+        "acc_norm_stderr": 0.007106976252751536,
+        "acc_stderr": 0.007180825220905937,
+        "prompt_name": "I was going to say\u2026",
+        "task_name": "multirc"
+      }
+    },
+    "multirc+Would it be good to answer\u2026": {
+      "2022-07-12-22-45-57": {
+        "acc": 0.45173267326732675,
+        "acc_norm": 0.4278052805280528,
+        "acc_norm_stderr": 0.007106544557507229,
+        "acc_stderr": 0.007148261386088041,
+        "prompt_name": "Would it be good to answer\u2026",
+        "task_name": "multirc"
+      }
+    },
+    "multirc+confirm": {
+      "2022-07-12-22-45-57": {
+        "acc": 0.4280115511551155,
+        "acc_norm": 0.4280115511551155,
+        "acc_norm_stderr": 0.007106976252751536,
+        "acc_stderr": 0.007106976252751536,
+        "prompt_name": "confirm",
+        "task_name": "multirc"
+      }
+    },
+    "multirc+correct": {
+      "2022-07-12-22-45-57": {
+        "acc": 0.5532178217821783,
+        "acc_norm": 0.4643151815181518,
+        "acc_norm_stderr": 0.00716348904876326,
+        "acc_stderr": 0.007141007544074806,
+        "prompt_name": "correct",
+        "task_name": "multirc"
+      }
+    },
+    "multirc+decide_valid": {
+      "2022-07-12-22-45-57": {
+        "acc": 0.5107260726072608,
+        "acc_norm": 0.4280115511551155,
+        "acc_norm_stderr": 0.007106976252751536,
+        "acc_stderr": 0.007180150402551771,
+        "prompt_name": "decide_valid",
+        "task_name": "multirc"
+      }
+    },
+    "multirc+found_this_answer": {
+      "2022-07-12-22-45-57": {
+        "acc": 0.4278052805280528,
+        "acc_norm": 0.4280115511551155,
+        "acc_norm_stderr": 0.007106976252751536,
+        "acc_stderr": 0.007106544557507229,
+        "prompt_name": "found_this_answer",
+        "task_name": "multirc"
+      }
+    },
+    "multirc+grading": {
+      "2022-07-12-22-45-57": {
+        "acc": 0.429042904290429,
+        "acc_norm": 0.4280115511551155,
+        "acc_norm_stderr": 0.007106976252751536,
+        "acc_stderr": 0.007109115814226985,
+        "prompt_name": "grading",
+        "task_name": "multirc"
+      }
+    },
+    "multirc+is the correct answer\u2026": {
+      "2022-07-12-22-45-57": {
+        "acc": 0.4498762376237624,
+        "acc_norm": 0.4273927392739274,
+        "acc_norm_stderr": 0.007105677382236137,
+        "acc_stderr": 0.0071456249799065185,
+        "prompt_name": "is the correct answer\u2026",
+        "task_name": "multirc"
+      }
+    },
+    "multirc+is\u2026 a correct answer?": {
+      "2022-07-12-22-45-57": {
+        "acc": 0.4278052805280528,
+        "acc_norm": 0.4280115511551155,
+        "acc_norm_stderr": 0.007106976252751536,
+        "acc_stderr": 0.007106544557507229,
+        "prompt_name": "is\u2026 a correct answer?",
+        "task_name": "multirc"
+      }
+    },
+    "multirc+paragraph\u2026 question\u2026 is it\u2026 ?": {
+      "2022-07-12-22-45-57": {
+        "acc": 0.5030940594059405,
+        "acc_norm": 0.42883663366336633,
+        "acc_norm_stderr": 0.007108690423137722,
+        "acc_stderr": 0.007181665598939583,
+        "prompt_name": "paragraph\u2026 question\u2026 is it\u2026 ?",
+        "task_name": "multirc"
+      }
+    },
+    "openbookqa": {
+      "2022-07-13-11-29-13": {
+        "acc": 0.214,
+        "acc_norm": 0.298,
+        "acc_norm_stderr": 0.020475118092988978,
+        "acc_stderr": 0.01835979750238702
+      }
+    },
+    "piqa": {
+      "2022-07-13-11-29-13": {
+        "acc": 0.6871599564744287,
+        "acc_norm": 0.7002176278563657,
+        "acc_norm_stderr": 0.010689686967138092,
+        "acc_stderr": 0.010817714425701112
+      }
+    },
+    "prost": {
+      "2022-07-13-11-29-13": {
+        "acc": 0.23505550811272416,
+        "acc_norm": 0.2670260461144321,
+        "acc_norm_stderr": 0.0032321702981822874,
+        "acc_stderr": 0.0030979423271461875
+      }
+    },
+    "pubmedqa": {
+      "2022-07-13-11-29-13": {
+        "acc": 0.56,
+        "acc_stderr": 0.015704987954361798
+      }
+    },
+    "qnli": {
+      "2022-07-13-11-29-13": {
+        "acc": 0.4962474830679114,
+        "acc_stderr": 0.006765220016415222
+      }
+    },
+    "qqp": {
+      "2022-07-13-11-29-13": {
+        "acc": 0.3681424684640119,
+        "acc_stderr": 0.0023986729832071816,
+        "f1": 0.5381138352498734,
+        "f1_stderr": 0.002555831569895799
+      }
+    },
+    "qqp+answer": {
+      "2022-07-12-22-45-57": {
+        "acc": 0.40558990848379917,
+        "acc_norm": 0.36816720257234725,
+        "acc_norm_stderr": 0.002398706610614492,
+        "acc_stderr": 0.002441969063495092,
+        "prompt_name": "answer",
+        "task_name": "qqp"
+      }
+    },
+    "qqp+duplicate": {
+      "2022-07-12-22-45-57": {
+        "acc": 0.3788523373732377,
+        "acc_norm": 0.36816720257234725,
+        "acc_norm_stderr": 0.002398706610614492,
+        "acc_stderr": 0.002412603277723025,
+        "prompt_name": "duplicate",
+        "task_name": "qqp"
+      }
+    },
+    "qqp+duplicate or not": {
+      "2022-07-13-19-23-37": {
+        "acc": 0.5761315854563444,
+        "acc_norm": 0.6318327974276527,
+        "acc_norm_stderr": 0.002398706610614492,
+        "acc_stderr": 0.0024577056660753426,
+        "prompt_name": "duplicate or not",
+        "task_name": "qqp"
+      }
+    },
+    "qqp+meaning": {
+      "2022-07-13-19-23-37": {
+        "acc": 0.3681424684640119,
+        "acc_norm": 0.36816720257234725,
+        "acc_norm_stderr": 0.002398706610614492,
+        "acc_stderr": 0.0023986729832071916,
+        "prompt_name": "meaning",
+        "task_name": "qqp"
+      }
+    },
+    "qqp+quora": {
+      "2022-07-13-19-23-37": {
+        "acc": 0.36821667078901804,
+        "acc_norm": 0.36816720257234725,
+        "acc_norm_stderr": 0.002398706610614492,
+        "acc_stderr": 0.0023987738450886556,
+        "prompt_name": "quora",
+        "task_name": "qqp"
+      }
+    },
+    "qqp+same thing": {
+      "2022-07-13-19-23-37": {
+        "acc": 0.5099431115508286,
+        "acc_norm": 0.36816720257234725,
+        "acc_norm_stderr": 0.002398706610614492,
+        "acc_stderr": 0.002486208885430481,
+        "prompt_name": "same thing",
+        "task_name": "qqp"
+      }
+    },
+    "race": {
+      "2022-07-13-11-29-13": {
+        "acc": 0.3320574162679426,
+        "acc_stderr": 0.014575582129545914
+      }
+    },
+    "rte": {
+      "2022-07-13-11-29-13": {
+        "acc": 0.5342960288808665,
+        "acc_stderr": 0.030025579819366426
+      }
+    },
+    "rte+does the claim\u2026 follow the fact\u2026": {
+      "2022-07-13-19-23-37": {
+        "acc": 0.4729241877256318,
+        "acc_norm": 0.5270758122743683,
+        "acc_norm_stderr": 0.0300523034631437,
+        "acc_stderr": 0.030052303463143706,
+        "prompt_name": "does the claim\u2026 follow the fact\u2026",
+        "task_name": "rte"
+      }
+    },
+    "rte+entailment explained": {
+      "2022-07-13-19-23-37": {
+        "acc": 0.49458483754512633,
+        "acc_norm": 0.4729241877256318,
+        "acc_norm_stderr": 0.0300523034631437,
+        "acc_stderr": 0.030094698123239966,
+        "prompt_name": "entailment explained",
+        "task_name": "rte"
+      }
+    },
+    "rte+imply": {
+      "2022-07-13-19-23-37": {
+        "acc": 0.48375451263537905,
+        "acc_norm": 0.5270758122743683,
+        "acc_norm_stderr": 0.0300523034631437,
+        "acc_stderr": 0.030080573208738064,
+        "prompt_name": "imply",
+        "task_name": "rte"
+      }
+    },
+    "rte+imply separated": {
+      "2022-07-13-19-23-37": {
+        "acc": 0.45126353790613716,
+        "acc_norm": 0.5270758122743683,
+        "acc_norm_stderr": 0.0300523034631437,
+        "acc_stderr": 0.029953149241808943,
+        "prompt_name": "imply separated",
+        "task_name": "rte"
+      }
+    },
+    "rte+mean": {
+      "2022-07-13-19-23-37": {
+        "acc": 0.48014440433212996,
+        "acc_norm": 0.5270758122743683,
+        "acc_norm_stderr": 0.0300523034631437,
+        "acc_stderr": 0.030072723167317194,
+        "prompt_name": "mean",
+        "task_name": "rte"
+      }
+    },
+    "sciq": {
+      "2022-07-13-11-29-13": {
+        "acc": 0.853,
+        "acc_norm": 0.771,
+        "acc_norm_stderr": 0.013294199326613609,
+        "acc_stderr": 0.011203415395160335
+      }
+    },
+    "sst": {
+      "2022-07-13-11-29-13": {
+        "acc": 0.6823394495412844,
+        "acc_stderr": 0.015775124845202545
+      }
+    },
+    "sst+following positive negative": {
+      "2022-07-13-19-23-37": {
+        "acc": 0.8061926605504587,
+        "acc_norm": 0.8061926605504587,
+        "acc_norm_stderr": 0.013393542261521812,
+        "acc_stderr": 0.013393542261521812,
+        "prompt_name": "following positive negative",
+        "task_name": "sst"
+      }
+    },
+    "sst+happy or mad": {
+      "2022-07-13-19-23-37": {
+        "acc": 0.5091743119266054,
+        "acc_norm": 0.5091743119266054,
+        "acc_norm_stderr": 0.01693900152535154,
+        "acc_stderr": 0.01693900152535154,
+        "prompt_name": "happy or mad",
+        "task_name": "sst"
+      }
+    },
+    "sst+positive negative after": {
+      "2022-07-13-19-23-37": {
+        "acc": 0.6204128440366973,
+        "acc_norm": 0.6204128440366973,
+        "acc_norm_stderr": 0.016443227556688766,
+        "acc_stderr": 0.016443227556688766,
+        "prompt_name": "positive negative after",
+        "task_name": "sst"
+      }
+    },
+    "sst+review": {
+      "2022-07-13-19-23-37": {
+        "acc": 0.5091743119266054,
+        "acc_norm": 0.5091743119266054,
+        "acc_norm_stderr": 0.01693900152535154,
+        "acc_stderr": 0.01693900152535154,
+        "prompt_name": "review",
+        "task_name": "sst"
+      }
+    },
+    "sst+said": {
+      "2022-07-13-19-23-37": {
+        "acc": 0.4908256880733945,
+        "acc_norm": 0.5091743119266054,
+        "acc_norm_stderr": 0.01693900152535154,
+        "acc_stderr": 0.01693900152535154,
+        "prompt_name": "said",
+        "task_name": "sst"
+      }
+    },
+    "triviaqa": {
+      "2022-07-13-11-29-13": {
+        "acc": 0.0313798285158667,
+        "acc_stderr": 0.0016392014864795154
+      }
+    },
+    "webqs": {
+      "2022-07-13-11-29-13": {
+        "acc": 0.012795275590551181,
+        "acc_stderr": 0.0024938680596856277
+      }
+    },
+    "wic": {
+      "2022-07-13-11-29-13": {
+        "acc": 0.5,
+        "acc_stderr": 0.01981072129375818
+      }
+    },
+    "wic+GPT-3-prompt": {
+      "2022-07-14-10-03-25": {
+        "acc": 0.5,
+        "acc_norm": 0.5,
+        "acc_norm_stderr": 0.01981072129375818,
+        "acc_stderr": 0.01981072129375818,
+        "prompt_name": "GPT-3-prompt",
+        "task_name": "wic"
+      }
+    },
+    "wic+GPT-3-prompt-with-label": {
+      "2022-07-14-10-03-25": {
+        "acc": 0.49216300940438873,
+        "acc_norm": 0.5,
+        "acc_norm_stderr": 0.01981072129375818,
+        "acc_stderr": 0.019808287657813832,
+        "prompt_name": "GPT-3-prompt-with-label",
+        "task_name": "wic"
+      }
+    },
+    "wic+affirmation_true_or_false": {
+      "2022-07-14-10-03-25": {
+        "acc": 0.5,
+        "acc_norm": 0.5078369905956113,
+        "acc_norm_stderr": 0.019808287657813832,
+        "acc_stderr": 0.01981072129375818,
+        "prompt_name": "affirmation_true_or_false",
+        "task_name": "wic"
+      }
+    },
+    "wic+grammar_homework": {
+      "2022-07-14-10-03-25": {
+        "acc": 0.5094043887147336,
+        "acc_norm": 0.49843260188087773,
+        "acc_norm_stderr": 0.019810623954060382,
+        "acc_stderr": 0.019807216763271497,
+        "prompt_name": "grammar_homework",
+        "task_name": "wic"
+      }
+    },
+    "wic+polysemous": {
+      "2022-07-14-10-03-25": {
+        "acc": 0.512539184952978,
+        "acc_norm": 0.49843260188087773,
+        "acc_norm_stderr": 0.019810623954060382,
+        "acc_stderr": 0.019804490588592596,
+        "prompt_name": "polysemous",
+        "task_name": "wic"
+      }
+    },
+    "wic+question-context": {
+      "2022-07-14-10-03-25": {
+        "acc": 0.5266457680250783,
+        "acc_norm": 0.5031347962382445,
+        "acc_norm_stderr": 0.019810331932097542,
+        "acc_stderr": 0.019782570188812167,
+        "prompt_name": "question-context",
+        "task_name": "wic"
+      }
+    },
+    "wic+question-context-meaning": {
+      "2022-07-14-10-03-25": {
+        "acc": 0.5438871473354232,
+        "acc_norm": 0.5015673981191222,
+        "acc_norm_stderr": 0.019810623954060382,
+        "acc_stderr": 0.019734259601993404,
+        "prompt_name": "question-context-meaning",
+        "task_name": "wic"
+      }
+    },
+    "wic+question-context-meaning-with-label": {
+      "2022-07-14-10-03-25": {
+        "acc": 0.5156739811912225,
+        "acc_norm": 0.5015673981191222,
+        "acc_norm_stderr": 0.019810623954060382,
+        "acc_stderr": 0.019800984955347847,
+        "prompt_name": "question-context-meaning-with-label",
+        "task_name": "wic"
+      }
+    },
+    "wic+same_sense": {
+      "2022-07-14-10-03-25": {
+        "acc": 0.5047021943573667,
+        "acc_norm": 0.5,
+        "acc_norm_stderr": 0.01981072129375818,
+        "acc_stderr": 0.019809845219259763,
+        "prompt_name": "same_sense",
+        "task_name": "wic"
+      }
+    },
+    "wic+similar-sense": {
+      "2022-07-14-10-03-25": {
+        "acc": 0.542319749216301,
+        "acc_norm": 0.5,
+        "acc_norm_stderr": 0.01981072129375818,
+        "acc_stderr": 0.01973963328373276,
+        "prompt_name": "similar-sense",
+        "task_name": "wic"
+      }
+    },
+    "winogrande": {
+      "2022-07-13-11-29-13": {
+        "acc": 0.5730071033938438,
+        "acc_stderr": 0.013901878072575058
+      }
+    },
+    "wnli": {
+      "2022-07-13-11-29-13": {
+        "acc": 0.43661971830985913,
+        "acc_stderr": 0.0592793555841297
+      }
+    },
+    "wnli+confident": {
+      "2022-07-14-10-03-25": {
+        "acc": 0.43661971830985913,
+        "acc_norm": 0.43661971830985913,
+        "acc_norm_stderr": 0.0592793555841297,
+        "acc_stderr": 0.0592793555841297,
+        "prompt_name": "confident",
+        "task_name": "wnli"
+      }
+    },
+    "wnli+entailment explained": {
+      "2022-07-14-10-03-25": {
+        "acc": 0.39436619718309857,
+        "acc_norm": 0.43661971830985913,
+        "acc_norm_stderr": 0.0592793555841297,
+        "acc_stderr": 0.058412510854444266,
+        "prompt_name": "entailment explained",
+        "task_name": "wnli"
+      }
+    },
+    "wnli+imply": {
+      "2022-07-14-10-03-25": {
+        "acc": 0.4225352112676056,
+        "acc_norm": 0.43661971830985913,
+        "acc_norm_stderr": 0.0592793555841297,
+        "acc_stderr": 0.05903984205682581,
+        "prompt_name": "imply",
+        "task_name": "wnli"
+      }
+    },
+    "wnli+justified": {
+      "2022-07-14-10-03-25": {
+        "acc": 0.43661971830985913,
+        "acc_norm": 0.43661971830985913,
+        "acc_norm_stderr": 0.0592793555841297,
+        "acc_stderr": 0.0592793555841297,
+        "prompt_name": "justified",
+        "task_name": "wnli"
+      }
+    },
+    "wnli+mean": {
+      "2022-07-14-10-03-25": {
+        "acc": 0.6619718309859155,
+        "acc_norm": 0.43661971830985913,
+        "acc_norm_stderr": 0.0592793555841297,
+        "acc_stderr": 0.05653887739133513,
+        "prompt_name": "mean",
+        "task_name": "wnli"
+      }
+    },
+    "wsc": {
+      "2022-07-13-11-29-13": {
+        "acc": 0.36538461538461536,
+        "acc_stderr": 0.0474473339327792
+      }
+    },
+    "wsc+GPT-3 Style": {
+      "2022-07-14-10-03-25": {
+        "acc": 0.36538461538461536,
+        "acc_norm": 0.36538461538461536,
+        "acc_norm_stderr": 0.0474473339327792,
+        "acc_stderr": 0.0474473339327792,
+        "prompt_name": "GPT-3 Style",
+        "task_name": "wsc"
+      }
+    },
+    "wsc+I think they mean": {
+      "2022-07-14-10-03-25": {
+        "acc": 0.36538461538461536,
+        "acc_norm": 0.36538461538461536,
+        "acc_norm_stderr": 0.0474473339327792,
+        "acc_stderr": 0.0474473339327792,
+        "prompt_name": "I think they mean",
+        "task_name": "wsc"
+      }
+    },
+    "wsc+Who or what is/are": {
+      "2022-07-14-10-03-25": {
+        "acc": 0.40384615384615385,
+        "acc_norm": 0.36538461538461536,
+        "acc_norm_stderr": 0.0474473339327792,
+        "acc_stderr": 0.048346889526540184,
+        "prompt_name": "Who or what is/are",
+        "task_name": "wsc"
+      }
+    },
+    "wsc+by p they mean": {
+      "2022-07-14-10-03-25": {
+        "acc": 0.36538461538461536,
+        "acc_norm": 0.36538461538461536,
+        "acc_norm_stderr": 0.0474473339327792,
+        "acc_stderr": 0.0474473339327792,
+        "prompt_name": "by p they mean",
+        "task_name": "wsc"
+      }
+    },
+    "wsc+does p stand for": {
+      "2022-07-14-10-03-25": {
+        "acc": 0.375,
+        "acc_norm": 0.36538461538461536,
+        "acc_norm_stderr": 0.0474473339327792,
+        "acc_stderr": 0.04770204856076104,
+        "prompt_name": "does p stand for",
+        "task_name": "wsc"
+      }
+    },
+    "wsc+does the pronoun refer to": {
+      "2022-07-14-10-03-25": {
+        "acc": 0.5480769230769231,
+        "acc_norm": 0.36538461538461536,
+        "acc_norm_stderr": 0.0474473339327792,
+        "acc_stderr": 0.049038186969314335,
+        "prompt_name": "does the pronoun refer to",
+        "task_name": "wsc"
+      }
+    },
+    "wsc+in other words": {
+      "2022-07-14-10-03-25": {
+        "acc": 0.36538461538461536,
+        "acc_norm": 0.5288461538461539,
+        "acc_norm_stderr": 0.04918440626354964,
+        "acc_stderr": 0.0474473339327792,
+        "prompt_name": "in other words",
+        "task_name": "wsc"
+      }
+    },
+    "wsc+p is/are r": {
+      "2022-07-14-10-03-25": {
+        "acc": 0.36538461538461536,
+        "acc_norm": 0.34615384615384615,
+        "acc_norm_stderr": 0.04687634642174987,
+        "acc_stderr": 0.0474473339327792,
+        "prompt_name": "p is/are r",
+        "task_name": "wsc"
+      }
+    },
+    "wsc+replaced with": {
+      "2022-07-14-10-03-25": {
+        "acc": 0.6153846153846154,
+        "acc_norm": 0.36538461538461536,
+        "acc_norm_stderr": 0.0474473339327792,
+        "acc_stderr": 0.047936688680750406,
+        "prompt_name": "replaced with",
+        "task_name": "wsc"
+      }
+    },
+    "wsc+the pronoun refers to": {
+      "2022-07-14-10-03-25": {
+        "acc": 0.36538461538461536,
+        "acc_norm": 0.5865384615384616,
+        "acc_norm_stderr": 0.04852294969729053,
+        "acc_stderr": 0.0474473339327792,
+        "prompt_name": "the pronoun refers to",
+        "task_name": "wsc"
+      }
+    }
+  },
+  "versions": {
+    "arc_challenge": 0,
+    "arc_easy": 0,
+    "axb+GPT-3 style": 0,
+    "axb+MNLI crowdsource": 0,
+    "axb+based on the previous passage": 0,
+    "axb+can we infer": 0,
+    "axb+does it follow that": 0,
+    "axb+does this imply": 0,
+    "axb+guaranteed true": 0,
+    "axb+justified in saying": 0,
+    "axb+must be true": 0,
+    "axb+should assume": 0,
+    "axg+GPT-3 style": 0,
+    "axg+MNLI crowdsource": 0,
+    "axg+based on the previous passage": 0,
+    "axg+can we infer": 0,
+    "axg+does it follow that": 0,
+    "axg+does this imply": 0,
+    "axg+guaranteed true": 0,
+    "axg+justified in saying": 0,
+    "axg+must be true": 0,
+    "axg+should assume": 0,
+    "boolq": 1,
+    "boolq+GPT-3 Style": 0,
+    "boolq+I wonder\u2026": 0,
+    "boolq+after_reading": 0,
+    "boolq+based on the following passage": 0,
+    "boolq+based on the previous passage": 0,
+    "boolq+could you tell me\u2026": 0,
+    "boolq+exam": 0,
+    "boolq+exercise": 0,
+    "boolq+valid_binary": 0,
+    "boolq+yes_no_question": 0,
+    "cb+GPT-3 style": 0,
+    "cb+MNLI crowdsource": 0,
+    "cb+always/sometimes/never": 0,
+    "cb+based on the previous passage": 0,
+    "cb+can we infer": 0,
+    "cb+claim true/false/inconclusive": 0,
+    "cb+consider always/sometimes/never": 0,
+    "cb+does it follow that": 0,
+    "cb+does this imply": 0,
+    "cb+guaranteed true": 0,
+    "cb+guaranteed/possible/impossible": 0,
+    "cb+justified in saying": 0,
+    "cb+must be true": 0,
+    "cb+should assume": 0,
+    "cb+take the following as truth": 0,
+    "cola+Following sentence acceptable": 0,
+    "cola+Make sense yes no": 0,
+    "cola+Previous sentence acceptable": 0,
+    "cola+editing": 0,
+    "cola+is_this_correct": 0,
+    "copa": 0,
+    "copa+C1 or C2? premise, so/because\u2026": 0,
+    "copa+best_option": 0,
+    "copa+cause_effect": 0,
+    "copa+choose": 0,
+    "copa+exercise": 0,
+    "copa+i_am_hesitating": 0,
+    "copa+more likely": 0,
+    "copa+plausible_alternatives": 0,
+    "crows_pairs_english+1": 0,
+    "crows_pairs_english+2": 0,
+    "crows_pairs_english+3": 0,
+    "crows_pairs_english+4": 0,
+    "crows_pairs_english+A_preference": 0,
+    "crows_pairs_english+A_reality_check": 0,
+    "crows_pairs_english+A_stereotype_true": 0,
+    "crows_pairs_french+1_fr": 0,
+    "crows_pairs_french+2_fr": 0,
+    "crows_pairs_french+3_fr": 0,
+    "crows_pairs_french+4_fr": 0,
+    "crows_pairs_french+A_preference_fr": 0,
+    "crows_pairs_french+A_reality_check_fr": 0,
+    "crows_pairs_french+A_stereotype_true_fr": 0,
+    "diabla+Is the error present? (same lang)": 0,
+    "diabla+Which is automatic?": 0,
+    "gsarti/flores_101_afr+null": 0,
+    "gsarti/flores_101_amh+null": 0,
+    "gsarti/flores_101_ara+null": 0,
+    "gsarti/flores_101_asm+null": 0,
+    "gsarti/flores_101_ast+null": 0,
+    "gsarti/flores_101_azj+null": 0,
+    "gsarti/flores_101_bel+null": 0,
+    "gsarti/flores_101_ben+null": 0,
+    "gsarti/flores_101_bos+null": 0,
+    "gsarti/flores_101_bul+null": 0,
+    "gsarti/flores_101_cat+null": 0,
+    "gsarti/flores_101_ceb+null": 0,
+    "gsarti/flores_101_ces+null": 0,
+    "gsarti/flores_101_ckb+null": 0,
+    "gsarti/flores_101_cym+null": 0,
+    "gsarti/flores_101_dan+null": 0,
+    "gsarti/flores_101_deu+null": 0,
+    "gsarti/flores_101_ell+null": 0,
+    "gsarti/flores_101_eng+null": 0,
+    "gsarti/flores_101_est+null": 0,
+    "gsarti/flores_101_fas+null": 0,
+    "gsarti/flores_101_fin+null": 0,
+    "gsarti/flores_101_fra+null": 0,
+    "gsarti/flores_101_ful+null": 0,
+    "gsarti/flores_101_gle+null": 0,
+    "gsarti/flores_101_glg+null": 0,
+    "gsarti/flores_101_guj+null": 0,
+    "gsarti/flores_101_hau+null": 0,
+    "gsarti/flores_101_heb+null": 0,
+    "gsarti/flores_101_hin+null": 0,
+    "gsarti/flores_101_hrv+null": 0,
+    "gsarti/flores_101_hun+null": 0,
+    "gsarti/flores_101_hye+null": 0,
+    "gsarti/flores_101_ibo+null": 0,
+    "gsarti/flores_101_ind+null": 0,
+    "gsarti/flores_101_isl+null": 0,
+    "gsarti/flores_101_ita+null": 0,
+    "gsarti/flores_101_jav+null": 0,
+    "gsarti/flores_101_jpn+null": 0,
+    "gsarti/flores_101_kam+null": 0,
+    "gsarti/flores_101_kan+null": 0,
+    "gsarti/flores_101_kat+null": 0,
+    "gsarti/flores_101_kaz+null": 0,
+    "gsarti/flores_101_kea+null": 0,
+    "gsarti/flores_101_kir+null": 0,
+    "gsarti/flores_101_kor+null": 0,
+    "gsarti/flores_101_lao+null": 0,
+    "gsarti/flores_101_lav+null": 0,
+    "gsarti/flores_101_lin+null": 0,
+    "gsarti/flores_101_lit+null": 0,
+    "gsarti/flores_101_ltz+null": 0,
+    "gsarti/flores_101_lug+null": 0,
+    "gsarti/flores_101_luo+null": 0,
+    "gsarti/flores_101_mal+null": 0,
+    "gsarti/flores_101_mar+null": 0,
+    "gsarti/flores_101_mkd+null": 0,
+    "gsarti/flores_101_mlt+null": 0,
+    "gsarti/flores_101_mon+null": 0,
+    "gsarti/flores_101_mri+null": 0,
+    "gsarti/flores_101_msa+null": 0,
+    "gsarti/flores_101_mya+null": 0,
+    "gsarti/flores_101_nld+null": 0,
+    "gsarti/flores_101_nob+null": 0,
+    "gsarti/flores_101_npi+null": 0,
+    "gsarti/flores_101_nso+null": 0,
+    "gsarti/flores_101_nya+null": 0,
+    "gsarti/flores_101_oci+null": 0,
+    "gsarti/flores_101_orm+null": 0,
+    "gsarti/flores_101_ory+null": 0,
+    "gsarti/flores_101_pan+null": 0,
+    "gsarti/flores_101_pol+null": 0,
+    "gsarti/flores_101_por+null": 0,
+    "gsarti/flores_101_pus+null": 0,
+    "gsarti/flores_101_ron+null": 0,
+    "gsarti/flores_101_rus+null": 0,
+    "gsarti/flores_101_slk+null": 0,
+    "gsarti/flores_101_slv+null": 0,
+    "gsarti/flores_101_sna+null": 0,
+    "gsarti/flores_101_snd+null": 0,
+    "gsarti/flores_101_som+null": 0,
+    "gsarti/flores_101_spa+null": 0,
+    "gsarti/flores_101_srp+null": 0,
+    "gsarti/flores_101_swe+null": 0,
+    "gsarti/flores_101_swh+null": 0,
+    "gsarti/flores_101_tam+null": 0,
+    "gsarti/flores_101_tel+null": 0,
+    "gsarti/flores_101_tgk+null": 0,
+    "gsarti/flores_101_tgl+null": 0,
+    "gsarti/flores_101_tha+null": 0,
+    "gsarti/flores_101_tur+null": 0,
+    "gsarti/flores_101_ukr+null": 0,
+    "gsarti/flores_101_umb+null": 0,
+    "gsarti/flores_101_urd+null": 0,
+    "gsarti/flores_101_uzb+null": 0,
+    "gsarti/flores_101_vie+null": 0,
+    "gsarti/flores_101_wol+null": 0,
+    "gsarti/flores_101_xho+null": 0,
+    "gsarti/flores_101_yor+null": 0,
+    "gsarti/flores_101_zho_simpl+null": 0,
+    "gsarti/flores_101_zho_trad+null": 0,
+    "gsarti/flores_101_zul+null": 0,
+    "headqa": 0,
+    "hellaswag": 0,
+    "lambada": 0,
+    "logiqa": 0,
+    "mathqa": 0,
+    "mc_taco": 0,
+    "mnli+GPT-3 style": 0,
+    "mnli+MNLI crowdsource": 0,
+    "mnli+always/sometimes/never": 0,
+    "mnli+based on the previous passage": 0,
+    "mnli+can we infer": 0,
+    "mnli+claim true/false/inconclusive": 0,
+    "mnli+consider always/sometimes/never": 0,
+    "mnli+does it follow that": 0,
+    "mnli+does this imply": 0,
+    "mnli+guaranteed true": 0,
+    "mnli+guaranteed/possible/impossible": 0,
+    "mnli+justified in saying": 0,
+    "mnli+must be true": 0,
+    "mnli+should assume": 0,
+    "mnli+take the following as truth": 0,
+    "mnli_mismatched+GPT-3 style": 0,
+    "mnli_mismatched+MNLI crowdsource": 0,
+    "mnli_mismatched+always/sometimes/never": 0,
+    "mnli_mismatched+based on the previous passage": 0,
+    "mnli_mismatched+can we infer": 0,
+    "mnli_mismatched+claim true/false/inconclusive": 0,
+    "mnli_mismatched+consider always/sometimes/never": 0,
+    "mnli_mismatched+does it follow that": 0,
+    "mnli_mismatched+does this imply": 0,
+    "mnli_mismatched+guaranteed true": 0,
+    "mnli_mismatched+guaranteed/possible/impossible": 0,
+    "mnli_mismatched+justified in saying": 0,
+    "mnli_mismatched+must be true": 0,
+    "mnli_mismatched+should assume": 0,
+    "mnli_mismatched+take the following as truth": 0,
+    "mrpc": 0,
+    "multirc": 1,
+    "multirc+I was going to say\u2026": 0,
+    "multirc+Would it be good to answer\u2026": 0,
+    "multirc+confirm": 0,
+    "multirc+correct": 0,
+    "multirc+decide_valid": 0,
+    "multirc+found_this_answer": 0,
+    "multirc+grading": 0,
+    "multirc+is the correct answer\u2026": 0,
+    "multirc+is\u2026 a correct answer?": 0,
+    "multirc+paragraph\u2026 question\u2026 is it\u2026 ?": 0,
+    "openbookqa": 0,
+    "piqa": 0,
+    "prost": 0,
+    "pubmedqa": 0,
+    "qnli": 0,
+    "qqp": 0,
+    "qqp+answer": 0,
+    "qqp+duplicate": 0,
+    "qqp+duplicate or not": 0,
+    "qqp+meaning": 0,
+    "qqp+quora": 0,
+    "qqp+same thing": 0,
+    "race": 1,
+    "rte": 0,
+    "rte+does the claim\u2026 follow the fact\u2026": 0,
+    "rte+entailment explained": 0,
+    "rte+imply": 0,
+    "rte+imply separated": 0,
+    "rte+mean": 0,
+    "sciq": 0,
+    "sst": 0,
+    "sst+following positive negative": 0,
+    "sst+happy or mad": 0,
+    "sst+positive negative after": 0,
+    "sst+review": 0,
+    "sst+said": 0,
+    "triviaqa": 0,
+    "webqs": 0,
+    "wic": 0,
+    "wic+GPT-3-prompt": 0,
+    "wic+GPT-3-prompt-with-label": 0,
+    "wic+affirmation_true_or_false": 0,
+    "wic+grammar_homework": 0,
+    "wic+polysemous": 0,
+    "wic+question-context": 0,
+    "wic+question-context-meaning": 0,
+    "wic+question-context-meaning-with-label": 0,
+    "wic+same_sense": 0,
+    "wic+similar-sense": 0,
+    "winogrande": 0,
+    "wnli": 1,
+    "wnli+confident": 1,
+    "wnli+entailment explained": 1,
+    "wnli+imply": 1,
+    "wnli+justified": 1,
+    "wnli+mean": 1,
+    "wsc": 0,
+    "wsc+GPT-3 Style": 0,
+    "wsc+I think they mean": 0,
+    "wsc+Who or what is/are": 0,
+    "wsc+by p they mean": 0,
+    "wsc+does p stand for": 0,
+    "wsc+does the pronoun refer to": 0,
+    "wsc+in other words": 0,
+    "wsc+p is/are r": 0,
+    "wsc+replaced with": 0,
+    "wsc+the pronoun refers to": 0
+  }
+}

evaluation/results/tr11/bloom1b3/bslmevalfiles/tr11-1b3-ml-evalharness-results_lm-eval_global_step340500_2022-07-13-11-29-13.json

@@ -0,0 +1,172 @@
+{
+    "results": {
+        "arc_challenge": {
+            "acc": 0.23464163822525597,
+            "acc_stderr": 0.012383873560768673,
+            "acc_norm": 0.26791808873720135,
+            "acc_norm_stderr": 0.012942030195136423
+        },
+        "arc_easy": {
+            "acc": 0.5631313131313131,
+            "acc_stderr": 0.010177672928157678,
+            "acc_norm": 0.4810606060606061,
+            "acc_norm_stderr": 0.010252420496894487
+        },
+        "boolq": {
+            "acc": 0.617737003058104,
+            "acc_stderr": 0.008499149690449272
+        },
+        "copa": {
+            "acc": 0.7,
+            "acc_stderr": 0.046056618647183814
+        },
+        "headqa": {
+            "acc": 0.25419401896425964,
+            "acc_stderr": 0.008316509290190668,
+            "acc_norm": 0.29576951130561635,
+            "acc_norm_stderr": 0.008717251898361422
+        },
+        "hellaswag": {
+            "acc": 0.37621987651862177,
+            "acc_stderr": 0.004834461997944872,
+            "acc_norm": 0.46564429396534557,
+            "acc_norm_stderr": 0.004977988452502641
+        },
+        "lambada": {
+            "ppl": 12.583447597222621,
+            "ppl_stderr": 0.4021518609838198,
+            "acc": 0.46322530564719583,
+            "acc_stderr": 0.006947110835634445
+        },
+        "logiqa": {
+            "acc": 0.21658986175115208,
+            "acc_stderr": 0.016156860583178303,
+            "acc_norm": 0.28110599078341014,
+            "acc_norm_stderr": 0.017632374626460005
+        },
+        "mathqa": {
+            "acc": 0.2489112227805695,
+            "acc_stderr": 0.007915319798861361,
+            "acc_norm": 0.2422110552763819,
+            "acc_norm_stderr": 0.007842810183504986
+        },
+        "mc_taco": {
+            "em": 0.12537537537537538,
+            "f1": 0.4747075325110886
+        },
+        "mrpc": {
+            "acc": 0.6813725490196079,
+            "acc_stderr": 0.023095996571841474,
+            "f1": 0.8104956268221574,
+            "f1_stderr": 0.016329211455484924
+        },
+        "multirc": {
+            "acc": 0.011542497376705142,
+            "acc_stderr": 0.003461867320927179
+        },
+        "openbookqa": {
+            "acc": 0.214,
+            "acc_stderr": 0.01835979750238702,
+            "acc_norm": 0.298,
+            "acc_norm_stderr": 0.020475118092988978
+        },
+        "piqa": {
+            "acc": 0.6871599564744287,
+            "acc_stderr": 0.010817714425701112,
+            "acc_norm": 0.7002176278563657,
+            "acc_norm_stderr": 0.010689686967138092
+        },
+        "prost": {
+            "acc": 0.23505550811272416,
+            "acc_stderr": 0.0030979423271461875,
+            "acc_norm": 0.2670260461144321,
+            "acc_norm_stderr": 0.0032321702981822874
+        },
+        "pubmedqa": {
+            "acc": 0.56,
+            "acc_stderr": 0.015704987954361798
+        },
+        "qnli": {
+            "acc": 0.4962474830679114,
+            "acc_stderr": 0.006765220016415222
+        },
+        "qqp": {
+            "acc": 0.3681424684640119,
+            "acc_stderr": 0.0023986729832071816,
+            "f1": 0.5381138352498734,
+            "f1_stderr": 0.002555831569895799
+        },
+        "race": {
+            "acc": 0.3320574162679426,
+            "acc_stderr": 0.014575582129545914
+        },
+        "rte": {
+            "acc": 0.5342960288808665,
+            "acc_stderr": 0.030025579819366426
+        },
+        "sciq": {
+            "acc": 0.853,
+            "acc_stderr": 0.011203415395160335,
+            "acc_norm": 0.771,
+            "acc_norm_stderr": 0.013294199326613609
+        },
+        "sst": {
+            "acc": 0.6823394495412844,
+            "acc_stderr": 0.015775124845202545
+        },
+        "triviaqa": {
+            "acc": 0.0313798285158667,
+            "acc_stderr": 0.0016392014864795154
+        },
+        "webqs": {
+            "acc": 0.012795275590551181,
+            "acc_stderr": 0.0024938680596856277
+        },
+        "wic": {
+            "acc": 0.5,
+            "acc_stderr": 0.01981072129375818
+        },
+        "winogrande": {
+            "acc": 0.5730071033938438,
+            "acc_stderr": 0.013901878072575058
+        },
+        "wnli": {
+            "acc": 0.43661971830985913,
+            "acc_stderr": 0.0592793555841297
+        },
+        "wsc": {
+            "acc": 0.36538461538461536,
+            "acc_stderr": 0.0474473339327792
+        }
+    },
+    "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/bloom1b3/humaneval_temp02.json

@@ -0,0 +1 @@
+{"pass@1": 0.04033536585365854, "pass@10": 0.06579071150715766, "pass@100": 0.08764228719065376}

evaluation/results/tr11/bloom1b3/humaneval_temp06.json

@@ -0,0 +1 @@
+{"pass@1": 0.031249999999999993, "pass@10": 0.07447701667197712, "pass@100": 0.1253791767704454}

evaluation/results/tr11/bloom1b3/humaneval_temp08.json

@@ -0,0 +1 @@
+{"pass@1": 0.023475609756097564, "pass@10": 0.06591235746713595, "pass@100": 0.12748827115496364}

evaluation/results/tr11/bloom2b5/bslmevalfiles/tr11c-2b5-ml-bsevalharness-results_lm-eval_global_step337250_2022-07-13-19-42-29.json

@@ -0,0 +1,1917 @@
+{
+    "results": [
+        {
+            "task_name": "multirc",
+            "prompt_name": "I was going to say\u2026",
+            "acc": 0.5724009900990099,
+            "fixed_answer_choice_list": [
+                "No",
+                "Yes"
+            ],
+            "dataset_path": "super_glue",
+            "dataset_name": "multirc",
+            "subset": null,
+            "prompt_id": "d2d78b88-8845-45b5-935a-6451da00b285",
+            "prompt_jinja": "{{ paragraph }}\n{{ question }} \nI was going to say \"{{ answer }}\". Does that sound right? ||| {% if label != -1 %}{{ answer_choices[label] }}{% endif %}",
+            "prompt_original_task": true,
+            "comment": "",
+            "acc_stderr": 0.007106111600745623
+        },
+        {
+            "task_name": "multirc",
+            "prompt_name": "I was going to say\u2026",
+            "acc_norm": 0.42883663366336633,
+            "fixed_answer_choice_list": [
+                "No",
+                "Yes"
+            ],
+            "dataset_path": "super_glue",
+            "dataset_name": "multirc",
+            "subset": null,
+            "prompt_id": "d2d78b88-8845-45b5-935a-6451da00b285",
+            "prompt_jinja": "{{ paragraph }}\n{{ question }} \nI was going to say \"{{ answer }}\". Does that sound right? ||| {% if label != -1 %}{{ answer_choices[label] }}{% endif %}",
+            "prompt_original_task": true,
+            "comment": "",
+            "acc_norm_stderr": 0.00710869042313772
+        },
+        {
+            "task_name": "multirc",
+            "prompt_name": "Would it be good to answer\u2026",
+            "acc": 0.5204207920792079,
+            "fixed_answer_choice_list": [
+                "No",
+                "Yes"
+            ],
+            "dataset_path": "super_glue",
+            "dataset_name": "multirc",
+            "subset": null,
+            "prompt_id": "4fc9e1ea-7451-4dba-a2cb-ce870e35ef8b",
+            "prompt_jinja": "{{ paragraph }}\n{{ question }} \nWould it be good to answer \"{{ answer }}\"? ||| {% if label != -1 %}{{ answer_choices[label] }}{% endif %}",
+            "prompt_original_task": true,
+            "comment": "",
+            "acc_stderr": 0.0071758108566598
+        },
+        {
+            "task_name": "multirc",
+            "prompt_name": "Would it be good to answer\u2026",
+            "acc_norm": 0.43337458745874585,
+            "fixed_answer_choice_list": [
+                "No",
+                "Yes"
+            ],
+            "dataset_path": "super_glue",
+            "dataset_name": "multirc",
+            "subset": null,
+            "prompt_id": "4fc9e1ea-7451-4dba-a2cb-ce870e35ef8b",
+            "prompt_jinja": "{{ paragraph }}\n{{ question }} \nWould it be good to answer \"{{ answer }}\"? ||| {% if label != -1 %}{{ answer_choices[label] }}{% endif %}",
+            "prompt_original_task": true,
+            "comment": "",
+            "acc_norm_stderr": 0.00711775827463544
+        },
+        {
+            "task_name": "multirc",
+            "prompt_name": "confirm",
+            "acc": 0.4329620462046205,
+            "fixed_answer_choice_list": [
+                "No",
+                "Yes"
+            ],
+            "dataset_path": "super_glue",
+            "dataset_name": "multirc",
+            "subset": null,
+            "prompt_id": "b63fd1c3-b4a6-43c3-8429-6a389235b2a4",
+            "prompt_jinja": "{{paragraph}}\n\nQuestion: {{question}}\nI think \"{{answer}}\" is a valid answer. Could you confirm? Yes or no?\n|||\n{% if label != -1 %}{{answer_choices[label]}}{% endif %}",
+            "prompt_original_task": true,
+            "comment": "",
+            "acc_stderr": 0.007116959070151668
+        },
+        {
+            "task_name": "multirc",
+            "prompt_name": "confirm",
+            "acc_norm": 0.4280115511551155,
+            "fixed_answer_choice_list": [
+                "No",
+                "Yes"
+            ],
+            "dataset_path": "super_glue",
+            "dataset_name": "multirc",
+            "subset": null,
+            "prompt_id": "b63fd1c3-b4a6-43c3-8429-6a389235b2a4",
+            "prompt_jinja": "{{paragraph}}\n\nQuestion: {{question}}\nI think \"{{answer}}\" is a valid answer. Could you confirm? Yes or no?\n|||\n{% if label != -1 %}{{answer_choices[label]}}{% endif %}",
+            "prompt_original_task": true,
+            "comment": "",
+            "acc_norm_stderr": 0.007106976252751536
+        },
+        {
+            "task_name": "multirc",
+            "prompt_name": "correct",
+            "acc": 0.5721947194719472,
+            "fixed_answer_choice_list": [
+                "No",
+                "Yes"
+            ],
+            "dataset_path": "super_glue",
+            "dataset_name": "multirc",
+            "subset": null,
+            "prompt_id": "ae9b2b0b-1731-4370-adcc-36c4a959490d",
+            "prompt_jinja": "Is \"{{answer}}\" a correct answer to the following question?\nQuestion: {{question}}\n\nRely on the following text: {{paragraph}}\n|||\n{% if label != -1 %}{{answer_choices[label]}}{% endif %}",
+            "prompt_original_task": true,
+            "comment": "",
+            "acc_stderr": 0.007106544557507229
+        },
+        {
+            "task_name": "multirc",
+            "prompt_name": "correct",
+            "acc_norm": 0.4709158415841584,
+            "fixed_answer_choice_list": [
+                "No",
+                "Yes"
+            ],
+            "dataset_path": "super_glue",
+            "dataset_name": "multirc",
+            "subset": null,
+            "prompt_id": "ae9b2b0b-1731-4370-adcc-36c4a959490d",
+            "prompt_jinja": "Is \"{{answer}}\" a correct answer to the following question?\nQuestion: {{question}}\n\nRely on the following text: {{paragraph}}\n|||\n{% if label != -1 %}{{answer_choices[label]}}{% endif %}",
+            "prompt_original_task": true,
+            "comment": "",
+            "acc_norm_stderr": 0.00716964280499065
+        },
+        {
+            "task_name": "multirc",
+            "prompt_name": "decide_valid",
+            "acc": 0.5375412541254125,
+            "fixed_answer_choice_list": [
+                "No",
+                "Yes"
+            ],
+            "dataset_path": "super_glue",
+            "dataset_name": "multirc",
+            "subset": null,
+            "prompt_id": "7bf537ea-ff8d-44c7-8fc9-305b35e3be66",
+            "prompt_jinja": "{{paragraph}}\n\nDecide whether \"{{answer}}\" is a valid answer to the following question: {{question}}\nAnswer yes or no.\n|||\n{% if label != -1 %}{{answer_choices[label]}}{% endif %}",
+            "prompt_original_task": true,
+            "comment": "",
+            "acc_stderr": 0.007161531207958062
+        },
+        {
+            "task_name": "multirc",
+            "prompt_name": "decide_valid",
+            "acc_norm": 0.4280115511551155,
+            "fixed_answer_choice_list": [
+                "No",
+                "Yes"
+            ],
+            "dataset_path": "super_glue",
+            "dataset_name": "multirc",
+            "subset": null,
+            "prompt_id": "7bf537ea-ff8d-44c7-8fc9-305b35e3be66",
+            "prompt_jinja": "{{paragraph}}\n\nDecide whether \"{{answer}}\" is a valid answer to the following question: {{question}}\nAnswer yes or no.\n|||\n{% if label != -1 %}{{answer_choices[label]}}{% endif %}",
+            "prompt_original_task": true,
+            "comment": "",
+            "acc_norm_stderr": 0.007106976252751536
+        },
+        {
+            "task_name": "multirc",
+            "prompt_name": "found_this_answer",
+            "acc": 0.4773102310231023,
+            "fixed_answer_choice_list": [
+                "No",
+                "Yes"
+            ],
+            "dataset_path": "super_glue",
+            "dataset_name": "multirc",
+            "subset": null,
+            "prompt_id": "2d95962b-a545-41ae-8d76-07ee6704ef65",
+            "prompt_jinja": "{{paragraph}}\n\nQuestion: {{question}}\nI found this answer \"{{answer}}\". Is that correct? Yes or no?\n|||\n{% if label != -1 %}{{answer_choices[label]}}{% endif %}",
+            "prompt_original_task": true,
+            "comment": "",
+            "acc_stderr": 0.007174404542630741
+        },
+        {
+            "task_name": "multirc",
+            "prompt_name": "found_this_answer",
+            "acc_norm": 0.4280115511551155,
+            "fixed_answer_choice_list": [
+                "No",
+                "Yes"
+            ],
+            "dataset_path": "super_glue",
+            "dataset_name": "multirc",
+            "subset": null,
+            "prompt_id": "2d95962b-a545-41ae-8d76-07ee6704ef65",
+            "prompt_jinja": "{{paragraph}}\n\nQuestion: {{question}}\nI found this answer \"{{answer}}\". Is that correct? Yes or no?\n|||\n{% if label != -1 %}{{answer_choices[label]}}{% endif %}",
+            "prompt_original_task": true,
+            "comment": "",
+            "acc_norm_stderr": 0.007106976252751536
+        },
+        {
+            "task_name": "multirc",
+            "prompt_name": "grading",
+            "acc": 0.5874587458745875,
+            "fixed_answer_choice_list": [
+                "No",
+                "Yes"
+            ],
+            "dataset_path": "super_glue",
+            "dataset_name": "multirc",
+            "subset": null,
+            "prompt_id": "431a5c97-af33-4053-83c8-afb0dfc04448",
+            "prompt_jinja": "{{paragraph}}\nQuestion: {{question}}\n\nI am grading my students' exercises. Is the answer \"{{answer}}\" correct?\n|||\n{% if label != -1 %}{{answer_choices[label]}}{% endif %}",
+            "prompt_original_task": true,
+            "comment": "",
+            "acc_stderr": 0.007071081930208332
+        },
+        {
+            "task_name": "multirc",
+            "prompt_name": "grading",
+            "acc_norm": 0.4280115511551155,
+            "fixed_answer_choice_list": [
+                "No",
+                "Yes"
+            ],
+            "dataset_path": "super_glue",
+            "dataset_name": "multirc",
+            "subset": null,
+            "prompt_id": "431a5c97-af33-4053-83c8-afb0dfc04448",
+            "prompt_jinja": "{{paragraph}}\nQuestion: {{question}}\n\nI am grading my students' exercises. Is the answer \"{{answer}}\" correct?\n|||\n{% if label != -1 %}{{answer_choices[label]}}{% endif %}",
+            "prompt_original_task": true,
+            "comment": "",
+            "acc_norm_stderr": 0.007106976252751536
+        },
+        {
+            "task_name": "multirc",
+            "prompt_name": "is the correct answer\u2026",
+            "acc": 0.5478547854785478,
+            "fixed_answer_choice_list": [
+                "No",
+                "Yes"
+            ],
+            "dataset_path": "super_glue",
+            "dataset_name": "multirc",
+            "subset": null,
+            "prompt_id": "7d878b89-2774-429a-82fb-ac801379e3ae",
+            "prompt_jinja": "{{ paragraph }}\nQuestion: {{ question }} \nIs the correct answer {{ answer }}? ||| {% if label != -1 %}{{ answer_choices[label] }}{% endif %}",
+            "prompt_original_task": true,
+            "comment": "",
+            "acc_stderr": 0.007148833615093023
+        },
+        {
+            "task_name": "multirc",
+            "prompt_name": "is the correct answer\u2026",
+            "acc_norm": 0.4278052805280528,
+            "fixed_answer_choice_list": [
+                "No",
+                "Yes"
+            ],
+            "dataset_path": "super_glue",
+            "dataset_name": "multirc",
+            "subset": null,
+            "prompt_id": "7d878b89-2774-429a-82fb-ac801379e3ae",
+            "prompt_jinja": "{{ paragraph }}\nQuestion: {{ question }} \nIs the correct answer {{ answer }}? ||| {% if label != -1 %}{{ answer_choices[label] }}{% endif %}",
+            "prompt_original_task": true,
+            "comment": "",
+            "acc_norm_stderr": 0.007106544557507229
+        },
+        {
+            "task_name": "multirc",
+            "prompt_name": "is\u2026 a correct answer?",
+            "acc": 0.45028877887788776,
+            "fixed_answer_choice_list": [
+                "No",
+                "Yes"
+            ],
+            "dataset_path": "super_glue",
+            "dataset_name": "multirc",
+            "subset": null,
+            "prompt_id": "42d47df9-09de-4691-8e49-7cfadd636cdd",
+            "prompt_jinja": "{{ paragraph }}\nBased on the previous passage, {{ question }} \nIs \"{{ answer }}\" a correct answer? ||| {% if label != -1 %}{{ answer_choices[label] }}{% endif %}",
+            "prompt_original_task": true,
+            "comment": "",
+            "acc_stderr": 0.007146219530521704
+        },
+        {
+            "task_name": "multirc",
+            "prompt_name": "is\u2026 a correct answer?",
+            "acc_norm": 0.4280115511551155,
+            "fixed_answer_choice_list": [
+                "No",
+                "Yes"
+            ],
+            "dataset_path": "super_glue",
+            "dataset_name": "multirc",
+            "subset": null,
+            "prompt_id": "42d47df9-09de-4691-8e49-7cfadd636cdd",
+            "prompt_jinja": "{{ paragraph }}\nBased on the previous passage, {{ question }} \nIs \"{{ answer }}\" a correct answer? ||| {% if label != -1 %}{{ answer_choices[label] }}{% endif %}",
+            "prompt_original_task": true,
+            "comment": "",
+            "acc_norm_stderr": 0.007106976252751536
+        },
+        {
+            "task_name": "multirc",
+            "prompt_name": "paragraph\u2026 question\u2026 is it\u2026 ?",
+            "acc": 0.5581683168316832,
+            "fixed_answer_choice_list": [
+                "No",
+                "Yes"
+            ],
+            "dataset_path": "super_glue",
+            "dataset_name": "multirc",
+            "subset": null,
+            "prompt_id": "59a2d847-27f3-4002-a125-cf9a291b3098",
+            "prompt_jinja": "{{ paragraph }}\nQuestion: {{ question }} \nIs it {{ answer }}? ||| {% if label != -1 %}{{ answer_choices[label] }}{% endif %}",
+            "prompt_original_task": true,
+            "comment": "",
+            "acc_stderr": 0.007133037518848498
+        },
+        {
+            "task_name": "multirc",
+            "prompt_name": "paragraph\u2026 question\u2026 is it\u2026 ?",
+            "acc_norm": 0.429042904290429,
+            "fixed_answer_choice_list": [
+                "No",
+                "Yes"
+            ],
+            "dataset_path": "super_glue",
+            "dataset_name": "multirc",
+            "subset": null,
+            "prompt_id": "59a2d847-27f3-4002-a125-cf9a291b3098",
+            "prompt_jinja": "{{ paragraph }}\nQuestion: {{ question }} \nIs it {{ answer }}? ||| {% if label != -1 %}{{ answer_choices[label] }}{% endif %}",
+            "prompt_original_task": true,
+            "comment": "",
+            "acc_norm_stderr": 0.007109115814226985
+        },
+        {
+            "task_name": "qqp",
+            "prompt_name": "answer",
+            "acc": 0.4095720999257977,
+            "fixed_answer_choice_list": [
+                "no",
+                "yes"
+            ],
+            "dataset_path": "glue",
+            "dataset_name": "qqp",
+            "subset": null,
+            "prompt_id": "c0182cd1-c7ac-4abe-829f-4651536af951",
+            "prompt_jinja": "Can an answer to \"{{question1}}\" also be used to answer \"{{question2}}\"? ||| {{ answer_choices[label] }}",
+            "prompt_original_task": false,
+            "comment": "",
+            "acc_stderr": 0.0024456940020775335
+        },
+        {
+            "task_name": "qqp",
+            "prompt_name": "answer",
+            "acc_norm": 0.36816720257234725,
+            "fixed_answer_choice_list": [
+                "no",
+                "yes"
+            ],
+            "dataset_path": "glue",
+            "dataset_name": "qqp",
+            "subset": null,
+            "prompt_id": "c0182cd1-c7ac-4abe-829f-4651536af951",
+            "prompt_jinja": "Can an answer to \"{{question1}}\" also be used to answer \"{{question2}}\"? ||| {{ answer_choices[label] }}",
+            "prompt_original_task": false,
+            "comment": "",
+            "acc_norm_stderr": 0.002398706610614492
+        },
+        {
+            "task_name": "qqp",
+            "prompt_name": "duplicate",
+            "acc": 0.5389809547365817,
+            "fixed_answer_choice_list": [
+                "no",
+                "yes"
+            ],
+            "dataset_path": "glue",
+            "dataset_name": "qqp",
+            "subset": null,
+            "prompt_id": "fd244bd3-ca3b-4e4f-9722-fd006c50e157",
+            "prompt_jinja": "I received the questions \"{{question1}}\" and \"{{question2}}\". Are they duplicates? ||| {{ answer_choices[label] }}",
+            "prompt_original_task": true,
+            "comment": "",
+            "acc_stderr": 0.0024791319564636633
+        },
+        {
+            "task_name": "qqp",
+            "prompt_name": "duplicate",
+            "acc_norm": 0.36816720257234725,
+            "fixed_answer_choice_list": [
+                "no",
+                "yes"
+            ],
+            "dataset_path": "glue",
+            "dataset_name": "qqp",
+            "subset": null,
+            "prompt_id": "fd244bd3-ca3b-4e4f-9722-fd006c50e157",
+            "prompt_jinja": "I received the questions \"{{question1}}\" and \"{{question2}}\". Are they duplicates? ||| {{ answer_choices[label] }}",
+            "prompt_original_task": true,
+            "comment": "",
+            "acc_norm_stderr": 0.002398706610614492
+        },
+        {
+            "task_name": "qqp",
+            "prompt_name": "duplicate or not",
+            "acc": 0.3811526094484294,
+            "fixed_answer_choice_list": [
+                "not duplicates",
+                "duplicates"
+            ],
+            "dataset_path": "glue",
+            "dataset_name": "qqp",
+            "subset": null,
+            "prompt_id": "94972071-a726-42a3-a726-13f414b65e67",
+            "prompt_jinja": "{{question1}}\n{{question2}}\nPick one: These questions are \"{{\"duplicates\"}}\" or \"{{\"not duplicates\"}}\".\n|||\n{{ answer_choices[label] }}",
+            "prompt_original_task": true,
+            "comment": "",
+            "acc_stderr": 0.0024154315297388092
+        },
+        {
+            "task_name": "qqp",
+            "prompt_name": "duplicate or not",
+            "acc_norm": 0.6317585951026465,
+            "fixed_answer_choice_list": [
+                "not duplicates",
+                "duplicates"
+            ],
+            "dataset_path": "glue",
+            "dataset_name": "qqp",
+            "subset": null,
+            "prompt_id": "94972071-a726-42a3-a726-13f414b65e67",
+            "prompt_jinja": "{{question1}}\n{{question2}}\nPick one: These questions are \"{{\"duplicates\"}}\" or \"{{\"not duplicates\"}}\".\n|||\n{{ answer_choices[label] }}",
+            "prompt_original_task": true,
+            "comment": "",
+            "acc_norm_stderr": 0.00239880745215712
+        },
+        {
+            "task_name": "qqp",
+            "prompt_name": "meaning",
+            "acc": 0.3842443729903537,
+            "fixed_answer_choice_list": [
+                "No",
+                "Yes"
+            ],
+            "dataset_path": "glue",
+            "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_stderr": 0.0024191425100536248
+        },
+        {
+            "task_name": "qqp",
+            "prompt_name": "meaning",
+            "acc_norm": 0.36816720257234725,
+            "fixed_answer_choice_list": [
+                "No",
+                "Yes"
+            ],
+            "dataset_path": "glue",
+            "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
+        },
+        {
+            "task_name": "qqp",
+            "prompt_name": "quora",
+            "acc": 0.36826613900568883,
+            "fixed_answer_choice_list": [
+                "no",
+                "yes"
+            ],
+            "dataset_path": "glue",
+            "dataset_name": "qqp",
+            "subset": null,
+            "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] }}",
+            "prompt_original_task": true,
+            "comment": "",
+            "acc_stderr": 0.002398841052447127
+        },
+        {
+            "task_name": "qqp",
+            "prompt_name": "quora",
+            "acc_norm": 0.36816720257234725,
+            "fixed_answer_choice_list": [
+                "no",
+                "yes"
+            ],
+            "dataset_path": "glue",
+            "dataset_name": "qqp",
+            "subset": null,
+            "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] }}",
+            "prompt_original_task": true,
+            "comment": "",
+            "acc_norm_stderr": 0.002398706610614492
+        },
+        {
+            "task_name": "qqp",
+            "prompt_name": "same thing",
+            "acc": 0.5813999505317833,
+            "fixed_answer_choice_list": [
+                "no",
+                "yes"
+            ],
+            "dataset_path": "glue",
+            "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_stderr": 0.0024535258231136925
+        },
+        {
+            "task_name": "qqp",
+            "prompt_name": "same thing",
+            "acc_norm": 0.36816720257234725,
+            "fixed_answer_choice_list": [
+                "no",
+                "yes"
+            ],
+            "dataset_path": "glue",
+            "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]}}",
+            "prompt_original_task": true,
+            "comment": "",
+            "acc_stderr": 0.0300523034631437
+        },
+        {
+            "task_name": "rte",
+            "prompt_name": "does the claim\u2026 follow the fact\u2026",
+            "acc_norm": 0.5270758122743683,
+            "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]}}",
+            "prompt_original_task": true,
+            "comment": "",
+            "acc_norm_stderr": 0.0300523034631437
+        },
+        {
+            "task_name": "rte",
+            "prompt_name": "entailment explained",
+            "acc": 0.516245487364621,
+            "fixed_answer_choice_list": [
+                "entailment",
+                "not entailment"
+            ],
+            "dataset_path": "glue",
+            "dataset_name": "rte",
+            "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]}}",
+            "prompt_original_task": true,
+            "comment": "",
+            "acc_stderr": 0.030080573208738064
+        },
+        {
+            "task_name": "rte",
+            "prompt_name": "entailment explained",
+            "acc_norm": 0.4729241877256318,
+            "fixed_answer_choice_list": [
+                "entailment",
+                "not entailment"
+            ],
+            "dataset_path": "glue",
+            "dataset_name": "rte",
+            "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]}}",
+            "prompt_original_task": true,
+            "comment": "",
+            "acc_norm_stderr": 0.0300523034631437
+        },
+        {
+            "task_name": "rte",
+            "prompt_name": "imply",
+            "acc": 0.47653429602888087,
+            "fixed_answer_choice_list": [
+                "yes",
+                "no"
+            ],
+            "dataset_path": "glue",
+            "dataset_name": "rte",
+            "subset": null,
+            "prompt_id": "c8dfc879-40f2-412d-be1e-4cd70107f6e6",
+            "prompt_jinja": "Does \"{{sentence1}}\" imply that \"{{sentence2}}\"? Please answer either {{\"yes\"}} or {{\"no\"}}.\n|||\n{{answer_choices[label]}}",
+            "prompt_original_task": true,
+            "comment": "",
+            "acc_stderr": 0.030063300411902652
+        },
+        {
+            "task_name": "rte",
+            "prompt_name": "imply",
+            "acc_norm": 0.5270758122743683,
+            "fixed_answer_choice_list": [
+                "yes",
+                "no"
+            ],
+            "dataset_path": "glue",
+            "dataset_name": "rte",
+            "subset": null,
+            "prompt_id": "c8dfc879-40f2-412d-be1e-4cd70107f6e6",
+            "prompt_jinja": "Does \"{{sentence1}}\" imply that \"{{sentence2}}\"? Please answer either {{\"yes\"}} or {{\"no\"}}.\n|||\n{{answer_choices[label]}}",
+            "prompt_original_task": true,
+            "comment": "",
+            "acc_norm_stderr": 0.0300523034631437
+        },
+        {
+            "task_name": "rte",
+            "prompt_name": "imply separated",
+            "acc": 0.4620938628158845,
+            "fixed_answer_choice_list": [
+                "yes",
+                "no"
+            ],
+            "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]}}",
+            "prompt_original_task": true,
+            "comment": "",
+            "acc_stderr": 0.03000984891252911
+        },
+        {
+            "task_name": "rte",
+            "prompt_name": "imply separated",
+            "acc_norm": 0.5270758122743683,
+            "fixed_answer_choice_list": [
+                "yes",
+                "no"
+            ],
+            "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]}}",
+            "prompt_original_task": true,
+            "comment": "",
+            "acc_norm_stderr": 0.0300523034631437
+        },
+        {
+            "task_name": "rte",
+            "prompt_name": "mean",
+            "acc": 0.47653429602888087,
+            "fixed_answer_choice_list": [
+                "yes",
+                "no"
+            ],
+            "dataset_path": "glue",
+            "dataset_name": "rte",
+            "subset": null,
+            "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]}}",
+            "prompt_original_task": true,
+            "comment": "",
+            "acc_stderr": 0.030063300411902652
+        },
+        {
+            "task_name": "rte",
+            "prompt_name": "mean",
+            "acc_norm": 0.5270758122743683,
+            "fixed_answer_choice_list": [
+                "yes",
+                "no"
+            ],
+            "dataset_path": "glue",
+            "dataset_name": "rte",
+            "subset": null,
+            "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]}}",
+            "prompt_original_task": true,
+            "comment": "",
+            "acc_norm_stderr": 0.0300523034631437
+        },
+        {
+            "task_name": "sst",
+            "prompt_name": "following positive negative",
+            "acc": 0.7603211009174312,
+            "fixed_answer_choice_list": [
+                "negative",
+                "positive"
+            ],
+            "dataset_path": "glue",
+            "dataset_name": "sst2",
+            "subset": null,
+            "prompt_id": "63c6b2be-8ecd-42ad-88c7-0d1dc1a8323a",
+            "prompt_jinja": "Does the following sentence have a {{\"positive\"}} or {{\"negative\"}} sentiment?\n{{sentence}}\n|||\n{{ answer_choices[label] }}",
+            "prompt_original_task": true,
+            "comment": "",
+            "acc_stderr": 0.014464530608155847
+        },
+        {
+            "task_name": "sst",
+            "prompt_name": "following positive negative",
+            "acc_norm": 0.7603211009174312,
+            "fixed_answer_choice_list": [
+                "negative",
+                "positive"
+            ],
+            "dataset_path": "glue",
+            "dataset_name": "sst2",
+            "subset": null,
+            "prompt_id": "63c6b2be-8ecd-42ad-88c7-0d1dc1a8323a",
+            "prompt_jinja": "Does the following sentence have a {{\"positive\"}} or {{\"negative\"}} sentiment?\n{{sentence}}\n|||\n{{ answer_choices[label] }}",
+            "prompt_original_task": true,
+            "comment": "",
+            "acc_norm_stderr": 0.014464530608155847
+        },
+        {
+            "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
+        },
+        {
+            "task_name": "sst",
+            "prompt_name": "happy or mad",
+            "acc_norm": 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_norm_stderr": 0.01693900152535154
+        },
+        {
+            "task_name": "sst",
+            "prompt_name": "positive negative after",
+            "acc": 0.5263761467889908,
+            "fixed_answer_choice_list": [
+                "negative",
+                "positive"
+            ],
+            "dataset_path": "glue",
+            "dataset_name": "sst2",
+            "subset": null,
+            "prompt_id": "11d1c505-9232-4c35-82a4-4c3642843e2e",
+            "prompt_jinja": "{{sentence}}\nQuestion: Was that sentence {{\"positive\"}} or {{\"negative\"}}? Answer: ||| {{ answer_choices[label] }}",
+            "prompt_original_task": true,
+            "comment": "",
+            "acc_stderr": 0.016918264333564144
+        },
+        {
+            "task_name": "sst",
+            "prompt_name": "positive negative after",
+            "acc_norm": 0.5263761467889908,
+            "fixed_answer_choice_list": [
+                "negative",
+                "positive"
+            ],
+            "dataset_path": "glue",
+            "dataset_name": "sst2",
+            "subset": null,
+            "prompt_id": "11d1c505-9232-4c35-82a4-4c3642843e2e",
+            "prompt_jinja": "{{sentence}}\nQuestion: Was that sentence {{\"positive\"}} or {{\"negative\"}}? Answer: ||| {{ answer_choices[label] }}",
+            "prompt_original_task": true,
+            "comment": "",
+            "acc_norm_stderr": 0.016918264333564144
+        },
+        {
+            "task_name": "sst",
+            "prompt_name": "review",
+            "acc": 0.5722477064220184,
+            "fixed_answer_choice_list": [
+                "negative",
+                "positive"
+            ],
+            "dataset_path": "glue",
+            "dataset_name": "sst2",
+            "subset": null,
+            "prompt_id": "228fcae7-7f4c-4e3c-9ac4-e49b26bc103d",
+            "prompt_jinja": "I'm reading a review that says \"{{sentence}}\".\n\nDo you think the review is {{\"positive\"}} or {{\"negative\"}}? ||| {{ answer_choices[label] }}",
+            "prompt_original_task": true,
+            "comment": "",
+            "acc_stderr": 0.016764056901835654
+        },
+        {
+            "task_name": "sst",
+            "prompt_name": "review",
+            "acc_norm": 0.5722477064220184,
+            "fixed_answer_choice_list": [
+                "negative",
+                "positive"
+            ],
+            "dataset_path": "glue",
+            "dataset_name": "sst2",
+            "subset": null,
+            "prompt_id": "228fcae7-7f4c-4e3c-9ac4-e49b26bc103d",
+            "prompt_jinja": "I'm reading a review that says \"{{sentence}}\".\n\nDo you think the review is {{\"positive\"}} or {{\"negative\"}}? ||| {{ answer_choices[label] }}",
+            "prompt_original_task": true,
+            "comment": "",
+            "acc_norm_stderr": 0.016764056901835654
+        },
+        {
+            "task_name": "sst",
+            "prompt_name": "said",
+            "acc": 0.5022935779816514,
+            "fixed_answer_choice_list": [
+                "sad",
+                "happy"
+            ],
+            "dataset_path": "glue",
+            "dataset_name": "sst2",
+            "subset": null,
+            "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] }}",
+            "prompt_original_task": true,
+            "comment": "",
+            "acc_stderr": 0.016941675443113525
+        },
+        {
+            "task_name": "sst",
+            "prompt_name": "said",
+            "acc_norm": 0.5091743119266054,
+            "fixed_answer_choice_list": [
+                "sad",
+                "happy"
+            ],
+            "dataset_path": "glue",
+            "dataset_name": "sst2",
+            "subset": null,
+            "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] }}",
+            "prompt_original_task": true,
+            "comment": "",
+            "acc_norm_stderr": 0.01693900152535154
+        },
+        {
+            "task_name": "tydiqa_primary",
+            "prompt_name": "en_after_reading_the_text",
+            "acc": 0.35064935064935066,
+            "fixed_answer_choice_list": [
+                "Yes",
+                "No"
+            ],
+            "dataset_path": "tydiqa",
+            "dataset_name": "primary_task",
+            "subset": null,
+            "prompt_id": "9c42e3fd-d46e-4149-bb60-4b3118104d95",
+            "prompt_jinja": "{% if language == \"english\" %} \n    {% if annotations.yes_no_answer[0] == \"YES\" or annotations.yes_no_answer[0] == \"NO\" %} \nAfter reading the following text snippet from Wikipedia, please answer the question: {{question_text}} \n{{document_plaintext}}\n||| \n{{annotations.yes_no_answer[0] | capitalize}}\n    {% endif %}\n{% endif %}",
+            "prompt_original_task": true,
+            "comment": "",
+            "acc_stderr": 0.054735534443086
+        },
+        {
+            "task_name": "tydiqa_primary",
+            "prompt_name": "en_after_reading_the_text",
+            "acc_norm": 0.6493506493506493,
+            "fixed_answer_choice_list": [
+                "Yes",
+                "No"
+            ],
+            "dataset_path": "tydiqa",
+            "dataset_name": "primary_task",
+            "subset": null,
+            "prompt_id": "9c42e3fd-d46e-4149-bb60-4b3118104d95",
+            "prompt_jinja": "{% if language == \"english\" %} \n    {% if annotations.yes_no_answer[0] == \"YES\" or annotations.yes_no_answer[0] == \"NO\" %} \nAfter reading the following text snippet from Wikipedia, please answer the question: {{question_text}} \n{{document_plaintext}}\n||| \n{{annotations.yes_no_answer[0] | capitalize}}\n    {% endif %}\n{% endif %}",
+            "prompt_original_task": true,
+            "comment": "",
+            "acc_norm_stderr": 0.054735534443086
+        },
+        {
+            "task_name": "tydiqa_primary",
+            "prompt_name": "en_based_on_the_text",
+            "acc": 0.33766233766233766,
+            "fixed_answer_choice_list": [
+                "Yes",
+                "No"
+            ],
+            "dataset_path": "tydiqa",
+            "dataset_name": "primary_task",
+            "subset": null,
+            "prompt_id": "e593017f-9bcf-4442-944d-fcdf2edcb4f7",
+            "prompt_jinja": "{% if language == \"english\" %} \n    {% if annotations.yes_no_answer[0] == \"YES\" or annotations.yes_no_answer[0] == \"NO\" %} \nBased on the following text snippet, {{question_text}} \n{{document_plaintext}}\n||| \n{{annotations.yes_no_answer[0] | capitalize}}\n    {% endif %}\n{% endif %}",
+            "prompt_original_task": true,
+            "comment": "",
+            "acc_stderr": 0.05424681453014242
+        },
+        {
+            "task_name": "tydiqa_primary",
+            "prompt_name": "en_based_on_the_text",
+            "acc_norm": 0.6363636363636364,
+            "fixed_answer_choice_list": [
+                "Yes",
+                "No"
+            ],
+            "dataset_path": "tydiqa",
+            "dataset_name": "primary_task",
+            "subset": null,
+            "prompt_id": "e593017f-9bcf-4442-944d-fcdf2edcb4f7",
+            "prompt_jinja": "{% if language == \"english\" %} \n    {% if annotations.yes_no_answer[0] == \"YES\" or annotations.yes_no_answer[0] == \"NO\" %} \nBased on the following text snippet, {{question_text}} \n{{document_plaintext}}\n||| \n{{annotations.yes_no_answer[0] | capitalize}}\n    {% endif %}\n{% endif %}",
+            "prompt_original_task": true,
+            "comment": "",
+            "acc_norm_stderr": 0.055179725333353066
+        },
+        {
+            "task_name": "tydiqa_primary",
+            "prompt_name": "en_heres_what_I_found",
+            "acc": 0.03685741998060136,
+            "fixed_answer_choice_list": [
+                "Yes",
+                "No",
+                "None"
+            ],
+            "dataset_path": "tydiqa",
+            "dataset_name": "primary_task",
+            "subset": null,
+            "prompt_id": "16f11e56-a78d-4e33-bba1-586f9947baf7",
+            "prompt_jinja": "{% if language == \"english\" %}\nI wonder {{question_text}}.\nHelp me answer this question with \"{{answer_choices[0]}}\" or \"{{answer_choices[1]}}\" or \"{{answer_choices[2]}}\" if none of the first two answers apply.\nHere's what I found on the internet:\nTopic: {{document_title}}\nArticle: {{document_plaintext}}\n|||\n{{annotations.yes_no_answer[0] | capitalize}}\n{% endif %}",
+            "prompt_original_task": true,
+            "comment": "",
+            "acc_stderr": 0.005870689955728106
+        },
+        {
+            "task_name": "tydiqa_primary",
+            "prompt_name": "en_heres_what_I_found",
+            "acc_norm": 0.8661493695441319,
+            "fixed_answer_choice_list": [
+                "Yes",
+                "No",
+                "None"
+            ],
+            "dataset_path": "tydiqa",
+            "dataset_name": "primary_task",
+            "subset": null,
+            "prompt_id": "16f11e56-a78d-4e33-bba1-586f9947baf7",
+            "prompt_jinja": "{% if language == \"english\" %}\nI wonder {{question_text}}.\nHelp me answer this question with \"{{answer_choices[0]}}\" or \"{{answer_choices[1]}}\" or \"{{answer_choices[2]}}\" if none of the first two answers apply.\nHere's what I found on the internet:\nTopic: {{document_title}}\nArticle: {{document_plaintext}}\n|||\n{{annotations.yes_no_answer[0] | capitalize}}\n{% endif %}",
+            "prompt_original_task": true,
+            "comment": "",
+            "acc_norm_stderr": 0.010609330898735572
+        },
+        {
+            "task_name": "tydiqa_primary",
+            "prompt_name": "en_open_domain_qa",
+            "acc": 0.6753246753246753,
+            "fixed_answer_choice_list": [
+                "Yes",
+                "No"
+            ],
+            "dataset_path": "tydiqa",
+            "dataset_name": "primary_task",
+            "subset": null,
+            "prompt_id": "b4f7c441-41b1-4665-93f9-f2e875aed92a",
+            "prompt_jinja": "{% if language == \"english\" %} \n    {% if annotations.yes_no_answer[0] == \"YES\" or annotations.yes_no_answer[0] == \"NO\" %} \nAnswer the question about {{document_title}}.\nQuestion: {{question_text}}. Yes or No?\n||| \n{{annotations.yes_no_answer[0] | capitalize}}\n    {% endif %}\n{% endif %}",
+            "prompt_original_task": false,
+            "comment": "",
+            "acc_stderr": 0.05371235012133188
+        },
+        {
+            "task_name": "tydiqa_primary",
+            "prompt_name": "en_open_domain_qa",
+            "acc_norm": 0.6753246753246753,
+            "fixed_answer_choice_list": [
+                "Yes",
+                "No"
+            ],
+            "dataset_path": "tydiqa",
+            "dataset_name": "primary_task",
+            "subset": null,
+            "prompt_id": "b4f7c441-41b1-4665-93f9-f2e875aed92a",
+            "prompt_jinja": "{% if language == \"english\" %} \n    {% if annotations.yes_no_answer[0] == \"YES\" or annotations.yes_no_answer[0] == \"NO\" %} \nAnswer the question about {{document_title}}.\nQuestion: {{question_text}}. Yes or No?\n||| \n{{annotations.yes_no_answer[0] | capitalize}}\n    {% endif %}\n{% endif %}",
+            "prompt_original_task": false,
+            "comment": "",
+            "acc_norm_stderr": 0.05371235012133188
+        },
+        {
+            "task_name": "tydiqa_primary",
+            "prompt_name": "en_open_domain_qa_without_choices",
+            "acc": 0.6753246753246753,
+            "fixed_answer_choice_list": [
+                "Yes",
+                "No"
+            ],
+            "dataset_path": "tydiqa",
+            "dataset_name": "primary_task",
+            "subset": null,
+            "prompt_id": "4b21e3be-fba4-49b7-beb1-a61de26eb0ac",
+            "prompt_jinja": "{% if language == \"english\" %} \n    {% if annotations.yes_no_answer[0] == \"YES\" or annotations.yes_no_answer[0] == \"NO\" %} \nAnswer the question about {{document_title}}. {{question_text}}\n||| \n{{annotations.yes_no_answer[0] | capitalize}} \n    {% endif %} \n{% endif %} ",
+            "prompt_original_task": false,
+            "comment": "",
+            "acc_stderr": 0.05371235012133188
+        },
+        {
+            "task_name": "tydiqa_primary",
+            "prompt_name": "en_open_domain_qa_without_choices",
+            "acc_norm": 0.6753246753246753,
+            "fixed_answer_choice_list": [
+                "Yes",
+                "No"
+            ],
+            "dataset_path": "tydiqa",
+            "dataset_name": "primary_task",
+            "subset": null,
+            "prompt_id": "4b21e3be-fba4-49b7-beb1-a61de26eb0ac",
+            "prompt_jinja": "{% if language == \"english\" %} \n    {% if annotations.yes_no_answer[0] == \"YES\" or annotations.yes_no_answer[0] == \"NO\" %} \nAnswer the question about {{document_title}}. {{question_text}}\n||| \n{{annotations.yes_no_answer[0] | capitalize}} \n    {% endif %} \n{% endif %} ",
+            "prompt_original_task": false,
+            "comment": "",
+            "acc_norm_stderr": 0.05371235012133188
+        },
+        {
+            "task_name": "tydiqa_primary",
+            "prompt_name": "en_read_and_answer",
+            "acc": 0.03685741998060136,
+            "fixed_answer_choice_list": [
+                "Yes",
+                "No",
+                "None"
+            ],
+            "dataset_path": "tydiqa",
+            "dataset_name": "primary_task",
+            "subset": null,
+            "prompt_id": "7b8b7707-dbad-40d2-a5c2-430e6ace10bb",
+            "prompt_jinja": "{% if language == \"english\" %}\nAnswer the following question with \"{{answer_choices[0]}}\" or \"{{answer_choices[1]}}\" or \"{{answer_choices[2]}}\" if none of the first two answers apply.\nQuestion: {{question_text}}\nTopic: {{document_title}}\nArticle: {{document_plaintext}}\n|||\n{{annotations.yes_no_answer[0] | capitalize}}\n{% endif %}",
+            "prompt_original_task": true,
+            "comment": "",
+            "acc_stderr": 0.005870689955728103
+        },
+        {
+            "task_name": "tydiqa_primary",
+            "prompt_name": "en_read_and_answer",
+            "acc_norm": 0.8845780795344326,
+            "fixed_answer_choice_list": [
+                "Yes",
+                "No",
+                "None"
+            ],
+            "dataset_path": "tydiqa",
+            "dataset_name": "primary_task",
+            "subset": null,
+            "prompt_id": "7b8b7707-dbad-40d2-a5c2-430e6ace10bb",
+            "prompt_jinja": "{% if language == \"english\" %}\nAnswer the following question with \"{{answer_choices[0]}}\" or \"{{answer_choices[1]}}\" or \"{{answer_choices[2]}}\" if none of the first two answers apply.\nQuestion: {{question_text}}\nTopic: {{document_title}}\nArticle: {{document_plaintext}}\n|||\n{{annotations.yes_no_answer[0] | capitalize}}\n{% endif %}",
+            "prompt_original_task": true,
+            "comment": "",
+            "acc_norm_stderr": 0.009956200231519313
+        },
+        {
+            "task_name": "tydiqa_primary",
+            "prompt_name": "en_yes_no_none",
+            "acc": 0.037827352085354024,
+            "fixed_answer_choice_list": [
+                "Yes",
+                "No",
+                "None"
+            ],
+            "dataset_path": "tydiqa",
+            "dataset_name": "primary_task",
+            "subset": null,
+            "prompt_id": "297fc59f-bd92-493b-ae61-3c3adcb46eb3",
+            "prompt_jinja": "{% if language == \"english\" %} \nQuestion: {{question_text}}\nAnswer the question with {{\"Yes\"}} or {{\"No\"}}. If it is not possible then answer {{\"None\"}}.\nHint: {{document_plaintext}}\n|||\n{{annotations.yes_no_answer[0] | capitalize}}\n{% endif %}",
+            "prompt_original_task": true,
+            "comment": "",
+            "acc_stderr": 0.005944438823944305
+        },
+        {
+            "task_name": "tydiqa_primary",
+            "prompt_name": "en_yes_no_none",
+            "acc_norm": 0.871968962172648,
+            "fixed_answer_choice_list": [
+                "Yes",
+                "No",
+                "None"
+            ],
+            "dataset_path": "tydiqa",
+            "dataset_name": "primary_task",
+            "subset": null,
+            "prompt_id": "297fc59f-bd92-493b-ae61-3c3adcb46eb3",
+            "prompt_jinja": "{% if language == \"english\" %} \nQuestion: {{question_text}}\nAnswer the question with {{\"Yes\"}} or {{\"No\"}}. If it is not possible then answer {{\"None\"}}.\nHint: {{document_plaintext}}\n|||\n{{annotations.yes_no_answer[0] | capitalize}}\n{% endif %}",
+            "prompt_original_task": true,
+            "comment": "",
+            "acc_norm_stderr": 0.01041093017771443
+        },
+        {
+            "task_name": "tydiqa_primary",
+            "prompt_name": "en_yes_no_question",
+            "acc": 0.7652764306498545,
+            "fixed_answer_choice_list": [
+                "Yes",
+                "No"
+            ],
+            "dataset_path": "tydiqa",
+            "dataset_name": "primary_task",
+            "subset": null,
+            "prompt_id": "6835dd64-96bd-4bf8-9ba5-645d6a7b8472",
+            "prompt_jinja": "{% if language == \"english\" %}\n{{question_text}}\nIs this a \"Yes/No\" question?\n|||\n{% if annotations. yes_no_answer[0] == \"NONE\" %}\nNo\n{% else %}\nYes\n{% endif %}\n{% endif %}",
+            "prompt_original_task": false,
+            "comment": "",
+            "acc_stderr": 0.013205927447521368
+        },
+        {
+            "task_name": "tydiqa_primary",
+            "prompt_name": "en_yes_no_question",
+            "acc_norm": 0.07565470417070805,
+            "fixed_answer_choice_list": [
+                "Yes",
+                "No"
+            ],
+            "dataset_path": "tydiqa",
+            "dataset_name": "primary_task",
+            "subset": null,
+            "prompt_id": "6835dd64-96bd-4bf8-9ba5-645d6a7b8472",
+            "prompt_jinja": "{% if language == \"english\" %}\n{{question_text}}\nIs this a \"Yes/No\" question?\n|||\n{% if annotations. yes_no_answer[0] == \"NONE\" %}\nNo\n{% else %}\nYes\n{% endif %}\n{% endif %}",
+            "prompt_original_task": false,
+            "comment": "",
+            "acc_norm_stderr": 0.008239796273494257
+        },
+        {
+            "task_name": "tydiqa_primary",
+            "prompt_name": "id_after_reading_the_text",
+            "acc": 0.2711864406779661,
+            "fixed_answer_choice_list": [
+                "Ya",
+                "Tidak"
+            ],
+            "dataset_path": "tydiqa",
+            "dataset_name": "primary_task",
+            "subset": null,
+            "prompt_id": "f93c6cde-cd5e-4d25-8549-f186546cea26",
+            "prompt_jinja": "{% if language == \"indonesian\" %} \n    {% if annotations.yes_no_answer[0] == \"YES\" or annotations.yes_no_answer[0] == \"NO\" %} \nSetelah membaca potongan teks Wikipedia di bawah ini, mohon jawab pertanyaan: \n{{question_text}} \n{{document_plaintext}}\n||| \n{{{\"NO\":\"Tidak\", \"YES\":\"Ya\", \"NONE\": \"Tidak ada\"}[annotations.yes_no_answer[0]]}}\n    {% endif %}\n{% endif %}",
+            "prompt_original_task": true,
+            "comment": "",
+            "acc_stderr": 0.058375177038848765
+        },
+        {
+            "task_name": "tydiqa_primary",
+            "prompt_name": "id_after_reading_the_text",
+            "acc_norm": 0.2033898305084746,
+            "fixed_answer_choice_list": [
+                "Ya",
+                "Tidak"
+            ],
+            "dataset_path": "tydiqa",
+            "dataset_name": "primary_task",
+            "subset": null,
+            "prompt_id": "f93c6cde-cd5e-4d25-8549-f186546cea26",
+            "prompt_jinja": "{% if language == \"indonesian\" %} \n    {% if annotations.yes_no_answer[0] == \"YES\" or annotations.yes_no_answer[0] == \"NO\" %} \nSetelah membaca potongan teks Wikipedia di bawah ini, mohon jawab pertanyaan: \n{{question_text}} \n{{document_plaintext}}\n||| \n{{{\"NO\":\"Tidak\", \"YES\":\"Ya\", \"NONE\": \"Tidak ada\"}[annotations.yes_no_answer[0]]}}\n    {% endif %}\n{% endif %}",
+            "prompt_original_task": true,
+            "comment": "",
+            "acc_norm_stderr": 0.052853474644238056
+        },
+        {
+            "task_name": "tydiqa_primary",
+            "prompt_name": "id_based_on_the_text",
+            "acc": 0.23728813559322035,
+            "fixed_answer_choice_list": [
+                "Ya",
+                "Tidak"
+            ],
+            "dataset_path": "tydiqa",
+            "dataset_name": "primary_task",
+            "subset": null,
+            "prompt_id": "fe910acd-a156-4f46-a757-4382821fcfd2",
+            "prompt_jinja": "{% if language == \"indonesian\" %} \n    {% if annotations.yes_no_answer[0] == \"YES\" or annotations.yes_no_answer[0] == \"NO\" %} \nMenurut potongan teks berikut, {{question_text}}\n{{document_plaintext}}\n||| \n{{{\"NO\":\"Tidak\", \"YES\":\"Ya\", \"NONE\": \"Tidak ada\"}[annotations.yes_no_answer[0]]}}\n    {% endif %}\n{% endif %}",
+            "prompt_original_task": true,
+            "comment": "",
+            "acc_stderr": 0.05586042894941199
+        },
+        {
+            "task_name": "tydiqa_primary",
+            "prompt_name": "id_based_on_the_text",
+            "acc_norm": 0.2033898305084746,
+            "fixed_answer_choice_list": [
+                "Ya",
+                "Tidak"
+            ],
+            "dataset_path": "tydiqa",
+            "dataset_name": "primary_task",
+            "subset": null,
+            "prompt_id": "fe910acd-a156-4f46-a757-4382821fcfd2",
+            "prompt_jinja": "{% if language == \"indonesian\" %} \n    {% if annotations.yes_no_answer[0] == \"YES\" or annotations.yes_no_answer[0] == \"NO\" %} \nMenurut potongan teks berikut, {{question_text}}\n{{document_plaintext}}\n||| \n{{{\"NO\":\"Tidak\", \"YES\":\"Ya\", \"NONE\": \"Tidak ada\"}[annotations.yes_no_answer[0]]}}\n    {% endif %}\n{% endif %}",
+            "prompt_original_task": true,
+            "comment": "",
+            "acc_norm_stderr": 0.052853474644238056
+        },
+        {
+            "task_name": "tydiqa_primary",
+            "prompt_name": "id_heres_what_I_found",
+            "acc": 0.007202216066481994,
+            "fixed_answer_choice_list": [
+                "Ya",
+                "Tidak",
+                "Tidak ada"
+            ],
+            "dataset_path": "tydiqa",
+            "dataset_name": "primary_task",
+            "subset": null,
+            "prompt_id": "764fda4e-dc13-4766-b8ab-eafd79fe875e",
+            "prompt_jinja": "{% if language == \"indonesian\" %}\nSaya penasaran {{question_text}}.\nTolong bantu saya menjawab pertanyaan ini dengan \"{{answer_choices[0]}}\", \"{{answer_choices[1]}}\" atau \"{{answer_choices[2]}}\" jika dua opsi pertama tidak bisa diaplikasikan.\nIni yang saya temukan di internet:\nTopik: {{document_title}}\nArtikel: {{document_plaintext}}\n|||\n{{{\"NO\":\"Tidak\",\"YES\": \"Ya\", \"NONE\": \"Tidak ada\"}[annotations.yes_no_answer[0]]}}\n{% endif %}",
+            "prompt_original_task": true,
+            "comment": "",
+            "acc_stderr": 0.001990880560147875
+        },
+        {
+            "task_name": "tydiqa_primary",
+            "prompt_name": "id_heres_what_I_found",
+            "acc_norm": 0.9662049861495845,
+            "fixed_answer_choice_list": [
+                "Ya",
+                "Tidak",
+                "Tidak ada"
+            ],
+            "dataset_path": "tydiqa",
+            "dataset_name": "primary_task",
+            "subset": null,
+            "prompt_id": "764fda4e-dc13-4766-b8ab-eafd79fe875e",
+            "prompt_jinja": "{% if language == \"indonesian\" %}\nSaya penasaran {{question_text}}.\nTolong bantu saya menjawab pertanyaan ini dengan \"{{answer_choices[0]}}\", \"{{answer_choices[1]}}\" atau \"{{answer_choices[2]}}\" jika dua opsi pertama tidak bisa diaplikasikan.\nIni yang saya temukan di internet:\nTopik: {{document_title}}\nArtikel: {{document_plaintext}}\n|||\n{{{\"NO\":\"Tidak\",\"YES\": \"Ya\", \"NONE\": \"Tidak ada\"}[annotations.yes_no_answer[0]]}}\n{% endif %}",
+            "prompt_original_task": true,
+            "comment": "",
+            "acc_norm_stderr": 0.0042544427599910594
+        },
+        {
+            "task_name": "tydiqa_primary",
+            "prompt_name": "id_open_domain_qa",
+            "acc": 0.4576271186440678,
+            "fixed_answer_choice_list": [
+                "Ya",
+                "Tidak"
+            ],
+            "dataset_path": "tydiqa",
+            "dataset_name": "primary_task",
+            "subset": null,
+            "prompt_id": "976fb48f-7135-4344-91c8-cee2e535b8ab",
+            "prompt_jinja": "{% if language == \"indonesian\" %} \n    {% if annotations.yes_no_answer[0] == \"YES\" or annotations.yes_no_answer[0] == \"NO\" %} \nJawab pertanyaan terkait {{document_title}}.\nPertanyaan: {{question_text}}. Ya atau Tidak?\n||| \n{{{\"NO\":\"Tidak\",\"YES\": \"Ya\", \"NONE\": \"Tidak ada\"}[annotations.yes_no_answer[0]]}}\n    {% endif %}\n{% endif %}",
+            "prompt_original_task": false,
+            "comment": "",
+            "acc_stderr": 0.06541703602400106
+        },
+        {
+            "task_name": "tydiqa_primary",
+            "prompt_name": "id_open_domain_qa",
+            "acc_norm": 0.2033898305084746,
+            "fixed_answer_choice_list": [
+                "Ya",
+                "Tidak"
+            ],
+            "dataset_path": "tydiqa",
+            "dataset_name": "primary_task",
+            "subset": null,
+            "prompt_id": "976fb48f-7135-4344-91c8-cee2e535b8ab",
+            "prompt_jinja": "{% if language == \"indonesian\" %} \n    {% if annotations.yes_no_answer[0] == \"YES\" or annotations.yes_no_answer[0] == \"NO\" %} \nJawab pertanyaan terkait {{document_title}}.\nPertanyaan: {{question_text}}. Ya atau Tidak?\n||| \n{{{\"NO\":\"Tidak\",\"YES\": \"Ya\", \"NONE\": \"Tidak ada\"}[annotations.yes_no_answer[0]]}}\n    {% endif %}\n{% endif %}",
+            "prompt_original_task": false,
+            "comment": "",
+            "acc_norm_stderr": 0.052853474644238056
+        },
+        {
+            "task_name": "tydiqa_primary",
+            "prompt_name": "id_open_domain_qa_without_choices",
+            "acc": 0.2711864406779661,
+            "fixed_answer_choice_list": [
+                "Ya",
+                "Tidak"
+            ],
+            "dataset_path": "tydiqa",
+            "dataset_name": "primary_task",
+            "subset": null,
+            "prompt_id": "d6139cbc-7b25-4539-80c7-2b0832183951",
+            "prompt_jinja": "{% if language == \"indonesian\" %} \n    {% if annotations.yes_no_answer[0] == \"YES\" or annotations.yes_no_answer[0] == \"NO\" %} \nJawab pertanyaan terkait {{document_title}}. {{question_text}}\n||| \n{{{\"NO\":\"Tidak\",\"YES\": \"Ya\", \"NONE\": \"Tidak ada\"}[annotations.yes_no_answer[0]]}}\n    {% endif %} \n{% endif %} ",
+            "prompt_original_task": false,
+            "comment": "",
+            "acc_stderr": 0.05837517703884878
+        },
+        {
+            "task_name": "tydiqa_primary",
+            "prompt_name": "id_open_domain_qa_without_choices",
+            "acc_norm": 0.2033898305084746,
+            "fixed_answer_choice_list": [
+                "Ya",
+                "Tidak"
+            ],
+            "dataset_path": "tydiqa",
+            "dataset_name": "primary_task",
+            "subset": null,
+            "prompt_id": "d6139cbc-7b25-4539-80c7-2b0832183951",
+            "prompt_jinja": "{% if language == \"indonesian\" %} \n    {% if annotations.yes_no_answer[0] == \"YES\" or annotations.yes_no_answer[0] == \"NO\" %} \nJawab pertanyaan terkait {{document_title}}. {{question_text}}\n||| \n{{{\"NO\":\"Tidak\",\"YES\": \"Ya\", \"NONE\": \"Tidak ada\"}[annotations.yes_no_answer[0]]}}\n    {% endif %} \n{% endif %} ",
+            "prompt_original_task": false,
+            "comment": "",
+            "acc_norm_stderr": 0.052853474644238056
+        },
+        {
+            "task_name": "tydiqa_primary",
+            "prompt_name": "id_read_and_answer",
+            "acc": 0.007202216066481994,
+            "fixed_answer_choice_list": [
+                "Ya",
+                "Tidak",
+                "Tidak ada"
+            ],
+            "dataset_path": "tydiqa",
+            "dataset_name": "primary_task",
+            "subset": null,
+            "prompt_id": "b368b4af-b8b1-4b0f-ab72-a4db0649ca65",
+            "prompt_jinja": "{% if language == \"indonesian\" %}\nJawab pertanyaan berikut dengan \"{{answer_choices[0]}}\" atau \"{{answer_choices[1]}}\" atau \"{{answer_choices[2]}}\" jika dua\nopsi pertama tidak dapat diaplikasikan.\nPertanyaan: {{question_text}}\nTopik: {{document_title}}\nArtikel: {{document_plaintext}}\n|||\n{{{\"NO\":\"Tidak\",\"YES\": \"Ya\", \"NONE\": \"Tidak ada\"}[annotations.yes_no_answer[0]]}}\n{% endif %}",
+            "prompt_original_task": true,
+            "comment": "",
+            "acc_stderr": 0.0019908805601478756
+        },
+        {
+            "task_name": "tydiqa_primary",
+            "prompt_name": "id_read_and_answer",
+            "acc_norm": 0.9662049861495845,
+            "fixed_answer_choice_list": [
+                "Ya",
+                "Tidak",
+                "Tidak ada"
+            ],
+            "dataset_path": "tydiqa",
+            "dataset_name": "primary_task",
+            "subset": null,
+            "prompt_id": "b368b4af-b8b1-4b0f-ab72-a4db0649ca65",
+            "prompt_jinja": "{% if language == \"indonesian\" %}\nJawab pertanyaan berikut dengan \"{{answer_choices[0]}}\" atau \"{{answer_choices[1]}}\" atau \"{{answer_choices[2]}}\" jika dua\nopsi pertama tidak dapat diaplikasikan.\nPertanyaan: {{question_text}}\nTopik: {{document_title}}\nArtikel: {{document_plaintext}}\n|||\n{{{\"NO\":\"Tidak\",\"YES\": \"Ya\", \"NONE\": \"Tidak ada\"}[annotations.yes_no_answer[0]]}}\n{% endif %}",
+            "prompt_original_task": true,
+            "comment": "",
+            "acc_norm_stderr": 0.0042544427599910594
+        },
+        {
+            "task_name": "tydiqa_primary",
+            "prompt_name": "id_yes_no_none",
+            "acc": 0.008310249307479225,
+            "fixed_answer_choice_list": [
+                "Ya",
+                "Tidak",
+                "Tidak ada"
+            ],
+            "dataset_path": "tydiqa",
+            "dataset_name": "primary_task",
+            "subset": null,
+            "prompt_id": "5c48e84c-10e4-44ee-b2b3-94a4d018e833",
+            "prompt_jinja": "{% if language == \"indonesian\" %} \nPertanyaan: {{question_text}}\nJawab pertanyaan tersebut dengan {{\"Ya\"}} atau {{\"Tidak\"}}. Jika tidak memungkinkan, jawab dengan {{\"Tidak ada\"}}.\nPetunjuk: {{document_plaintext}}\n|||\n{{{\"NO\":\"Tidak\",\"YES\": \"Ya\", \"NONE\": \"Tidak ada\"}[annotations.yes_no_answer[0]]}}\n{% endif %}",
+            "prompt_original_task": true,
+            "comment": "",
+            "acc_stderr": 0.002137355052582956
+        },
+        {
+            "task_name": "tydiqa_primary",
+            "prompt_name": "id_yes_no_none",
+            "acc_norm": 0.9662049861495845,
+            "fixed_answer_choice_list": [
+                "Ya",
+                "Tidak",
+                "Tidak ada"
+            ],
+            "dataset_path": "tydiqa",
+            "dataset_name": "primary_task",
+            "subset": null,
+            "prompt_id": "5c48e84c-10e4-44ee-b2b3-94a4d018e833",
+            "prompt_jinja": "{% if language == \"indonesian\" %} \nPertanyaan: {{question_text}}\nJawab pertanyaan tersebut dengan {{\"Ya\"}} atau {{\"Tidak\"}}. Jika tidak memungkinkan, jawab dengan {{\"Tidak ada\"}}.\nPetunjuk: {{document_plaintext}}\n|||\n{{{\"NO\":\"Tidak\",\"YES\": \"Ya\", \"NONE\": \"Tidak ada\"}[annotations.yes_no_answer[0]]}}\n{% endif %}",
+            "prompt_original_task": true,
+            "comment": "",
+            "acc_norm_stderr": 0.0042544427599910594
+        },
+        {
+            "task_name": "tydiqa_primary",
+            "prompt_name": "id_yes_no_question",
+            "acc": 0.8138504155124654,
+            "fixed_answer_choice_list": [
+                "Ya",
+                "Tidak"
+            ],
+            "dataset_path": "tydiqa",
+            "dataset_name": "primary_task",
+            "subset": null,
+            "prompt_id": "ebba1db1-daf2-4e40-9dca-4cbe4298cd3e",
+            "prompt_jinja": "{% if language == \"indonesian\" %}\n{{question_text}}\nApakah ini termasuk kalimat tanya \"Ya/Tidak\"?\n|||\n{% if annotations. yes_no_answer[0] == \"NONE\" %}\nTidak\n{% else %}\nYa\n{% endif %}\n{% endif %}",
+            "prompt_original_task": false,
+            "comment": "",
+            "acc_stderr": 0.009163999646097152
+        },
+        {
+            "task_name": "tydiqa_primary",
+            "prompt_name": "id_yes_no_question",
+            "acc_norm": 0.9673130193905817,
+            "fixed_answer_choice_list": [
+                "Ya",
+                "Tidak"
+            ],
+            "dataset_path": "tydiqa",
+            "dataset_name": "primary_task",
+            "subset": null,
+            "prompt_id": "ebba1db1-daf2-4e40-9dca-4cbe4298cd3e",
+            "prompt_jinja": "{% if language == \"indonesian\" %}\n{{question_text}}\nApakah ini termasuk kalimat tanya \"Ya/Tidak\"?\n|||\n{% if annotations. yes_no_answer[0] == \"NONE\" %}\nTidak\n{% else %}\nYa\n{% endif %}\n{% endif %}",
+            "prompt_original_task": false,
+            "comment": "",
+            "acc_norm_stderr": 0.0041865150102794995
+        },
+        {
+            "task_name": "tydiqa_primary",
+            "prompt_name": "jp_after_reading_the_text",
+            "acc": 0.7635135135135135,
+            "fixed_answer_choice_list": [
+                "\u306f\u3044",
+                "\u3044\u3044\u3048"
+            ],
+            "dataset_path": "tydiqa",
+            "dataset_name": "primary_task",
+            "subset": null,
+            "prompt_id": "d160228e-9169-456d-a16a-0f5288452c9a",
+            "prompt_jinja": "{% if language == \"japanese\" %}\n    {% if annotations.yes_no_answer[0] == \"YES\" or annotations.yes_no_answer[0] == \"NO\" %} \n        \u30a6\u30a3\u30ad\u30da\u30c7\u30a3\u30a2\u306e\u8a18\u4e8b\u3092\u8aad\u3093\u3060\u3042\u3068\u3001\u6b21\u306e\u8cea\u554f\u306b\u7b54\u3048\u3066\u304f\u3060\u3055\u3044: {{question_text}}\n        {{document_plaintext}}\n        |||\n        {{{\"YES\":\"\u306f\u3044\", \"NO\":\"\u3044\u3044\u3048\", \"NONE\": \"\u3069\u3061\u3089\u3067\u3082\u306a\u3044\"}[annotations.yes_no_answer[0]]}}\n    {% endif %}\n{% endif %}",
+            "prompt_original_task": true,
+            "comment": "",
+            "acc_stderr": 0.03504716241250439
+        },
+        {
+            "task_name": "tydiqa_primary",
+            "prompt_name": "jp_after_reading_the_text",
+            "acc_norm": 0.2972972972972973,
+            "fixed_answer_choice_list": [
+                "\u306f\u3044",
+                "\u3044\u3044\u3048"
+            ],
+            "dataset_path": "tydiqa",
+            "dataset_name": "primary_task",
+            "subset": null,
+            "prompt_id": "d160228e-9169-456d-a16a-0f5288452c9a",
+            "prompt_jinja": "{% if language == \"japanese\" %}\n    {% if annotations.yes_no_answer[0] == \"YES\" or annotations.yes_no_answer[0] == \"NO\" %} \n        \u30a6\u30a3\u30ad\u30da\u30c7\u30a3\u30a2\u306e\u8a18\u4e8b\u3092\u8aad\u3093\u3060\u3042\u3068\u3001\u6b21\u306e\u8cea\u554f\u306b\u7b54\u3048\u3066\u304f\u3060\u3055\u3044: {{question_text}}\n        {{document_plaintext}}\n        |||\n        {{{\"YES\":\"\u306f\u3044\", \"NO\":\"\u3044\u3044\u3048\", \"NONE\": \"\u3069\u3061\u3089\u3067\u3082\u306a\u3044\"}[annotations.yes_no_answer[0]]}}\n    {% endif %}\n{% endif %}",
+            "prompt_original_task": true,
+            "comment": "",
+            "acc_norm_stderr": 0.037698374558241474
+        },
+        {
+            "task_name": "tydiqa_primary",
+            "prompt_name": "jp_based_on_the_text",
+            "acc": 0.7635135135135135,
+            "fixed_answer_choice_list": [
+                "\u306f\u3044",
+                "\u3044\u3044\u3048"
+            ],
+            "dataset_path": "tydiqa",
+            "dataset_name": "primary_task",
+            "subset": null,
+            "prompt_id": "733a3ff3-6edd-4440-b038-bf9736ebaff7",
+            "prompt_jinja": "{% if language == \"japanese\" %}\n    {% if annotations.yes_no_answer[0] == \"YES\" or annotations.yes_no_answer[0] == \"NO\" %}\n        \u6b21\u306e\u6587\u7ae0\u306b\u3082\u3068\u3065\u304f\u3068\u3001 , {{question_text}} \n        {{document_plaintext}}\n        ||| \n        {{{\"YES\":\"\u306f\u3044\", \"NO\":\"\u3044\u3044\u3048\", \"NONE\": \"\u3069\u3061\u3089\u3067\u3082\u306a\u3044\"}[annotations.yes_no_answer[0]]}}\n    {% endif %}\n{% endif %}",
+            "prompt_original_task": true,
+            "comment": "",
+            "acc_stderr": 0.03504716241250439
+        },
+        {
+            "task_name": "tydiqa_primary",
+            "prompt_name": "jp_based_on_the_text",
+            "acc_norm": 0.2905405405405405,
+            "fixed_answer_choice_list": [
+                "\u306f\u3044",
+                "\u3044\u3044\u3048"
+            ],
+            "dataset_path": "tydiqa",
+            "dataset_name": "primary_task",
+            "subset": null,
+            "prompt_id": "733a3ff3-6edd-4440-b038-bf9736ebaff7",
+            "prompt_jinja": "{% if language == \"japanese\" %}\n    {% if annotations.yes_no_answer[0] == \"YES\" or annotations.yes_no_answer[0] == \"NO\" %}\n        \u6b21\u306e\u6587\u7ae0\u306b\u3082\u3068\u3065\u304f\u3068\u3001 , {{question_text}} \n        {{document_plaintext}}\n        ||| \n        {{{\"YES\":\"\u306f\u3044\", \"NO\":\"\u3044\u3044\u3048\", \"NONE\": \"\u3069\u3061\u3089\u3067\u3082\u306a\u3044\"}[annotations.yes_no_answer[0]]}}\n    {% endif %}\n{% endif %}",
+            "prompt_original_task": true,
+            "comment": "",
+            "acc_norm_stderr": 0.03744626397928733
+        }
+    ],
+    "versions": {
+        "multirc+I was going to say\u2026": 0,
+        "multirc+Would it be good to answer\u2026": 0,
+        "multirc+confirm": 0,
+        "multirc+correct": 0,
+        "multirc+decide_valid": 0,
+        "multirc+found_this_answer": 0,
+        "multirc+grading": 0,
+        "multirc+is the correct answer\u2026": 0,
+        "multirc+is\u2026 a correct answer?": 0,
+        "multirc+paragraph\u2026 question\u2026 is it\u2026 ?": 0,
+        "qqp+answer": 0,
+        "qqp+duplicate": 0,
+        "qqp+duplicate or not": 0,
+        "qqp+meaning": 0,
+        "qqp+quora": 0,
+        "qqp+same thing": 0,
+        "rte+does the claim\u2026 follow the fact\u2026": 0,
+        "rte+entailment explained": 0,
+        "rte+imply": 0,
+        "rte+imply separated": 0,
+        "rte+mean": 0,
+        "sst+following positive negative": 0,
+        "sst+happy or mad": 0,
+        "sst+positive negative after": 0,
+        "sst+review": 0,
+        "sst+said": 0,
+        "tydiqa_primary+en_after_reading_the_text": 0,
+        "tydiqa_primary+en_based_on_the_text": 0,
+        "tydiqa_primary+en_heres_what_I_found": 0,
+        "tydiqa_primary+en_open_domain_qa": 0,
+        "tydiqa_primary+en_open_domain_qa_without_choices": 0,
+        "tydiqa_primary+en_read_and_answer": 0,
+        "tydiqa_primary+en_yes_no_none": 0,
+        "tydiqa_primary+en_yes_no_question": 0,
+        "tydiqa_primary+id_after_reading_the_text": 0,
+        "tydiqa_primary+id_based_on_the_text": 0,
+        "tydiqa_primary+id_heres_what_I_found": 0,
+        "tydiqa_primary+id_open_domain_qa": 0,
+        "tydiqa_primary+id_open_domain_qa_without_choices": 0,
+        "tydiqa_primary+id_read_and_answer": 0,
+        "tydiqa_primary+id_yes_no_none": 0,
+        "tydiqa_primary+id_yes_no_question": 0,
+        "tydiqa_primary+jp_after_reading_the_text": 0,
+        "tydiqa_primary+jp_based_on_the_text": 0
+    },
+    "table_results": {
+        "multirc+I was going to say\u2026": {
+            "task_name": "multirc",
+            "prompt_name": "I was going to say\u2026",
+            "acc": 0.5724009900990099,
+            "acc_stderr": 0.007106111600745623,
+            "acc_norm": 0.42883663366336633,
+            "acc_norm_stderr": 0.00710869042313772
+        },
+        "multirc+Would it be good to answer\u2026": {
+            "task_name": "multirc",
+            "prompt_name": "Would it be good to answer\u2026",
+            "acc": 0.5204207920792079,
+            "acc_stderr": 0.0071758108566598,
+            "acc_norm": 0.43337458745874585,
+            "acc_norm_stderr": 0.00711775827463544
+        },
+        "multirc+confirm": {
+            "task_name": "multirc",
+            "prompt_name": "confirm",
+            "acc": 0.4329620462046205,
+            "acc_stderr": 0.007116959070151668,
+            "acc_norm": 0.4280115511551155,
+            "acc_norm_stderr": 0.007106976252751536
+        },
+        "multirc+correct": {
+            "task_name": "multirc",
+            "prompt_name": "correct",
+            "acc": 0.5721947194719472,
+            "acc_stderr": 0.007106544557507229,
+            "acc_norm": 0.4709158415841584,
+            "acc_norm_stderr": 0.00716964280499065
+        },
+        "multirc+decide_valid": {
+            "task_name": "multirc",
+            "prompt_name": "decide_valid",
+            "acc": 0.5375412541254125,
+            "acc_stderr": 0.007161531207958062,
+            "acc_norm": 0.4280115511551155,
+            "acc_norm_stderr": 0.007106976252751536
+        },
+        "multirc+found_this_answer": {
+            "task_name": "multirc",
+            "prompt_name": "found_this_answer",
+            "acc": 0.4773102310231023,
+            "acc_stderr": 0.007174404542630741,
+            "acc_norm": 0.4280115511551155,
+            "acc_norm_stderr": 0.007106976252751536
+        },
+        "multirc+grading": {
+            "task_name": "multirc",
+            "prompt_name": "grading",
+            "acc": 0.5874587458745875,
+            "acc_stderr": 0.007071081930208332,
+            "acc_norm": 0.4280115511551155,
+            "acc_norm_stderr": 0.007106976252751536
+        },
+        "multirc+is the correct answer\u2026": {
+            "task_name": "multirc",
+            "prompt_name": "is the correct answer\u2026",
+            "acc": 0.5478547854785478,
+            "acc_stderr": 0.007148833615093023,
+            "acc_norm": 0.4278052805280528,
+            "acc_norm_stderr": 0.007106544557507229
+        },
+        "multirc+is\u2026 a correct answer?": {
+            "task_name": "multirc",
+            "prompt_name": "is\u2026 a correct answer?",
+            "acc": 0.45028877887788776,
+            "acc_stderr": 0.007146219530521704,
+            "acc_norm": 0.4280115511551155,
+            "acc_norm_stderr": 0.007106976252751536
+        },
+        "multirc+paragraph\u2026 question\u2026 is it\u2026 ?": {
+            "task_name": "multirc",
+            "prompt_name": "paragraph\u2026 question\u2026 is it\u2026 ?",
+            "acc": 0.5581683168316832,
+            "acc_stderr": 0.007133037518848498,
+            "acc_norm": 0.429042904290429,
+            "acc_norm_stderr": 0.007109115814226985
+        },
+        "qqp+answer": {
+            "task_name": "qqp",
+            "prompt_name": "answer",
+            "acc": 0.4095720999257977,
+            "acc_stderr": 0.0024456940020775335,
+            "acc_norm": 0.36816720257234725,
+            "acc_norm_stderr": 0.002398706610614492
+        },
+        "qqp+duplicate": {
+            "task_name": "qqp",
+            "prompt_name": "duplicate",
+            "acc": 0.5389809547365817,
+            "acc_stderr": 0.0024791319564636633,
+            "acc_norm": 0.36816720257234725,
+            "acc_norm_stderr": 0.002398706610614492
+        },
+        "qqp+duplicate or not": {
+            "task_name": "qqp",
+            "prompt_name": "duplicate or not",
+            "acc": 0.3811526094484294,
+            "acc_stderr": 0.0024154315297388092,
+            "acc_norm": 0.6317585951026465,
+            "acc_norm_stderr": 0.00239880745215712
+        },
+        "qqp+meaning": {
+            "task_name": "qqp",
+            "prompt_name": "meaning",
+            "acc": 0.3842443729903537,
+            "acc_stderr": 0.0024191425100536248,
+            "acc_norm": 0.36816720257234725,
+            "acc_norm_stderr": 0.002398706610614492
+        },
+        "qqp+quora": {
+            "task_name": "qqp",
+            "prompt_name": "quora",
+            "acc": 0.36826613900568883,
+            "acc_stderr": 0.002398841052447127,
+            "acc_norm": 0.36816720257234725,
+            "acc_norm_stderr": 0.002398706610614492
+        },
+        "qqp+same thing": {
+            "task_name": "qqp",
+            "prompt_name": "same thing",
+            "acc": 0.5813999505317833,
+            "acc_stderr": 0.0024535258231136925,
+            "acc_norm": 0.36816720257234725,
+            "acc_norm_stderr": 0.002398706610614492
+        },
+        "rte+does the claim\u2026 follow the fact\u2026": {
+            "task_name": "rte",
+            "prompt_name": "does the claim\u2026 follow the fact\u2026",
+            "acc": 0.4729241877256318,
+            "acc_stderr": 0.0300523034631437,
+            "acc_norm": 0.5270758122743683,
+            "acc_norm_stderr": 0.0300523034631437
+        },
+        "rte+entailment explained": {
+            "task_name": "rte",
+            "prompt_name": "entailment explained",
+            "acc": 0.516245487364621,
+            "acc_stderr": 0.030080573208738064,
+            "acc_norm": 0.4729241877256318,
+            "acc_norm_stderr": 0.0300523034631437
+        },
+        "rte+imply": {
+            "task_name": "rte",
+            "prompt_name": "imply",
+            "acc": 0.47653429602888087,
+            "acc_stderr": 0.030063300411902652,
+            "acc_norm": 0.5270758122743683,
+            "acc_norm_stderr": 0.0300523034631437
+        },
+        "rte+imply separated": {
+            "task_name": "rte",
+            "prompt_name": "imply separated",
+            "acc": 0.4620938628158845,
+            "acc_stderr": 0.03000984891252911,
+            "acc_norm": 0.5270758122743683,
+            "acc_norm_stderr": 0.0300523034631437
+        },
+        "rte+mean": {
+            "task_name": "rte",
+            "prompt_name": "mean",
+            "acc": 0.47653429602888087,
+            "acc_stderr": 0.030063300411902652,
+            "acc_norm": 0.5270758122743683,
+            "acc_norm_stderr": 0.0300523034631437
+        },
+        "sst+following positive negative": {
+            "task_name": "sst",
+            "prompt_name": "following positive negative",
+            "acc": 0.7603211009174312,
+            "acc_stderr": 0.014464530608155847,
+            "acc_norm": 0.7603211009174312,
+            "acc_norm_stderr": 0.014464530608155847
+        },
+        "sst+happy or mad": {
+            "task_name": "sst",
+            "prompt_name": "happy or mad",
+            "acc": 0.5091743119266054,
+            "acc_stderr": 0.01693900152535154,
+            "acc_norm": 0.5091743119266054,
+            "acc_norm_stderr": 0.01693900152535154
+        },
+        "sst+positive negative after": {
+            "task_name": "sst",
+            "prompt_name": "positive negative after",
+            "acc": 0.5263761467889908,
+            "acc_stderr": 0.016918264333564144,
+            "acc_norm": 0.5263761467889908,
+            "acc_norm_stderr": 0.016918264333564144
+        },
+        "sst+review": {
+            "task_name": "sst",
+            "prompt_name": "review",
+            "acc": 0.5722477064220184,
+            "acc_stderr": 0.016764056901835654,
+            "acc_norm": 0.5722477064220184,
+            "acc_norm_stderr": 0.016764056901835654
+        },
+        "sst+said": {
+            "task_name": "sst",
+            "prompt_name": "said",
+            "acc": 0.5022935779816514,
+            "acc_stderr": 0.016941675443113525,
+            "acc_norm": 0.5091743119266054,
+            "acc_norm_stderr": 0.01693900152535154
+        },
+        "tydiqa_primary+en_after_reading_the_text": {
+            "task_name": "tydiqa_primary",
+            "prompt_name": "en_after_reading_the_text",
+            "acc": 0.35064935064935066,
+            "acc_stderr": 0.054735534443086,
+            "acc_norm": 0.6493506493506493,
+            "acc_norm_stderr": 0.054735534443086
+        },
+        "tydiqa_primary+en_based_on_the_text": {
+            "task_name": "tydiqa_primary",
+            "prompt_name": "en_based_on_the_text",
+            "acc": 0.33766233766233766,
+            "acc_stderr": 0.05424681453014242,
+            "acc_norm": 0.6363636363636364,
+            "acc_norm_stderr": 0.055179725333353066
+        },
+        "tydiqa_primary+en_heres_what_I_found": {
+            "task_name": "tydiqa_primary",
+            "prompt_name": "en_heres_what_I_found",
+            "acc": 0.03685741998060136,
+            "acc_stderr": 0.005870689955728106,
+            "acc_norm": 0.8661493695441319,
+            "acc_norm_stderr": 0.010609330898735572
+        },
+        "tydiqa_primary+en_open_domain_qa": {
+            "task_name": "tydiqa_primary",
+            "prompt_name": "en_open_domain_qa",
+            "acc": 0.6753246753246753,
+            "acc_stderr": 0.05371235012133188,
+            "acc_norm": 0.6753246753246753,
+            "acc_norm_stderr": 0.05371235012133188
+        },
+        "tydiqa_primary+en_open_domain_qa_without_choices": {
+            "task_name": "tydiqa_primary",
+            "prompt_name": "en_open_domain_qa_without_choices",
+            "acc": 0.6753246753246753,
+            "acc_stderr": 0.05371235012133188,
+            "acc_norm": 0.6753246753246753,
+            "acc_norm_stderr": 0.05371235012133188
+        },
+        "tydiqa_primary+en_read_and_answer": {
+            "task_name": "tydiqa_primary",
+            "prompt_name": "en_read_and_answer",
+            "acc": 0.03685741998060136,
+            "acc_stderr": 0.005870689955728103,
+            "acc_norm": 0.8845780795344326,
+            "acc_norm_stderr": 0.009956200231519313
+        },
+        "tydiqa_primary+en_yes_no_none": {
+            "task_name": "tydiqa_primary",
+            "prompt_name": "en_yes_no_none",
+            "acc": 0.037827352085354024,
+            "acc_stderr": 0.005944438823944305,
+            "acc_norm": 0.871968962172648,
+            "acc_norm_stderr": 0.01041093017771443
+        },
+        "tydiqa_primary+en_yes_no_question": {
+            "task_name": "tydiqa_primary",
+            "prompt_name": "en_yes_no_question",
+            "acc": 0.7652764306498545,
+            "acc_stderr": 0.013205927447521368,
+            "acc_norm": 0.07565470417070805,
+            "acc_norm_stderr": 0.008239796273494257
+        },
+        "tydiqa_primary+id_after_reading_the_text": {
+            "task_name": "tydiqa_primary",
+            "prompt_name": "id_after_reading_the_text",
+            "acc": 0.2711864406779661,
+            "acc_stderr": 0.058375177038848765,
+            "acc_norm": 0.2033898305084746,
+            "acc_norm_stderr": 0.052853474644238056
+        },
+        "tydiqa_primary+id_based_on_the_text": {
+            "task_name": "tydiqa_primary",
+            "prompt_name": "id_based_on_the_text",
+            "acc": 0.23728813559322035,
+            "acc_stderr": 0.05586042894941199,
+            "acc_norm": 0.2033898305084746,
+            "acc_norm_stderr": 0.052853474644238056
+        },
+        "tydiqa_primary+id_heres_what_I_found": {
+            "task_name": "tydiqa_primary",
+            "prompt_name": "id_heres_what_I_found",
+            "acc": 0.007202216066481994,
+            "acc_stderr": 0.001990880560147875,
+            "acc_norm": 0.9662049861495845,
+            "acc_norm_stderr": 0.0042544427599910594
+        },
+        "tydiqa_primary+id_open_domain_qa": {
+            "task_name": "tydiqa_primary",
+            "prompt_name": "id_open_domain_qa",
+            "acc": 0.4576271186440678,
+            "acc_stderr": 0.06541703602400106,
+            "acc_norm": 0.2033898305084746,
+            "acc_norm_stderr": 0.052853474644238056
+        },
+        "tydiqa_primary+id_open_domain_qa_without_choices": {
+            "task_name": "tydiqa_primary",
+            "prompt_name": "id_open_domain_qa_without_choices",
+            "acc": 0.2711864406779661,
+            "acc_stderr": 0.05837517703884878,
+            "acc_norm": 0.2033898305084746,
+            "acc_norm_stderr": 0.052853474644238056
+        },
+        "tydiqa_primary+id_read_and_answer": {
+            "task_name": "tydiqa_primary",
+            "prompt_name": "id_read_and_answer",
+            "acc": 0.007202216066481994,
+            "acc_stderr": 0.0019908805601478756,
+            "acc_norm": 0.9662049861495845,
+            "acc_norm_stderr": 0.0042544427599910594
+        },
+        "tydiqa_primary+id_yes_no_none": {
+            "task_name": "tydiqa_primary",
+            "prompt_name": "id_yes_no_none",
+            "acc": 0.008310249307479225,
+            "acc_stderr": 0.002137355052582956,
+            "acc_norm": 0.9662049861495845,
+            "acc_norm_stderr": 0.0042544427599910594
+        },
+        "tydiqa_primary+id_yes_no_question": {
+            "task_name": "tydiqa_primary",
+            "prompt_name": "id_yes_no_question",
+            "acc": 0.8138504155124654,
+            "acc_stderr": 0.009163999646097152,
+            "acc_norm": 0.9673130193905817,
+            "acc_norm_stderr": 0.0041865150102794995
+        },
+        "tydiqa_primary+jp_after_reading_the_text": {
+            "task_name": "tydiqa_primary",
+            "prompt_name": "jp_after_reading_the_text",
+            "acc": 0.7635135135135135,
+            "acc_stderr": 0.03504716241250439,
+            "acc_norm": 0.2972972972972973,
+            "acc_norm_stderr": 0.037698374558241474
+        },
+        "tydiqa_primary+jp_based_on_the_text": {
+            "task_name": "tydiqa_primary",
+            "prompt_name": "jp_based_on_the_text",
+            "acc": 0.7635135135135135,
+            "acc_stderr": 0.03504716241250439,
+            "acc_norm": 0.2905405405405405,
+            "acc_norm_stderr": 0.03744626397928733
+        }
+    },
+    "config": {
+        "adaptive_seq_len": true,
+        "num_fewshot": 0,
+        "bootstrap_iters": 100000
+    }
+}

evaluation/results/tr11/bloom2b5/bslmevalfiles/tr11c-2b5-ml-bsevalharness-results_lm-eval_global_step337250_2022-07-14-20-09-16.json

@@ -0,0 +1,1255 @@
+{
+    "results": [
+        {
+            "task_name": "gsarti/flores_101_kor",
+            "prompt_name": null,
+            "word_perplexity": 1199924.6918920355
+        },
+        {
+            "task_name": "gsarti/flores_101_kor",
+            "prompt_name": null,
+            "byte_perplexity": 3.932884847226212
+        },
+        {
+            "task_name": "gsarti/flores_101_kor",
+            "prompt_name": null,
+            "bits_per_byte": 1.9755879455567535
+        },
+        {
+            "task_name": "gsarti/flores_101_kir",
+            "prompt_name": null,
+            "word_perplexity": 140474672.36703426
+        },
+        {
+            "task_name": "gsarti/flores_101_kir",
+            "prompt_name": null,
+            "byte_perplexity": 3.729278369847201
+        },
+        {
+            "task_name": "gsarti/flores_101_kir",
+            "prompt_name": null,
+            "bits_per_byte": 1.8988964902756764
+        },
+        {
+            "task_name": "gsarti/flores_101_lao",
+            "prompt_name": null,
+            "word_perplexity": 6.1350041352351446e+26
+        },
+        {
+            "task_name": "gsarti/flores_101_lao",
+            "prompt_name": null,
+            "byte_perplexity": 2.9077314760849924
+        },
+        {
+            "task_name": "gsarti/flores_101_lao",
+            "prompt_name": null,
+            "bits_per_byte": 1.5398940450457603
+        },
+        {
+            "task_name": "gsarti/flores_101_lav",
+            "prompt_name": null,
+            "word_perplexity": 10925745.685132286
+        },
+        {
+            "task_name": "gsarti/flores_101_lav",
+            "prompt_name": null,
+            "byte_perplexity": 7.777221919194806
+        },
+        {
+            "task_name": "gsarti/flores_101_lav",
+            "prompt_name": null,
+            "bits_per_byte": 2.959254905963978
+        },
+        {
+            "task_name": "gsarti/flores_101_lin",
+            "prompt_name": null,
+            "word_perplexity": 166841.83897098716
+        },
+        {
+            "task_name": "gsarti/flores_101_lin",
+            "prompt_name": null,
+            "byte_perplexity": 7.524842908050988
+        },
+        {
+            "task_name": "gsarti/flores_101_lin",
+            "prompt_name": null,
+            "bits_per_byte": 2.9116614638468965
+        },
+        {
+            "task_name": "gsarti/flores_101_lit",
+            "prompt_name": null,
+            "word_perplexity": 8532364.031813102
+        },
+        {
+            "task_name": "gsarti/flores_101_lit",
+            "prompt_name": null,
+            "byte_perplexity": 7.369179434621725
+        },
+        {
+            "task_name": "gsarti/flores_101_lit",
+            "prompt_name": null,
+            "bits_per_byte": 2.88150398275188
+        },
+        {
+            "task_name": "gsarti/flores_101_luo",
+            "prompt_name": null,
+            "word_perplexity": 1335199.656768974
+        },
+        {
+            "task_name": "gsarti/flores_101_luo",
+            "prompt_name": null,
+            "byte_perplexity": 11.975963093623681
+        },
+        {
+            "task_name": "gsarti/flores_101_luo",
+            "prompt_name": null,
+            "bits_per_byte": 3.5820697754437467
+        },
+        {
+            "task_name": "gsarti/flores_101_ltz",
+            "prompt_name": null,
+            "word_perplexity": 4081613.1281958995
+        },
+        {
+            "task_name": "gsarti/flores_101_ltz",
+            "prompt_name": null,
+            "byte_perplexity": 8.801059747949214
+        },
+        {
+            "task_name": "gsarti/flores_101_ltz",
+            "prompt_name": null,
+            "bits_per_byte": 3.1376772511430198
+        },
+        {
+            "task_name": "gsarti/flores_101_mkd",
+            "prompt_name": null,
+            "word_perplexity": 291548.6603872499
+        },
+        {
+            "task_name": "gsarti/flores_101_mkd",
+            "prompt_name": null,
+            "byte_perplexity": 2.9656732291754087
+        },
+        {
+            "task_name": "gsarti/flores_101_mkd",
+            "prompt_name": null,
+            "bits_per_byte": 1.5683596441110415
+        },
+        {
+            "task_name": "gsarti/flores_101_msa",
+            "prompt_name": null,
+            "word_perplexity": 931.4191160965655
+        },
+        {
+            "task_name": "gsarti/flores_101_msa",
+            "prompt_name": null,
+            "byte_perplexity": 2.5710001772665634
+        },
+        {
+            "task_name": "gsarti/flores_101_msa",
+            "prompt_name": null,
+            "bits_per_byte": 1.3623297096432079
+        },
+        {
+            "task_name": "gsarti/flores_101_mal",
+            "prompt_name": null,
+            "word_perplexity": 1.207348615509252e+18
+        },
+        {
+            "task_name": "gsarti/flores_101_mal",
+            "prompt_name": null,
+            "byte_perplexity": 4.615948455160037
+        },
+        {
+            "task_name": "gsarti/flores_101_mal",
+            "prompt_name": null,
+            "bits_per_byte": 2.2066271139530245
+        },
+        {
+            "task_name": "gsarti/flores_101_mlt",
+            "prompt_name": null,
+            "word_perplexity": 1820552051.5260184
+        },
+        {
+            "task_name": "gsarti/flores_101_mlt",
+            "prompt_name": null,
+            "byte_perplexity": 15.004773437665275
+        },
+        {
+            "task_name": "gsarti/flores_101_mlt",
+            "prompt_name": null,
+            "bits_per_byte": 3.9073496302297994
+        },
+        {
+            "task_name": "gsarti/flores_101_mri",
+            "prompt_name": null,
+            "word_perplexity": 26466.98082941409
+        },
+        {
+            "task_name": "gsarti/flores_101_mri",
+            "prompt_name": null,
+            "byte_perplexity": 7.474035895661322
+        },
+        {
+            "task_name": "gsarti/flores_101_mri",
+            "prompt_name": null,
+            "bits_per_byte": 2.9018874925878335
+        },
+        {
+            "task_name": "gsarti/flores_101_mar",
+            "prompt_name": null,
+            "word_perplexity": 54017030487867.64
+        },
+        {
+            "task_name": "gsarti/flores_101_mar",
+            "prompt_name": null,
+            "byte_perplexity": 5.483253482821379
+        },
+        {
+            "task_name": "gsarti/flores_101_mar",
+            "prompt_name": null,
+            "bits_per_byte": 2.4550321688665875
+        },
+        {
+            "task_name": "gsarti/flores_101_mon",
+            "prompt_name": null,
+            "word_perplexity": 6612951.176601774
+        },
+        {
+            "task_name": "gsarti/flores_101_mon",
+            "prompt_name": null,
+            "byte_perplexity": 3.410598542315402
+        },
+        {
+            "task_name": "gsarti/flores_101_mon",
+            "prompt_name": null,
+            "bits_per_byte": 1.7700249469487581
+        },
+        {
+            "task_name": "gsarti/flores_101_npi",
+            "prompt_name": null,
+            "word_perplexity": 9218412485042.457
+        },
+        {
+            "task_name": "gsarti/flores_101_npi",
+            "prompt_name": null,
+            "byte_perplexity": 5.199342701937889
+        },
+        {
+            "task_name": "gsarti/flores_101_npi",
+            "prompt_name": null,
+            "bits_per_byte": 2.3783292500628397
+        },
+        {
+            "task_name": "gsarti/flores_101_nso",
+            "prompt_name": null,
+            "word_perplexity": 84236.45826211123
+        },
+        {
+            "task_name": "gsarti/flores_101_nso",
+            "prompt_name": null,
+            "byte_perplexity": 8.154626800955667
+        },
+        {
+            "task_name": "gsarti/flores_101_nso",
+            "prompt_name": null,
+            "bits_per_byte": 3.027618853058479
+        },
+        {
+            "task_name": "gsarti/flores_101_nob",
+            "prompt_name": null,
+            "word_perplexity": 36969.51682419191
+        },
+        {
+            "task_name": "gsarti/flores_101_nob",
+            "prompt_name": null,
+            "byte_perplexity": 5.402763169129877
+        },
+        {
+            "task_name": "gsarti/flores_101_nob",
+            "prompt_name": null,
+            "bits_per_byte": 2.4336974426149056
+        },
+        {
+            "task_name": "gsarti/flores_101_nya",
+            "prompt_name": null,
+            "word_perplexity": 6609896.030066139
+        },
+        {
+            "task_name": "gsarti/flores_101_nya",
+            "prompt_name": null,
+            "byte_perplexity": 8.179860208369393
+        },
+        {
+            "task_name": "gsarti/flores_101_nya",
+            "prompt_name": null,
+            "bits_per_byte": 3.0320761881040017
+        },
+        {
+            "task_name": "gsarti/flores_101_oci",
+            "prompt_name": null,
+            "word_perplexity": 21641.316763505896
+        },
+        {
+            "task_name": "gsarti/flores_101_oci",
+            "prompt_name": null,
+            "byte_perplexity": 4.8617357393685845
+        },
+        {
+            "task_name": "gsarti/flores_101_oci",
+            "prompt_name": null,
+            "bits_per_byte": 2.2814714775164466
+        },
+        {
+            "task_name": "gsarti/flores_101_ory",
+            "prompt_name": null,
+            "word_perplexity": 11873283711992.748
+        },
+        {
+            "task_name": "gsarti/flores_101_ory",
+            "prompt_name": null,
+            "byte_perplexity": 5.189421861225964
+        },
+        {
+            "task_name": "gsarti/flores_101_ory",
+            "prompt_name": null,
+            "bits_per_byte": 2.375573820972048
+        },
+        {
+            "task_name": "gsarti/flores_101_orm",
+            "prompt_name": null,
+            "word_perplexity": 944722910.1683049
+        },
+        {
+            "task_name": "gsarti/flores_101_orm",
+            "prompt_name": null,
+            "byte_perplexity": 12.911595421079408
+        },
+        {
+            "task_name": "gsarti/flores_101_orm",
+            "prompt_name": null,
+            "bits_per_byte": 3.690595373136525
+        },
+        {
+            "task_name": "gsarti/flores_101_pus",
+            "prompt_name": null,
+            "word_perplexity": 153261.38659736273
+        },
+        {
+            "task_name": "gsarti/flores_101_pus",
+            "prompt_name": null,
+            "byte_perplexity": 4.4963371422771585
+        },
+        {
+            "task_name": "gsarti/flores_101_pus",
+            "prompt_name": null,
+            "bits_per_byte": 2.1687502151085742
+        },
+        {
+            "task_name": "gsarti/flores_101_fas",
+            "prompt_name": null,
+            "word_perplexity": 44174.10652942002
+        },
+        {
+            "task_name": "gsarti/flores_101_fas",
+            "prompt_name": null,
+            "byte_perplexity": 3.058009097116482
+        },
+        {
+            "task_name": "gsarti/flores_101_fas",
+            "prompt_name": null,
+            "bits_per_byte": 1.6125926985055565
+        },
+        {
+            "task_name": "gsarti/flores_101_pol",
+            "prompt_name": null,
+            "word_perplexity": 104253.80848720921
+        },
+        {
+            "task_name": "gsarti/flores_101_pol",
+            "prompt_name": null,
+            "byte_perplexity": 4.625550458479643
+        },
+        {
+            "task_name": "gsarti/flores_101_pol",
+            "prompt_name": null,
+            "bits_per_byte": 2.2096250621616695
+        },
+        {
+            "task_name": "gsarti/flores_101_por",
+            "prompt_name": null,
+            "word_perplexity": 70.12185258792593
+        },
+        {
+            "task_name": "gsarti/flores_101_por",
+            "prompt_name": null,
+            "byte_perplexity": 1.9754515986213523
+        },
+        {
+            "task_name": "gsarti/flores_101_por",
+            "prompt_name": null,
+            "bits_per_byte": 0.9821824986646657
+        },
+        {
+            "task_name": "gsarti/flores_101_pan",
+            "prompt_name": null,
+            "word_perplexity": 847925284.3968099
+        },
+        {
+            "task_name": "gsarti/flores_101_pan",
+            "prompt_name": null,
+            "byte_perplexity": 4.698477289331806
+        },
+        {
+            "task_name": "gsarti/flores_101_pan",
+            "prompt_name": null,
+            "bits_per_byte": 2.2321932752863454
+        },
+        {
+            "task_name": "gsarti/flores_101_ron",
+            "prompt_name": null,
+            "word_perplexity": 36440.61611845943
+        },
+        {
+            "task_name": "gsarti/flores_101_ron",
+            "prompt_name": null,
+            "byte_perplexity": 4.965456830031304
+        },
+        {
+            "task_name": "gsarti/flores_101_ron",
+            "prompt_name": null,
+            "bits_per_byte": 2.31192645412871
+        },
+        {
+            "task_name": "gsarti/flores_101_rus",
+            "prompt_name": null,
+            "word_perplexity": 12717.27557342625
+        },
+        {
+            "task_name": "gsarti/flores_101_rus",
+            "prompt_name": null,
+            "byte_perplexity": 2.0498020542445303
+        },
+        {
+            "task_name": "gsarti/flores_101_rus",
+            "prompt_name": null,
+            "bits_per_byte": 1.0354845979511649
+        },
+        {
+            "task_name": "gsarti/flores_101_srp",
+            "prompt_name": null,
+            "word_perplexity": 179094.36755355867
+        },
+        {
+            "task_name": "gsarti/flores_101_srp",
+            "prompt_name": null,
+            "byte_perplexity": 2.871214785885079
+        },
+        {
+            "task_name": "gsarti/flores_101_srp",
+            "prompt_name": null,
+            "bits_per_byte": 1.5216612577275341
+        },
+        {
+            "task_name": "gsarti/flores_101_sna",
+            "prompt_name": null,
+            "word_perplexity": 64794029.630749054
+        },
+        {
+            "task_name": "gsarti/flores_101_sna",
+            "prompt_name": null,
+            "byte_perplexity": 8.462166771382726
+        },
+        {
+            "task_name": "gsarti/flores_101_sna",
+            "prompt_name": null,
+            "bits_per_byte": 3.0810271184378166
+        },
+        {
+            "task_name": "gsarti/flores_101_snd",
+            "prompt_name": null,
+            "word_perplexity": 1593844.7987764536
+        },
+        {
+            "task_name": "gsarti/flores_101_snd",
+            "prompt_name": null,
+            "byte_perplexity": 5.466066951221973
+        },
+        {
+            "task_name": "gsarti/flores_101_snd",
+            "prompt_name": null,
+            "bits_per_byte": 2.450503130846187
+        },
+        {
+            "task_name": "gsarti/flores_101_slk",
+            "prompt_name": null,
+            "word_perplexity": 766753.5771631876
+        },
+        {
+            "task_name": "gsarti/flores_101_slk",
+            "prompt_name": null,
+            "byte_perplexity": 6.450822127057479
+        },
+        {
+            "task_name": "gsarti/flores_101_slk",
+            "prompt_name": null,
+            "bits_per_byte": 2.6894830369770566
+        },
+        {
+            "task_name": "gsarti/flores_101_slv",
+            "prompt_name": null,
+            "word_perplexity": 281495.6973621906
+        },
+        {
+            "task_name": "gsarti/flores_101_slv",
+            "prompt_name": null,
+            "byte_perplexity": 6.620252120186232
+        },
+        {
+            "task_name": "gsarti/flores_101_slv",
+            "prompt_name": null,
+            "bits_per_byte": 2.726886160479057
+        },
+        {
+            "task_name": "gsarti/flores_101_som",
+            "prompt_name": null,
+            "word_perplexity": 9117591.536991648
+        },
+        {
+            "task_name": "gsarti/flores_101_som",
+            "prompt_name": null,
+            "byte_perplexity": 11.95918054093392
+        },
+        {
+            "task_name": "gsarti/flores_101_som",
+            "prompt_name": null,
+            "bits_per_byte": 3.5800466324138576
+        },
+        {
+            "task_name": "gsarti/flores_101_ckb",
+            "prompt_name": null,
+            "word_perplexity": 7641937.513844287
+        },
+        {
+            "task_name": "gsarti/flores_101_ckb",
+            "prompt_name": null,
+            "byte_perplexity": 3.7255124939234765
+        },
+        {
+            "task_name": "gsarti/flores_101_ckb",
+            "prompt_name": null,
+            "bits_per_byte": 1.8974389011678956
+        },
+        {
+            "task_name": "gsarti/flores_101_spa",
+            "prompt_name": null,
+            "word_perplexity": 50.48600403475257
+        },
+        {
+            "task_name": "gsarti/flores_101_spa",
+            "prompt_name": null,
+            "byte_perplexity": 1.8965140104323535
+        },
+        {
+            "task_name": "gsarti/flores_101_spa",
+            "prompt_name": null,
+            "bits_per_byte": 0.9233500295317635
+        },
+        {
+            "task_name": "gsarti/flores_101_swh",
+            "prompt_name": null,
+            "word_perplexity": 4756.310957867697
+        },
+        {
+            "task_name": "gsarti/flores_101_swh",
+            "prompt_name": null,
+            "byte_perplexity": 3.6973091886730676
+        },
+        {
+            "task_name": "gsarti/flores_101_swh",
+            "prompt_name": null,
+            "bits_per_byte": 1.8864756944079395
+        },
+        {
+            "task_name": "gsarti/flores_101_swe",
+            "prompt_name": null,
+            "word_perplexity": 50609.194691403645
+        },
+        {
+            "task_name": "gsarti/flores_101_swe",
+            "prompt_name": null,
+            "byte_perplexity": 5.054972008155866
+        },
+        {
+            "task_name": "gsarti/flores_101_swe",
+            "prompt_name": null,
+            "bits_per_byte": 2.3377031032447033
+        },
+        {
+            "task_name": "gsarti/flores_101_tgk",
+            "prompt_name": null,
+            "word_perplexity": 4653242.643384356
+        },
+        {
+            "task_name": "gsarti/flores_101_tgk",
+            "prompt_name": null,
+            "byte_perplexity": 3.5994818827380426
+        },
+        {
+            "task_name": "gsarti/flores_101_tgk",
+            "prompt_name": null,
+            "bits_per_byte": 1.847789256832959
+        },
+        {
+            "task_name": "gsarti/flores_101_tam",
+            "prompt_name": null,
+            "word_perplexity": 1.7375636861561886e+16
+        },
+        {
+            "task_name": "gsarti/flores_101_tam",
+            "prompt_name": null,
+            "byte_perplexity": 4.539493400469833
+        },
+        {
+            "task_name": "gsarti/flores_101_tam",
+            "prompt_name": null,
+            "bits_per_byte": 2.182531304254031
+        },
+        {
+            "task_name": "gsarti/flores_101_tel",
+            "prompt_name": null,
+            "word_perplexity": 6240250468604343.0
+        },
+        {
+            "task_name": "gsarti/flores_101_tel",
+            "prompt_name": null,
+            "byte_perplexity": 5.807499987508966
+        },
+        {
+            "task_name": "gsarti/flores_101_tel",
+            "prompt_name": null,
+            "bits_per_byte": 2.537917245931069
+        },
+        {
+            "task_name": "gsarti/flores_101_tha",
+            "prompt_name": null,
+            "word_perplexity": 2.7023221906004898e+31
+        },
+        {
+            "task_name": "gsarti/flores_101_tha",
+            "prompt_name": null,
+            "byte_perplexity": 2.365940201944242
+        },
+        {
+            "task_name": "gsarti/flores_101_tha",
+            "prompt_name": null,
+            "bits_per_byte": 1.242413610681628
+        },
+        {
+            "task_name": "gsarti/flores_101_tur",
+            "prompt_name": null,
+            "word_perplexity": 598170.0194818947
+        },
+        {
+            "task_name": "gsarti/flores_101_tur",
+            "prompt_name": null,
+            "byte_perplexity": 4.885014749844601
+        },
+        {
+            "task_name": "gsarti/flores_101_tur",
+            "prompt_name": null,
+            "bits_per_byte": 2.288362918282818
+        },
+        {
+            "task_name": "gsarti/flores_101_ukr",
+            "prompt_name": null,
+            "word_perplexity": 375312.1511987307
+        },
+        {
+            "task_name": "gsarti/flores_101_ukr",
+            "prompt_name": null,
+            "byte_perplexity": 2.7240934990288483
+        },
+        {
+            "task_name": "gsarti/flores_101_ukr",
+            "prompt_name": null,
+            "bits_per_byte": 1.445776221804572
+        },
+        {
+            "task_name": "gsarti/flores_101_umb",
+            "prompt_name": null,
+            "word_perplexity": 286182026.84727985
+        },
+        {
+            "task_name": "gsarti/flores_101_umb",
+            "prompt_name": null,
+            "byte_perplexity": 12.766915508610673
+        },
+        {
+            "task_name": "gsarti/flores_101_umb",
+            "prompt_name": null,
+            "bits_per_byte": 3.6743381063848357
+        },
+        {
+            "task_name": "gsarti/flores_101_urd",
+            "prompt_name": null,
+            "word_perplexity": 294.7473718166965
+        },
+        {
+            "task_name": "gsarti/flores_101_urd",
+            "prompt_name": null,
+            "byte_perplexity": 1.9797467071381232
+        },
+        {
+            "task_name": "gsarti/flores_101_urd",
+            "prompt_name": null,
+            "bits_per_byte": 0.9853158607436239
+        },
+        {
+            "task_name": "gsarti/flores_101_uzb",
+            "prompt_name": null,
+            "word_perplexity": 657971096.5030558
+        },
+        {
+            "task_name": "gsarti/flores_101_uzb",
+            "prompt_name": null,
+            "byte_perplexity": 12.002337637722146
+        },
+        {
+            "task_name": "gsarti/flores_101_uzb",
+            "prompt_name": null,
+            "bits_per_byte": 3.5852435148799184
+        },
+        {
+            "task_name": "gsarti/flores_101_vie",
+            "prompt_name": null,
+            "word_perplexity": 30.113286809710246
+        },
+        {
+            "task_name": "gsarti/flores_101_vie",
+            "prompt_name": null,
+            "byte_perplexity": 1.76578415476397
+        },
+        {
+            "task_name": "gsarti/flores_101_vie",
+            "prompt_name": null,
+            "bits_per_byte": 0.8203090021691818
+        },
+        {
+            "task_name": "gsarti/flores_101_cym",
+            "prompt_name": null,
+            "word_perplexity": 2638019.4579179045
+        },
+        {
+            "task_name": "gsarti/flores_101_cym",
+            "prompt_name": null,
+            "byte_perplexity": 12.539424151448149
+        },
+        {
+            "task_name": "gsarti/flores_101_cym",
+            "prompt_name": null,
+            "bits_per_byte": 3.6483991915978407
+        },
+        {
+            "task_name": "gsarti/flores_101_wol",
+            "prompt_name": null,
+            "word_perplexity": 119795.78671768666
+        },
+        {
+            "task_name": "gsarti/flores_101_wol",
+            "prompt_name": null,
+            "byte_perplexity": 9.144285650306488
+        },
+        {
+            "task_name": "gsarti/flores_101_wol",
+            "prompt_name": null,
+            "bits_per_byte": 3.1928704713393357
+        },
+        {
+            "task_name": "gsarti/flores_101_xho",
+            "prompt_name": null,
+            "word_perplexity": 54307092.21333007
+        },
+        {
+            "task_name": "gsarti/flores_101_xho",
+            "prompt_name": null,
+            "byte_perplexity": 7.403240538286952
+        },
+        {
+            "task_name": "gsarti/flores_101_xho",
+            "prompt_name": null,
+            "bits_per_byte": 2.8881569038733983
+        },
+        {
+            "task_name": "gsarti/flores_101_yor",
+            "prompt_name": null,
+            "word_perplexity": 130267.12232132205
+        },
+        {
+            "task_name": "gsarti/flores_101_yor",
+            "prompt_name": null,
+            "byte_perplexity": 5.91272037551173
+        },
+        {
+            "task_name": "gsarti/flores_101_yor",
+            "prompt_name": null,
+            "bits_per_byte": 2.5638220507535796
+        },
+        {
+            "task_name": "gsarti/flores_101_zul",
+            "prompt_name": null,
+            "word_perplexity": 493606524.8156374
+        },
+        {
+            "task_name": "gsarti/flores_101_zul",
+            "prompt_name": null,
+            "byte_perplexity": 8.53353320693145
+        },
+        {
+            "task_name": "gsarti/flores_101_zul",
+            "prompt_name": null,
+            "bits_per_byte": 3.0931431957905224
+        }
+    ],
+    "versions": {
+        "gsarti/flores_101_kor+null": 0,
+        "gsarti/flores_101_kir+null": 0,
+        "gsarti/flores_101_lao+null": 0,
+        "gsarti/flores_101_lav+null": 0,
+        "gsarti/flores_101_lin+null": 0,
+        "gsarti/flores_101_lit+null": 0,
+        "gsarti/flores_101_luo+null": 0,
+        "gsarti/flores_101_ltz+null": 0,
+        "gsarti/flores_101_mkd+null": 0,
+        "gsarti/flores_101_msa+null": 0,
+        "gsarti/flores_101_mal+null": 0,
+        "gsarti/flores_101_mlt+null": 0,
+        "gsarti/flores_101_mri+null": 0,
+        "gsarti/flores_101_mar+null": 0,
+        "gsarti/flores_101_mon+null": 0,
+        "gsarti/flores_101_npi+null": 0,
+        "gsarti/flores_101_nso+null": 0,
+        "gsarti/flores_101_nob+null": 0,
+        "gsarti/flores_101_nya+null": 0,
+        "gsarti/flores_101_oci+null": 0,
+        "gsarti/flores_101_ory+null": 0,
+        "gsarti/flores_101_orm+null": 0,
+        "gsarti/flores_101_pus+null": 0,
+        "gsarti/flores_101_fas+null": 0,
+        "gsarti/flores_101_pol+null": 0,
+        "gsarti/flores_101_por+null": 0,
+        "gsarti/flores_101_pan+null": 0,
+        "gsarti/flores_101_ron+null": 0,
+        "gsarti/flores_101_rus+null": 0,
+        "gsarti/flores_101_srp+null": 0,
+        "gsarti/flores_101_sna+null": 0,
+        "gsarti/flores_101_snd+null": 0,
+        "gsarti/flores_101_slk+null": 0,
+        "gsarti/flores_101_slv+null": 0,
+        "gsarti/flores_101_som+null": 0,
+        "gsarti/flores_101_ckb+null": 0,
+        "gsarti/flores_101_spa+null": 0,
+        "gsarti/flores_101_swh+null": 0,
+        "gsarti/flores_101_swe+null": 0,
+        "gsarti/flores_101_tgk+null": 0,
+        "gsarti/flores_101_tam+null": 0,
+        "gsarti/flores_101_tel+null": 0,
+        "gsarti/flores_101_tha+null": 0,
+        "gsarti/flores_101_tur+null": 0,
+        "gsarti/flores_101_ukr+null": 0,
+        "gsarti/flores_101_umb+null": 0,
+        "gsarti/flores_101_urd+null": 0,
+        "gsarti/flores_101_uzb+null": 0,
+        "gsarti/flores_101_vie+null": 0,
+        "gsarti/flores_101_cym+null": 0,
+        "gsarti/flores_101_wol+null": 0,
+        "gsarti/flores_101_xho+null": 0,
+        "gsarti/flores_101_yor+null": 0,
+        "gsarti/flores_101_zul+null": 0
+    },
+    "table_results": {
+        "gsarti/flores_101_kor+null": {
+            "task_name": "gsarti/flores_101_kor",
+            "prompt_name": "null",
+            "word_perplexity": 1199924.6918920355,
+            "byte_perplexity": 3.932884847226212,
+            "bits_per_byte": 1.9755879455567535
+        },
+        "gsarti/flores_101_kir+null": {
+            "task_name": "gsarti/flores_101_kir",
+            "prompt_name": "null",
+            "word_perplexity": 140474672.36703426,
+            "byte_perplexity": 3.729278369847201,
+            "bits_per_byte": 1.8988964902756764
+        },
+        "gsarti/flores_101_lao+null": {
+            "task_name": "gsarti/flores_101_lao",
+            "prompt_name": "null",
+            "word_perplexity": 6.1350041352351446e+26,
+            "byte_perplexity": 2.9077314760849924,
+            "bits_per_byte": 1.5398940450457603
+        },
+        "gsarti/flores_101_lav+null": {
+            "task_name": "gsarti/flores_101_lav",
+            "prompt_name": "null",
+            "word_perplexity": 10925745.685132286,
+            "byte_perplexity": 7.777221919194806,
+            "bits_per_byte": 2.959254905963978
+        },
+        "gsarti/flores_101_lin+null": {
+            "task_name": "gsarti/flores_101_lin",
+            "prompt_name": "null",
+            "word_perplexity": 166841.83897098716,
+            "byte_perplexity": 7.524842908050988,
+            "bits_per_byte": 2.9116614638468965
+        },
+        "gsarti/flores_101_lit+null": {
+            "task_name": "gsarti/flores_101_lit",
+            "prompt_name": "null",
+            "word_perplexity": 8532364.031813102,
+            "byte_perplexity": 7.369179434621725,
+            "bits_per_byte": 2.88150398275188
+        },
+        "gsarti/flores_101_luo+null": {
+            "task_name": "gsarti/flores_101_luo",
+            "prompt_name": "null",
+            "word_perplexity": 1335199.656768974,
+            "byte_perplexity": 11.975963093623681,
+            "bits_per_byte": 3.5820697754437467
+        },
+        "gsarti/flores_101_ltz+null": {
+            "task_name": "gsarti/flores_101_ltz",
+            "prompt_name": "null",
+            "word_perplexity": 4081613.1281958995,
+            "byte_perplexity": 8.801059747949214,
+            "bits_per_byte": 3.1376772511430198
+        },
+        "gsarti/flores_101_mkd+null": {
+            "task_name": "gsarti/flores_101_mkd",
+            "prompt_name": "null",
+            "word_perplexity": 291548.6603872499,
+            "byte_perplexity": 2.9656732291754087,
+            "bits_per_byte": 1.5683596441110415
+        },
+        "gsarti/flores_101_msa+null": {
+            "task_name": "gsarti/flores_101_msa",
+            "prompt_name": "null",
+            "word_perplexity": 931.4191160965655,
+            "byte_perplexity": 2.5710001772665634,
+            "bits_per_byte": 1.3623297096432079
+        },
+        "gsarti/flores_101_mal+null": {
+            "task_name": "gsarti/flores_101_mal",
+            "prompt_name": "null",
+            "word_perplexity": 1.207348615509252e+18,
+            "byte_perplexity": 4.615948455160037,
+            "bits_per_byte": 2.2066271139530245
+        },
+        "gsarti/flores_101_mlt+null": {
+            "task_name": "gsarti/flores_101_mlt",
+            "prompt_name": "null",
+            "word_perplexity": 1820552051.5260184,
+            "byte_perplexity": 15.004773437665275,
+            "bits_per_byte": 3.9073496302297994
+        },
+        "gsarti/flores_101_mri+null": {
+            "task_name": "gsarti/flores_101_mri",
+            "prompt_name": "null",
+            "word_perplexity": 26466.98082941409,
+            "byte_perplexity": 7.474035895661322,
+            "bits_per_byte": 2.9018874925878335
+        },
+        "gsarti/flores_101_mar+null": {
+            "task_name": "gsarti/flores_101_mar",
+            "prompt_name": "null",
+            "word_perplexity": 54017030487867.64,
+            "byte_perplexity": 5.483253482821379,
+            "bits_per_byte": 2.4550321688665875
+        },
+        "gsarti/flores_101_mon+null": {
+            "task_name": "gsarti/flores_101_mon",
+            "prompt_name": "null",
+            "word_perplexity": 6612951.176601774,
+            "byte_perplexity": 3.410598542315402,
+            "bits_per_byte": 1.7700249469487581
+        },
+        "gsarti/flores_101_npi+null": {
+            "task_name": "gsarti/flores_101_npi",
+            "prompt_name": "null",
+            "word_perplexity": 9218412485042.457,
+            "byte_perplexity": 5.199342701937889,
+            "bits_per_byte": 2.3783292500628397
+        },
+        "gsarti/flores_101_nso+null": {
+            "task_name": "gsarti/flores_101_nso",
+            "prompt_name": "null",
+            "word_perplexity": 84236.45826211123,
+            "byte_perplexity": 8.154626800955667,
+            "bits_per_byte": 3.027618853058479
+        },
+        "gsarti/flores_101_nob+null": {
+            "task_name": "gsarti/flores_101_nob",
+            "prompt_name": "null",
+            "word_perplexity": 36969.51682419191,
+            "byte_perplexity": 5.402763169129877,
+            "bits_per_byte": 2.4336974426149056
+        },
+        "gsarti/flores_101_nya+null": {
+            "task_name": "gsarti/flores_101_nya",
+            "prompt_name": "null",
+            "word_perplexity": 6609896.030066139,
+            "byte_perplexity": 8.179860208369393,
+            "bits_per_byte": 3.0320761881040017
+        },
+        "gsarti/flores_101_oci+null": {
+            "task_name": "gsarti/flores_101_oci",
+            "prompt_name": "null",
+            "word_perplexity": 21641.316763505896,
+            "byte_perplexity": 4.8617357393685845,
+            "bits_per_byte": 2.2814714775164466
+        },
+        "gsarti/flores_101_ory+null": {
+            "task_name": "gsarti/flores_101_ory",
+            "prompt_name": "null",
+            "word_perplexity": 11873283711992.748,
+            "byte_perplexity": 5.189421861225964,
+            "bits_per_byte": 2.375573820972048
+        },
+        "gsarti/flores_101_orm+null": {
+            "task_name": "gsarti/flores_101_orm",
+            "prompt_name": "null",
+            "word_perplexity": 944722910.1683049,
+            "byte_perplexity": 12.911595421079408,
+            "bits_per_byte": 3.690595373136525
+        },
+        "gsarti/flores_101_pus+null": {
+            "task_name": "gsarti/flores_101_pus",
+            "prompt_name": "null",
+            "word_perplexity": 153261.38659736273,
+            "byte_perplexity": 4.4963371422771585,
+            "bits_per_byte": 2.1687502151085742
+        },
+        "gsarti/flores_101_fas+null": {
+            "task_name": "gsarti/flores_101_fas",
+            "prompt_name": "null",
+            "word_perplexity": 44174.10652942002,
+            "byte_perplexity": 3.058009097116482,
+            "bits_per_byte": 1.6125926985055565
+        },
+        "gsarti/flores_101_pol+null": {
+            "task_name": "gsarti/flores_101_pol",
+            "prompt_name": "null",
+            "word_perplexity": 104253.80848720921,
+            "byte_perplexity": 4.625550458479643,
+            "bits_per_byte": 2.2096250621616695
+        },
+        "gsarti/flores_101_por+null": {
+            "task_name": "gsarti/flores_101_por",
+            "prompt_name": "null",
+            "word_perplexity": 70.12185258792593,
+            "byte_perplexity": 1.9754515986213523,
+            "bits_per_byte": 0.9821824986646657
+        },
+        "gsarti/flores_101_pan+null": {
+            "task_name": "gsarti/flores_101_pan",
+            "prompt_name": "null",
+            "word_perplexity": 847925284.3968099,
+            "byte_perplexity": 4.698477289331806,
+            "bits_per_byte": 2.2321932752863454
+        },
+        "gsarti/flores_101_ron+null": {
+            "task_name": "gsarti/flores_101_ron",
+            "prompt_name": "null",
+            "word_perplexity": 36440.61611845943,
+            "byte_perplexity": 4.965456830031304,
+            "bits_per_byte": 2.31192645412871
+        },
+        "gsarti/flores_101_rus+null": {
+            "task_name": "gsarti/flores_101_rus",
+            "prompt_name": "null",
+            "word_perplexity": 12717.27557342625,
+            "byte_perplexity": 2.0498020542445303,
+            "bits_per_byte": 1.0354845979511649
+        },
+        "gsarti/flores_101_srp+null": {
+            "task_name": "gsarti/flores_101_srp",
+            "prompt_name": "null",
+            "word_perplexity": 179094.36755355867,
+            "byte_perplexity": 2.871214785885079,
+            "bits_per_byte": 1.5216612577275341
+        },
+        "gsarti/flores_101_sna+null": {
+            "task_name": "gsarti/flores_101_sna",
+            "prompt_name": "null",
+            "word_perplexity": 64794029.630749054,
+            "byte_perplexity": 8.462166771382726,
+            "bits_per_byte": 3.0810271184378166
+        },
+        "gsarti/flores_101_snd+null": {
+            "task_name": "gsarti/flores_101_snd",
+            "prompt_name": "null",
+            "word_perplexity": 1593844.7987764536,
+            "byte_perplexity": 5.466066951221973,
+            "bits_per_byte": 2.450503130846187
+        },
+        "gsarti/flores_101_slk+null": {
+            "task_name": "gsarti/flores_101_slk",
+            "prompt_name": "null",
+            "word_perplexity": 766753.5771631876,
+            "byte_perplexity": 6.450822127057479,
+            "bits_per_byte": 2.6894830369770566
+        },
+        "gsarti/flores_101_slv+null": {
+            "task_name": "gsarti/flores_101_slv",
+            "prompt_name": "null",
+            "word_perplexity": 281495.6973621906,
+            "byte_perplexity": 6.620252120186232,
+            "bits_per_byte": 2.726886160479057
+        },
+        "gsarti/flores_101_som+null": {
+            "task_name": "gsarti/flores_101_som",
+            "prompt_name": "null",
+            "word_perplexity": 9117591.536991648,
+            "byte_perplexity": 11.95918054093392,
+            "bits_per_byte": 3.5800466324138576
+        },
+        "gsarti/flores_101_ckb+null": {
+            "task_name": "gsarti/flores_101_ckb",
+            "prompt_name": "null",
+            "word_perplexity": 7641937.513844287,
+            "byte_perplexity": 3.7255124939234765,
+            "bits_per_byte": 1.8974389011678956
+        },
+        "gsarti/flores_101_spa+null": {
+            "task_name": "gsarti/flores_101_spa",
+            "prompt_name": "null",
+            "word_perplexity": 50.48600403475257,
+            "byte_perplexity": 1.8965140104323535,
+            "bits_per_byte": 0.9233500295317635
+        },
+        "gsarti/flores_101_swh+null": {
+            "task_name": "gsarti/flores_101_swh",
+            "prompt_name": "null",
+            "word_perplexity": 4756.310957867697,
+            "byte_perplexity": 3.6973091886730676,
+            "bits_per_byte": 1.8864756944079395
+        },
+        "gsarti/flores_101_swe+null": {
+            "task_name": "gsarti/flores_101_swe",
+            "prompt_name": "null",
+            "word_perplexity": 50609.194691403645,
+            "byte_perplexity": 5.054972008155866,
+            "bits_per_byte": 2.3377031032447033
+        },
+        "gsarti/flores_101_tgk+null": {
+            "task_name": "gsarti/flores_101_tgk",
+            "prompt_name": "null",
+            "word_perplexity": 4653242.643384356,
+            "byte_perplexity": 3.5994818827380426,
+            "bits_per_byte": 1.847789256832959
+        },
+        "gsarti/flores_101_tam+null": {
+            "task_name": "gsarti/flores_101_tam",
+            "prompt_name": "null",
+            "word_perplexity": 1.7375636861561886e+16,
+            "byte_perplexity": 4.539493400469833,
+            "bits_per_byte": 2.182531304254031
+        },
+        "gsarti/flores_101_tel+null": {
+            "task_name": "gsarti/flores_101_tel",
+            "prompt_name": "null",
+            "word_perplexity": 6240250468604343.0,
+            "byte_perplexity": 5.807499987508966,
+            "bits_per_byte": 2.537917245931069
+        },
+        "gsarti/flores_101_tha+null": {
+            "task_name": "gsarti/flores_101_tha",
+            "prompt_name": "null",
+            "word_perplexity": 2.7023221906004898e+31,
+            "byte_perplexity": 2.365940201944242,
+            "bits_per_byte": 1.242413610681628
+        },
+        "gsarti/flores_101_tur+null": {
+            "task_name": "gsarti/flores_101_tur",
+            "prompt_name": "null",
+            "word_perplexity": 598170.0194818947,
+            "byte_perplexity": 4.885014749844601,
+            "bits_per_byte": 2.288362918282818
+        },
+        "gsarti/flores_101_ukr+null": {
+            "task_name": "gsarti/flores_101_ukr",
+            "prompt_name": "null",
+            "word_perplexity": 375312.1511987307,
+            "byte_perplexity": 2.7240934990288483,
+            "bits_per_byte": 1.445776221804572
+        },
+        "gsarti/flores_101_umb+null": {
+            "task_name": "gsarti/flores_101_umb",
+            "prompt_name": "null",
+            "word_perplexity": 286182026.84727985,
+            "byte_perplexity": 12.766915508610673,
+            "bits_per_byte": 3.6743381063848357
+        },
+        "gsarti/flores_101_urd+null": {
+            "task_name": "gsarti/flores_101_urd",
+            "prompt_name": "null",
+            "word_perplexity": 294.7473718166965,
+            "byte_perplexity": 1.9797467071381232,
+            "bits_per_byte": 0.9853158607436239
+        },
+        "gsarti/flores_101_uzb+null": {
+            "task_name": "gsarti/flores_101_uzb",
+            "prompt_name": "null",
+            "word_perplexity": 657971096.5030558,
+            "byte_perplexity": 12.002337637722146,
+            "bits_per_byte": 3.5852435148799184
+        },
+        "gsarti/flores_101_vie+null": {
+            "task_name": "gsarti/flores_101_vie",
+            "prompt_name": "null",
+            "word_perplexity": 30.113286809710246,
+            "byte_perplexity": 1.76578415476397,
+            "bits_per_byte": 0.8203090021691818
+        },
+        "gsarti/flores_101_cym+null": {
+            "task_name": "gsarti/flores_101_cym",
+            "prompt_name": "null",
+            "word_perplexity": 2638019.4579179045,
+            "byte_perplexity": 12.539424151448149,
+            "bits_per_byte": 3.6483991915978407
+        },
+        "gsarti/flores_101_wol+null": {
+            "task_name": "gsarti/flores_101_wol",
+            "prompt_name": "null",
+            "word_perplexity": 119795.78671768666,
+            "byte_perplexity": 9.144285650306488,
+            "bits_per_byte": 3.1928704713393357
+        },
+        "gsarti/flores_101_xho+null": {
+            "task_name": "gsarti/flores_101_xho",
+            "prompt_name": "null",
+            "word_perplexity": 54307092.21333007,
+            "byte_perplexity": 7.403240538286952,
+            "bits_per_byte": 2.8881569038733983
+        },
+        "gsarti/flores_101_yor+null": {
+            "task_name": "gsarti/flores_101_yor",
+            "prompt_name": "null",
+            "word_perplexity": 130267.12232132205,
+            "byte_perplexity": 5.91272037551173,
+            "bits_per_byte": 2.5638220507535796
+        },
+        "gsarti/flores_101_zul+null": {
+            "task_name": "gsarti/flores_101_zul",
+            "prompt_name": "null",
+            "word_perplexity": 493606524.8156374,
+            "byte_perplexity": 8.53353320693145,
+            "bits_per_byte": 3.0931431957905224
+        }
+    },
+    "config": {
+        "adaptive_seq_len": true,
+        "num_fewshot": 0,
+        "bootstrap_iters": 100000
+    }
+}

evaluation/results/tr11/bloom350m/humaneval_temp02.json

@@ -0,0 +1 @@
+{"pass@1": 0.00817073170731707, "pass@10": 0.020465171677199096, "pass@100": 0.024390015529347924}

evaluation/results/tr11/bloom350m/humaneval_temp06.json

@@ -0,0 +1 @@
+{"pass@1": 0.0064939024390243925, "pass@10": 0.030182658898012457, "pass@100": 0.06233670887015886}

evaluation/results/tr11/get_templates.sh

@@ -0,0 +1,27 @@
+DATASETS_AND_CONFIGS=(
+piaf,None,None
+GEM/wiki_lingua,ar,ar
+GEM/wiki_lingua,en,en
+GEM/wiki_lingua,es,es
+GEM/wiki_lingua,fr,fr
+GEM/wiki_lingua,hi,hi
+GEM/wiki_lingua,id,id
+GEM/wiki_lingua,pt,pt
+GEM/wiki_lingua,vi,vi
+GEM/wiki_lingua,zh,zh
+GEM/web_nlg,en,en
+GEM/web_nlg,ru,ru
+wmt14,fr-en,fr-en
+)
+
+# Unique ones: 0 1 2 5 6 7 8 9 10 11
+for val in {0..12}; do
+    DATASET_AND_CONFIG=${DATASETS_AND_CONFIGS[$val]}
+    IFS=',' read dataset_name dataset_config_name template_config_name <<< "${DATASET_AND_CONFIG}"
+    echo $dataset_config_name
+    python evaluation/results/tr13/tzeroeval/get_templates.py \
+            --dataset_name $dataset_name \
+            --dataset_config_name $dataset_config_name \
+            --template_config_name $template_config_name
+done
+

evaluation/results/tr11/opt/humaneval_temp02.json

@@ -0,0 +1 @@
+{"pass@1": 0.0, "pass@10": 0.0, "pass@100": 0.0}

evaluation/results/tr11/opt/humaneval_temp08.json

@@ -0,0 +1 @@
+{"pass@1": 0.0, "pass@10": 0.0, "pass@100": 0.0}

evaluation/results/tr11/scripts/download.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(task_list)
+
+if __name__ == '__main__':
+    main()
+

evaluation/results/tr11/scripts/multi_eurlex_tmp.slurm

@@ -0,0 +1,63 @@
+#!/bin/bash
+#SBATCH --job-name=multieurlex
+#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=$six_ALL_CCFRWORK/metrics
+export HF_DATASETS_OFFLINE=1
+export TRANSFORMERS_OFFLINE=1
+export TOKENIZERS_PARALLELISM=false
+
+# Converted transformer checkpoint
+#MODEL_CKPT=/gpfsscratch/rech/six/commun/experiments/muennighoff/bloomckpt/6b3/bloom-7b1
+MODEL_CKPT=/gpfsscratch/rech/six/commun/experiments/muennighoff/bloomckpt/6b3t0/tr13f-6b3-ml-t0-lmtoks341b-t0toks13b-xp3capmixv2lossseq
+
+cd /gpfsscratch/rech/six/commun/experiments/muennighoff/bslmevalgeneration/lm-evaluation-harness
+
+DATASETS_AND_CONFIGS=(
+multi_eurlex_mt,multi,"version-fr-en-source+target"
+multi_eurlex_mt,multi,"version-en-fr-source+target"
+multi_eurlex_mt,multi,"a_good_translation-fr-en-source+target"
+multi_eurlex_mt,multi,"a_good_translation-en-fr-source+target"
+multi_eurlex_mt,multi,"prev_doc-en-fr"
+multi_eurlex_mt,multi,"prev_doc-fr-en"
+)
+
+DATASET_AND_CONFIG=${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 \
+    --num_fewshot 0 \
+    --limit 500
+
+echo "END TIME: $(date)"

evaluation/results/tr11/scripts/report-to-csv.py

@@ -0,0 +1,58 @@
+#!/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 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:
+    results = json.load(f)
+
+with io.open(csv_file, 'w', encoding='utf-8') as f:
+
+    writer = csv.writer(f)
+    writer.writerow(["task", "metric", "value", "err", "version"])
+
+    versions = results["versions"]
+
+    for k,v in sorted(results["results"].items()):
+        if k not in versions:
+            versions[k] = -1
+
+        if "acc" in v:
+            writer.writerow([k, "acc", v["acc"], v["acc_stderr"], versions[k]])
+        if "acc_norm" in v:
+            writer.writerow([k, "acc_norm", v["acc_norm"], v["acc_norm_stderr"], versions[k]])
+        if "f1" in v:
+            writer.writerow([k, "f1", v["f1"], v["f1_stderr"] if "f1_stderr" in v else "", versions[k]])
+        # if "ppl" in v:
+        #     writer.writerow([k, "ppl", v["ppl"], v["ppl_stderr"], versions[k]])
+        # if "em" in v:
+        #     writer.writerow([k, "em", v["em"], v["em_stderr"] if "em_stderr" in v else "", versions[k]])

evaluation/results/tr11/scripts/run_bsevalharness_generation_176b.slurm

@@ -0,0 +1,128 @@
+#!/bin/bash
+#SBATCH --job-name=genbseval
+#SBATCH --partition=gpu_p5
+#SBATCH --constraint=a100
+#SBATCH --reservation=hug
+#SBATCH --qos=qos_gpu-gc             # up to 100h
+#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 100:00:00             # 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/uan68tv-model-conversion/bloom
+
+cd /gpfsscratch/rech/six/commun/experiments/muennighoff/bslmevalgeneration/lm-evaluation-harness
+
+
+DATASETS_AND_CONFIGS=(
+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_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_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"
+)
+
+DATASETS_AND_CONFIGS=(
+wmt14_fr_en,fr-en,"version-en-fr-target"
+wmt14_fr_en,fr-en,"a_good_translation-en-fr-target"
+wmt14_fr_en,fr-en,"a_good_translation-en-fr-source+target"
+wmt14_fr_en,fr-en,"xglm-en-fr-target"
+wmt14_fr_en,fr-en,"gpt3-en-fr"
+wmt14_fr_en,fr-en,"version-fr-en-target"
+wmt14_fr_en,fr-en,"a_good_translation-fr-en-target"
+wmt14_fr_en,fr-en,"a_good_translation-fr-en-source+target"
+wmt14_fr_en,fr-en,"xglm-fr-en-target"
+wmt14_fr_en,fr-en,"gpt3-fr-en"
+)
+
+DATASETS_AND_CONFIGS=(
+GEM/web_nlg_en,en,"PALM_prompt"
+GEM/web_nlg_en,en,"explicit-graph-description-2"
+GEM/web_nlg_en,en,"implicit-graph-description"
+GEM/web_nlg_en,en,"non-explicit-description"
+GEM/web_nlg_en,en,"use-category"
+GEM/web_nlg_ru,ru,"PALM_prompt"
+GEM/web_nlg_ru,ru,"explicit-graph-description-2-Russian"
+GEM/web_nlg_ru,ru,"implicit-graph-description-Russian"
+GEM/web_nlg_ru,ru,"non-explicit-description-Russian"
+GEM/web_nlg_ru,ru,"use-category-Russian"
+)
+
+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=bfloat16 \
+    --device cuda \
+    --batch_size 8 \
+    --no_tracking \
+    --task_name $dataset_name \
+    --template_names $template_name \
+    --bootstrap_iters 10
+
+echo "END TIME: $(date)"

evaluation/results/tr11/scripts/run_bsevalharness_generation_350m.slurm

@@ -0,0 +1,110 @@
+#!/bin/bash
+#SBATCH --job-name=run_bsevalharness-generation-350m
+#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-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/commun/experiments/muennighoff/bloomckpt/350m/bloom-350m
+
+cd /gpfsscratch/rech/six/commun/experiments/muennighoff/bslmevalgeneration/lm-evaluation-harness
+
+# WMT19 ZH-EN does not work
+DATASETS_AND_CONFIGS=(
+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_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_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"
+)
+
+#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_generation_760m.slurm

@@ -0,0 +1,110 @@
+#!/bin/bash
+#SBATCH --job-name=run_bsevalharness-generation-760m
+#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-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/760m/bloom-760m
+
+cd /gpfsscratch/rech/six/commun/experiments/muennighoff/bslmevalgeneration/lm-evaluation-harness
+
+# WMT19 ZH-EN does not work
+DATASETS_AND_CONFIGS=(
+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_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_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"
+)
+
+#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_tr11c-2b5-ml.slurm

@@ -0,0 +1,121 @@
+#!/bin/bash
+#SBATCH --job-name=run_bsevalharness-tr11c-2b5-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: $(date)"
+
+# a unique identifier for the current eval ideally correspnding to the modelname
+VARIANT="tr11c-2b5-ml-bsevalharness"
+
+
+CHECKPOINT_PATH=$six_ALL_CCFRSCRATCH/checkpoints/tr11c-2B5-ml/checkpoints/main/global_step337250
+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_tr11e-350m-ml.slurm

@@ -0,0 +1,120 @@
+#!/bin/bash
+#SBATCH --job-name=run_bsevalharness-tr11e-350m-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: $(date)"
+
+# a unique identifier for the current eval ideally correspnding to the modelname
+VARIANT="tr11e-350m-ml-bsevalharness"
+
+
+CHECKPOINT_PATH=$six_ALL_CCFRSCRATCH/checkpoints/tr11e-350M-ml/checkpoints/main/global_step659500
+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=$six_ALL_CCFRWORK/datasetseval
+export HF_MODULES_CACHE=$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 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_bsevalharness_tr11f-6b3-ml.slurm

@@ -0,0 +1,121 @@
+#!/bin/bash
+#SBATCH --job-name=run_bsevalharness-tr11f-6b3-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: $(date)"
+
+# a unique identifier for the current eval ideally correspnding to the modelname
+VARIANT="tr11f-6b3-ml-bsevalharness"
+
+
+CHECKPOINT_PATH=$six_ALL_CCFRSCRATCH/checkpoints/tr11f-6B3-ml/checkpoints/main/global_step337500
+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=$six_ALL_CCFRWORK/datasetseval
+export HF_MODULES_CACHE=$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_evalharness_deepspeed.md

@@ -0,0 +1,158 @@
+# How to run lm-eval on Megatron-DeepSpeed checkpoint using the original setup
+
+This particular setup uses the normal deepspeed checkpoint and requires no conversion to Megatron-LM.
+
+This doc assumes usage on JZ, so some peculiar requirements in places. Ignore these if you're not running this on JZ.
+
+## Prerequisites
+
+1. Install software
+
+On login console with external network
+
+Get lm-eval harness (https://github.com/EleutherAI/lm-evaluation-harness) and `best-download==0.0.7` needed to download some tasks.
+```
+start-prod
+pip install best-download==0.0.7
+pip install git+https://github.com/EleutherAI/lm-evaluation-harness
+```
+
+2. Pre-download needed datasets
+
+some symlinks due to lm-harness' issues with relative position of data
+```
+mkdir data
+ln -s `pwd`/data tasks/eval_harness/data
+```
+Also make sure `data` is not on one of the limited paritions like WORKSF.
+
+Then install datasets for the tasks:
+```
+python ./tasks/eval_harness/download.py --task_list
+arc_challenge,arc_easy,boolq,copa,hellaswag,lambada,logiqa,mathqa,mc_taco,mrpc,multirc,openbookqa,piqa,prost,pubmedqa,qnli,qqp,race,rte,sciq,sst,triviaqa,webqs,wic,winogrande,wnli,wsc
+```
+and make sure that `export HF_DATASETS_OFFLINE=1`
+
+If there are things like custom tokenizers, pre-download those too, e.g.:
+
+```
+python -c "from transformers import AutoTokenizer; AutoTokenizer.from_pretrained('bigscience/oscar_13_languages_alpha_weight')"
+```
+and make sure that `export TRANSFORMERS_OFFLINE=1` is in the script.
+You know there is a custom tokenizer if the training script had something like:
+
+```
+--tokenizer-type PretrainedFromHF \
+ --tokenizer-name-or-path bigscience/oscar_13_languages_alpha_weight \
+```
+
+3. Prepare the slurm script
+
+Prepare the run script, replace `variant` with a unique identifier for the current eval so that multiple evals could run in parallel and not all log into the same `results.json` file. so, e.g., `tr9c-1B3-swiglu`
+
+```
+cp examples/run_evalharness_deepspeed.slurm run_evalharness-variant.slurm
+```
+
+now edit `run_evalharness-variant.slurm`
+
+
+Note that the eval code knows to pull the original training args from the checkpoint, so we don't need to pass any of those. And we just need to setup the evaluation args.
+
+Note that for the bigscience lm-eval-harness fork (https://github.com/bigscience-workshop/lm-evaluation-harness), the corresponding scripts are `evaluate_bsevalharness.py` & `run_bsevalharness_tr11-176b-ml.slurm`.
+
+1. Edit:
+
+```
+PP_SIZE=1
+TP_SIZE=1
+```
+to match the eval topology. If the model fits into 1 gpu, then there is nothing to change.
+
+The eval script will automatically reshape the model if it was of a different topology.
+
+
+2. Adjust the following to fit the chosen GPU. As of last check for 1.3B model the settings are one of:
+```
+EVAL_MICRO_BATCH_SIZE=6  # 16GB GPU 1.3B model
+EVAL_MICRO_BATCH_SIZE=12 # 32GB GPU 1.3B model
+```
+
+If you get OOM lower it further.
+
+3. If not using the Deepspeed path, disable it by removing:
+
+```
+    --deepspeed \
+    --deepspeed_config ds_config.json \
+```
+
+If you didn't disable it and the program crashed on checkpoint loading unable to find some key, disable deepspeed as explained above.
+
+4. Additional flags
+
+- To reduce the amount of iterations for stderr estimation, use e.g. `--bootstrap_iters 2`. This saves 1-2 minutes per dataset.
+- To print intermediate results when running multiple tasks use `--intermed_results`.
+- To reduce the bubble when setting PP use the flag `--micro_bs_multiplier`. Reducing `--micro-batch-size` may be needed when increasing the multiplier. 
+    - Running the 176B model with PP=8, `--micro_bs_multiplier 8` & `--micro-batch-size 4` produced the fastest results for PiQA on 1 node in 2min18s.
+
+## Eval
+
+Currently it takes 2-3 hours to run on 32GB for 1.3B model, 6-7h for 16GB GPU, so a 20h slurm job should be enough.
+
+When ready, launch:
+```
+sbatch ./run_evalharness-variant.slurm
+```
+
+To monitor progress:
+```
+tail -f tail -f $VARIANT-eval-harness.log
+```
+where the variant is what you set `$VARIANT` to in the slurm script.
+
+The template is set up for 16GB gpu since they are easier to get by. If you change to 32GB, adjust:
+```
+#SBATCH --constraint=v100-32g
+...
+EVAL_MICRO_BATCH_SIZE=12 # 32GB GPU 1.3B model
+```
+
+
+Note that the original ETA at the start of the run can be 10x too longer than the actual outcome. For example it may suggest 18 hours but will complete in 2 hours.
+
+
+## Short eval
+
+if you just want to quickly test that everything can run to the end, edit `tasks/eval_harness/evaluate.py`,  e.g. to run only 10 batches:
+```
+- results = evaluator.evaluate(adaptor, task_dict, False, 0, None)
++ results = evaluator.evaluate(adaptor, task_dict, False, 0, 10)
+```
+
+(XXX: could be a cmd line option so that code won't need to be modified)
+
+
+## Import into spreadsheet
+
+https://docs.google.com/spreadsheets/d/1CI8Q9RCblLRzUOPJ6ViqBmo284-8ojluQ-CmaEuhuv0/edit?usp=sharing
+
+Note that the spreadsheet format is quite different, so use this script:
+```
+./tasks/eval_harness/report-to-csv.py results.json
+```
+to reformat the json results into csv while changing its shape to match the spreadsheet format
+
+Since some records might be missing or extraneous here is the best way to do it:
+
+1. copy the data from first 2 columns to some place under the main spreadsheet
+
+2. put the pointer to the 3rd column next to where the 2 first columns were copied.
+
+3. import `results.csv` using file-> import -> file ->
+
+Import location: Replace data at selected cell
+
+4. Now it should be easy to align the new records with the old ones - delete irrelevant records and Insert->Cells where data is missing until the first 2 columns match
+
+5. now create 2 cols in the main table on top and now it should be safe to Copy-n-Paste the 2-col data range, without the task/metrics columns into the newly created space.

evaluation/results/tr11/scripts/run_evalharness_deepspeed.slurm

@@ -0,0 +1,98 @@
+#!/bin/bash
+#SBATCH --job-name=eval-harness-deepspeed
+#SBATCH --constraint=v100-16g
+#SBATCH --nodes=1
+#SBATCH --ntasks-per-node=1          # crucial - only 1 task per dist per node!
+#SBATCH --cpus-per-task=40           # 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@gpu
+
+
+set -x -e
+
+source $six_ALL_CCFRWORK/start-prod
+
+echo "START TIME: $(date)"
+
+# a unique identifier for the current eval so that multiple evals could run in parallel and not all log into the same "results.json" file.
+VARIANT="tr9c-1B3-swiglu"
+
+CHECKPOINT_PATH=/gpfsdsstore/projects/rech/six/commun/checkpoints/tr3m-1B3-emb-norm-pile/global_step296023
+MEGATRON_DEEPSPEED_REPO=/gpfsssd/worksf/projects/rech/six/commun/code/eval/Megatron-DeepSpeed
+
+# you want these 2 on JZ, and pre-download/cache any datasets/tokenizers/models
+# but comment these out if you're running on a node with Internet access
+export HF_DATASETS_OFFLINE=1
+export TRANSFORMERS_OFFLINE=1
+
+cd $MEGATRON_DEEPSPEED_REPO
+
+# eval topology
+PP_SIZE=1
+TP_SIZE=1
+
+VOCAB_FILE=$MEGATRON_DEEPSPEED_REPO/data/gpt2-vocab.json
+MERGE_FILE=$MEGATRON_DEEPSPEED_REPO/data/gpt2-merges.txt
+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=6  # 16GB GPU 1.3B model
+#EVAL_MICRO_BATCH_SIZE=12 # 32GB GPU 1.3B model
+
+
+#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"
+cat <<EOT > $config_json
+{
+  "train_micro_batch_size_per_gpu": 1,
+  "train_batch_size": 1,
+  "zero_optimization": { "stage": $ZERO_STAGE },
+  "fp16": { "enabled": true },
+  "steps_per_print": 2000,
+  "wall_clock_breakdown": false
+}
+EOT
+
+CMD="./tasks/eval_harness/evaluate.py  \
+    --load $CHECKPOINT_PATH \
+    --results_path $VARIANT-results.json \
+    --tensor-model-parallel-size $TP_SIZE  \
+    --pipeline-model-parallel-size $PP_SIZE \
+    --vocab-file $VOCAB_FILE \
+    --merge-file $MERGE_FILE \
+    --micro-batch-size $EVAL_MICRO_BATCH_SIZE \
+    --no-load-optim \
+    --no-load-rng \
+    --inference \
+    --deepspeed \
+    --deepspeed_config ds_config.json \
+    --seq-length $SEQ_LEN \
+    --adaptive_seq_len \
+    --eval_fp32 \
+    --task_list arc_challenge,arc_easy,boolq,copa,hellaswag,lambada,logiqa,mathqa,mc_taco,mrpc,multirc,openbookqa,piqa,prost,pubmedqa,qnli,qqp,race,rte,sst,webqs,wic,winogrande,wnli,wsc,triviaqa,sciq \
+    $MEGATRON_REQUIRED_ARGS \
+    "
+
+N_GPUS=1
+LAUNCHER="deepspeed --num_gpus $N_GPUS"
+echo $LAUNCHER $CMD
+
+export PYTHONPATH=$MEGATRON_DEEPSPEED_REPO
+
+$LAUNCHER $CMD 2>&1 | tee $VARIANT-eval-harness.log

evaluation/results/tr11/scripts/run_evalharness_tr11-176b-ml.slurm

@@ -0,0 +1,121 @@
+#!/bin/bash
+#SBATCH --job-name=run_evalharness-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
+
+
+set -x -e
+
+source $six_ALL_CCFRWORK/start-py38-pt111
+
+echo "START TIME: $(date)"
+
+# a unique identifier for the current eval ideally correspnding to the modelname
+VARIANT="tr11-176b-ml"
+
+
+CHECKPOINT_PATH=$six_ALL_CCFRSCRATCH/checkpoints/tr11-176B-ml/checkpoints/main/global_step50000
+MEGATRON_DEEPSPEED_REPO=/gpfsssd/worksf/projects/rech/six/commun/code/eval/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.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 arc_challenge,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 \
+    --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_evalharness_tr11b-1b3-ml.slurm

@@ -0,0 +1,120 @@
+#!/bin/bash
+#SBATCH --job-name=run_evalharness-tr11b-2b5-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-py38-pt111
+
+echo "START TIME: $(date)"
+
+# a unique identifier for the current eval ideally correspnding to the modelname
+VARIANT="tr11b-1b3-ml-evalharness"
+
+
+CHECKPOINT_PATH=$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=$six_ALL_CCFRWORK/datasets
+export HF_MODULES_CACHE=$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=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.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 \
+    --eval_fp32 \
+    --inference \
+    --seq-length $SEQ_LEN \
+    --task_list arc_challenge,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 \
+    --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_evalharness_tr11c-2b5-ml.slurm

@@ -0,0 +1,120 @@
+#!/bin/bash
+#SBATCH --job-name=run_evalharness-tr11b-2b5-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-py38-pt111
+
+echo "START TIME: $(date)"
+
+# a unique identifier for the current eval ideally correspnding to the modelname
+VARIANT="tr11b-2b5-ml-evalharness"
+
+
+CHECKPOINT_PATH=$six_ALL_CCFRSCRATCH/checkpoints/tr11c-2B5-ml/checkpoints/main/global_step337250
+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=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.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 \
+    --eval_fp32 \
+    --inference \
+    --seq-length $SEQ_LEN \
+    --task_list arc_challenge,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 \
+    --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_evalharness_tr11d-760m-ml.slurm

@@ -0,0 +1,118 @@
+#!/bin/bash
+#SBATCH --job-name=run_evalharness-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-py38-pt111
+
+echo "START TIME: $(date)"
+
+# a unique identifier for the current eval ideally correspnding to the modelname
+VARIANT="tr11d-760m-ml-evalharness"
+
+#/gpfsscratch/rech/six/commun/experiments/muennighoff/megdsbslmeval/Megatron-DeepSpeed
+CHECKPOINT_PATH=/gpfsscratch/rech/six/commun/checkpoints/tr11d-760M-ml/checkpoints/main/global_step660750
+MEGATRON_DEEPSPEED_REPO=$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=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.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 \
+    --eval_fp32 \
+    --inference \
+    --seq-length $SEQ_LEN \
+    --task_list arc_challenge,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 \
+    --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_evalharness_tr11e-350m-ml.slurm

@@ -0,0 +1,118 @@
+#!/bin/bash
+#SBATCH --job-name=run_evalharness-tr11e-350m-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-py38-pt111
+
+echo "START TIME: $(date)"
+
+# a unique identifier for the current eval ideally correspnding to the modelname
+VARIANT="tr11e-350m-ml-evalharness"
+
+
+CHECKPOINT_PATH=$six_ALL_CCFRSCRATCH/checkpoints/tr11e-350M-ml/checkpoints/main/global_step659500
+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=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.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 \
+    --eval_fp32 \
+    --inference \
+    --seq-length $SEQ_LEN \
+    --task_list arc_challenge,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 \
+    --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_evalharness_tr11f-6b3-ml.slurm

@@ -0,0 +1,120 @@
+#!/bin/bash
+#SBATCH --job-name=run_evalharness-tr11f-6b3-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-py38-pt111
+
+echo "START TIME: $(date)"
+
+# a unique identifier for the current eval ideally correspnding to the modelname
+VARIANT="tr11f-6b3-ml-evalharness"
+
+
+CHECKPOINT_PATH=$six_ALL_CCFRSCRATCH/checkpoints/tr11f-6B3-ml/checkpoints/main/global_step337500
+MEGATRON_DEEPSPEED_REPO=/gpfsssd/worksf/projects/rech/six/commun/code/eval/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=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.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 \
+    --eval_fp32 \
+    --inference \
+    --seq-length $SEQ_LEN \
+    --task_list arc_challenge,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 \
+    --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=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_trevalharness_7b1.slurm

@@ -0,0 +1,60 @@
+#!/bin/bash
+#SBATCH --job-name=run_trevalharness-tr11f-6b3-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-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=$six_ALL_CCFRWORK/metrics
+export HF_DATASETS_OFFLINE=1
+export TRANSFORMERS_OFFLINE=1
+export TOKENIZERS_PARALLELISM=false
+
+# Converted transformer checkpoint
+#MODEL_CKPT=/gpfsscratch/rech/six/commun/uan68tv-model-conversion/bloom
+MODEL_CKPT=/gpfsscratch/rech/six/commun/experiments/muennighoff/bloomckpt/6b3/bloom-7b1
+
+cd /gpfsscratch/rech/six/commun/commun/experiments/muennighoff/bslmevaltransformers/lm-evaluation-harness
+
+
+DATASETS_AND_CONFIGS=(
+arc_challenge
+arc_easy
+)
+#,arc_easy,boolq,copa,headqa,hellaswag,lambada,logiqa,mathqa,mc_taco,mrpc,multirc,openbookqa,piqa,prost,pubmedqa,qnli,qqp,race,rte,sciq,sst,triviaqa,webqs,wic,winogrande,wnli,wsc
+
+DATASET_AND_CONFIG=${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 \
+    --batch_size 16 \
+    --tasks $dataset_name \
+    --output_path "${MODEL_CKPT}_{$dataset_name}.json" \
+    --skip_tokenizer \
+    --no_cache \
+    --dtype=float16
+
+echo "END TIME: $(date)"

evaluation/results/tr13/download_bslmeval.slurm

@@ -0,0 +1,37 @@
+#!/bin/bash
+#SBATCH --job-name=download-bslmeval
+#SBATCH --partition=prepost
+#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 20:00:00              # maximum execution time (HH:MM:SS)
+#SBATCH --output=%x-%j.out           # output file name
+#SBATCH --account=six@cpu
+
+set -x -e
+
+echo "START TIME: $(date)"
+
+source $six_ALL_CCFRWORK/start-py38-pt111
+conda activate muennighofflmeval
+
+#export HF_DATASETS_OFFLINE=1
+#export TRANSFORMERS_OFFLINE=1
+
+export TRANSFORMERS_CACHE=$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 /gpfsscratch/rech/six/commun/experiments/muennighoff/bloomckpt/lm-evaluation-harness
+
+#  GEM/web_nlg_en,GEM/web_nlg_en_challenge_test_numbers,GEM/web_nlg_en_challenge_test_scramble,GEM/web_nlg_en_challenge_validation_sample,GEM/web_nlg_ru,GEM/web_nlg_ru_challenge_test_scramble,GEM/web_nlg_ru_challenge_validation_sample,GEM/wiki_auto_asset_turk_challenge_test_asset_backtranslation,GEM/wiki_auto_asset_turk_challenge_test_asset_bfp02,GEM/wiki_auto_asset_turk_challenge_test_asset_bfp05,GEM/wiki_auto_asset_turk_challenge_test_asset_nopunc,GEM/wiki_auto_asset_turk_challenge_test_turk_backtranslation,GEM/wiki_auto_asset_turk_challenge_test_turk_bfp02,GEM/wiki_auto_asset_turk_challenge_test_turk_bfp05,GEM/wiki_auto_asset_turk_challenge_test_turk_nopunc,GEM/wiki_auto_asset_turk_test_asset,GEM/wiki_auto_asset_turk_test_turk,GEM/wiki_lingua_ar,GEM/wiki_lingua_cs,GEM/wiki_lingua_de,GEM/wiki_lingua_en,GEM/wiki_lingua_es,GEM/wiki_lingua_fr,GEM/wiki_lingua_hi,GEM/wiki_lingua_id,GEM/wiki_lingua_it,GEM/wiki_lingua_ja,GEM/wiki_lingua_ko,GEM/wiki_lingua_nl,GEM/wiki_lingua_pt,GEM/wiki_lingua_ru,GEM/wiki_lingua_th,GEM/wiki_lingua_tr,GEM/wiki_lingua_vi,GEM/wiki_lingua_zh,gem_xsum,gem_xsum_challenge_sample,gem_xsum_challenge_test_backtranslation,gem_xsum_challenge_test_bfp_02,gem_xsum_challenge_test_bfp_05,gem_xsum_challenge_test_covid,gem_xsum_challenge_test_nopunc \
+python3 main.py --model hf-causal \
+    --model_args pretrained=hf-internal-testing/tiny-random-gpt2,use_accelerate=True,tokenizer=hf-internal-testing/tiny-random-gpt2,dtype=float16 \
+    --tasks wmt14_fr_en,wmt19_ru_en,wmt19_zh_en \
+    --device cuda \
+    --limit 1 \
+    --no_cache \
+    --num_fewshot 0

evaluation/results/tr13/lmeval/megdsbslmeval.slurm

@@ -0,0 +1,139 @@
+#!/bin/bash
+#SBATCH --job-name=tr13-base-eval
+#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: $(date)"
+
+# a unique identifier for the current eval ideally correspnding to the modelname
+VARIANT="tr13-base"
+
+CHECKPOINT_PATH=$six_ALL_CCFRSCRATCH/checkpoints/tr11f-6B3-ml/checkpoints/main/global_step163750
+#CHECKPOINT_PATH=/gpfsscratch/rech/six/commun/checkpoints/tr13f-6B3-ml-t0/checkpoints/loss/global_step3100
+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
+
+
+# Only in evalharness：hellaswag ; winogrande
+TASKS=(
+anli_r1
+anli_r2
+anli_r3
+cb
+rte
+wsc.fixed
+wic
+copa
+xcopa_id
+xcopa_sw
+xcopa_ta
+xcopa_vi
+xcopa_zh
+)
+
+
+CMD="./tasks/eval_harness/evaluate_bsevalharness_prefix.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 \
+    --eval_fp32 \
+    --inference \
+    --seq-length $SEQ_LEN \
+    --task_list ${TASKS[$SLURM_ARRAY_TASK_ID]} \
+    --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 + $SLURM_ARRAY_TASK_ID))
+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/tr13/lmeval/run_generation.slurm

@@ -0,0 +1,90 @@
+#!/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
+#SBATCH --array=0-9
+
+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=$six_ALL_CCFRWORK/metrics
+export HF_DATASETS_OFFLINE=1
+export TRANSFORMERS_OFFLINE=1
+export TOKENIZERS_PARALLELISM=false
+
+# Converted transformer checkpoint
+MODEL_CKPT=/gpfsscratch/rech/six/commun/experiments/muennighoff/bloomckpt/6b3t0/tr13f-6b3-ml-t0-lmtoks341b-t0toks13b-xp3capmixlossseq
+MODEL_CKPT=/gpfsscratch/rech/six/commun/experiments/muennighoff/bloomckpt/6b3t0/tr13f-6b3-ml-t0-lmtoks341b-t0toks13b-xp3capmix
+MODEL_CKPT=/gpfsscratch/rech/six/commun/experiments/muennighoff/bloomckpt/6b3/bloom-6b3
+MODEL_CKPT=/gpfsscratch/rech/six/commun/experiments/muennighoff/bloomckpt/6b3t0/tr13f-6b3-ml-t0-lmtoks341b-t0toks13b-p31
+
+cd /gpfsscratch/rech/six/commun/experiments/muennighoff/bslmevalgeneration/lm-evaluation-harness
+
+# WMT19 ZH-EN does not work
+DATASETS_AND_CONFIGS=(
+wmt19_zh_en,zh-en,"version-en-zh-target"
+wmt19_zh_en,zh-en,"a_good_translation-en-zh-target"
+wmt19_zh_en,zh-en,"a_good_translation-en-zh-source+target"
+wmt19_zh_en,zh-en,"xglm-en-zh-target"
+wmt19_zh_en,zh-en,"gpt3-en-zh"
+wmt19_zh_en,zh-en,"version-zh-en-target"
+wmt19_zh_en,zh-en,"a_good_translation-zh-en-target"
+wmt19_zh_en,zh-en,"a_good_translation-zh-en-source+target"
+wmt19_zh_en,zh-en,"xglm-zh-en-target"
+wmt19_zh_en,zh-en,"gpt3-zh-en"
+)
+
+DATASETS_AND_CONFIGS=(
+wmt14_fr_en,fr-en,"version-en-fr-target"
+wmt14_fr_en,fr-en,"a_good_translation-en-fr-target"
+wmt14_fr_en,fr-en,"a_good_translation-en-fr-source+target"
+wmt14_fr_en,fr-en,"xglm-en-fr-target"
+wmt14_fr_en,fr-en,"gpt3-en-fr"
+wmt14_fr_en,fr-en,"version-fr-en-target"
+wmt14_fr_en,fr-en,"a_good_translation-fr-en-target"
+wmt14_fr_en,fr-en,"a_good_translation-fr-en-source+target"
+wmt14_fr_en,fr-en,"xglm-fr-en-target"
+wmt14_fr_en,fr-en,"gpt3-fr-en"
+)
+
+# Use --limit 3000
+DATASETS_AND_CONFIGS=(
+mlsum_es,"es","layman_summ_es"
+mlsum_es,"es","palm_prompt"
+mlsum_es,"es","summarise_this_in_es_few_sentences"
+)
+
+DATASET_AND_CONFIG=${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 \
+    --limit 3000
+
+echo "END TIME: $(date)"

evaluation/results/tr13/lmeval/run_generation_7b1.slurm

@@ -0,0 +1,86 @@
+#!/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
+#SBATCH --array=0-2
+
+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=$six_ALL_CCFRWORK/metrics
+export HF_DATASETS_OFFLINE=1
+export TRANSFORMERS_OFFLINE=1
+export TOKENIZERS_PARALLELISM=false
+
+# Converted transformer checkpoint
+MODEL_CKPT=/gpfsscratch/rech/six/commun/experiments/muennighoff/bloomckpt/6b3t0/tr13f-6b3-ml-t0-lmtoks341b-t0toks13b-xp3capmixnewcodelonglossseq
+
+cd /gpfsscratch/rech/six/commun/experiments/muennighoff/lm-evaluation-harness
+
+
+DATASETS_AND_CONFIGS=(
+wmt14_fr_en,fr-en,"version-en-fr-target"
+wmt14_fr_en,fr-en,"a_good_translation-en-fr-target"
+wmt14_fr_en,fr-en,"a_good_translation-en-fr-source+target"
+wmt14_fr_en,fr-en,"xglm-en-fr-target"
+wmt14_fr_en,fr-en,"gpt3-en-fr"
+wmt14_fr_en,fr-en,"version-fr-en-target"
+wmt14_fr_en,fr-en,"a_good_translation-fr-en-target"
+wmt14_fr_en,fr-en,"a_good_translation-fr-en-source+target"
+wmt14_fr_en,fr-en,"xglm-fr-en-target"
+wmt14_fr_en,fr-en,"gpt3-fr-en"
+)
+
+DATASETS_AND_CONFIGS=(
+wmt14_hi_en,hi-en,"version-en-hi-target"
+wmt14_hi_en,hi-en,"a_good_translation-en-hi-target"
+wmt14_hi_en,hi-en,"a_good_translation-en-hi-source+target"
+wmt14_hi_en,hi-en,"xglm-en-hi-target"
+wmt14_hi_en,hi-en,"gpt3-en-hi-target"
+wmt14_hi_en,hi-en,"version-hi-en-target"
+wmt14_hi_en,hi-en,"a_good_translation-hi-en-target"
+wmt14_hi_en,hi-en,"a_good_translation-hi-en-source+target"
+wmt14_hi_en,hi-en,"xglm-hi-en-target"
+wmt14_hi_en,hi-en,"gpt3-hi-en-target"
+)
+
+DATASETS_AND_CONFIGS=(
+mlsum_es,"es","layman_summ_es"
+mlsum_es,"es","palm_prompt"
+mlsum_es,"es","summarise_this_in_es_few_sentences"
+)
+
+DATASET_AND_CONFIG=${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 \
+    --limit 3000
+
+echo "END TIME: $(date)"

evaluation/results/tr13/lmeval/transformersbslmeval.slurm

@@ -0,0 +1,53 @@
+#!/bin/bash
+#SBATCH --job-name=bseval-tr13f-6B3
+#SBATCH --partition=gpu_p5
+#SBATCH --constraint=a100
+#SBATCH --reservation=hug
+#SBATCH --qos=qos_gpu-gc             # up to 100h
+#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 100:00:00             # 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 muennighofflmeval
+
+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=$six_ALL_CCFRWORK/metrics
+export HF_DATASETS_OFFLINE=1
+export TRANSFORMERS_OFFLINE=1
+
+# Converted transformer checkpoint
+#MODEL_CKPT=/gpfsscratch/rech/six/commun/experiments/muennighoff/bloomckpt/6b3t0/tr13f-6B3-ml-t0-lmtoks168B-t0toks8b5
+#MODEL_CKPT=/gpfsscratch/rech/six/commun/experiments/muennighoff/bloomckpt/6b3t0/tr13f-6B3-ml-t0-lmtoks168B-t0toks0
+MODEL_CKPT=/gpfsscratch/rech/six/commun/experiments/muennighoff/bloomckpt/6b3t0/tr13f-6b3-ml-t0-lmtoks168b-t0toks13b
+
+cd /gpfsscratch/rech/six/commun/experiments/muennighoff/bloomckpt/lm-evaluation-harness
+
+# GEM/wiki_lingua_es has 5 prompts
+NUM_TASKS=5
+
+
+# Use this fork of lm-eval: https://github.com/bigscience-workshop/lm-evaluation-harness/pull/109
+python3 main.py --model hf-causal \
+    --model_args pretrained=$MODEL_CKPT,use_accelerate=True,tokenizer=$MODEL_CKPT,dtype=float16 \
+    --tasks GEM/wiki_lingua_es \
+    --device cuda \
+    --batch_size 16 \
+    --no_cache \
+    --no_tracking \
+    --prompts $SLURM_ARRAY_TASK_ID \
+    --num_fewshot 0
+
+echo "END TIME: $(date)"

evaluation/results/tr13/tzeroeval/convert_validation_176b.slurm

@@ -0,0 +1,373 @@
+#!/bin/bash
+#SBATCH --job-name=ckpts
+#SBATCH --ntasks=1                   # number of MP tasks
+#SBATCH --nodes=1
+#SBATCH --cpus-per-task=40           # number of cores per tasks
+#SBATCH --hint=nomultithread         # we get physical cores not logical
+#SBATCH --time=20:00:00             # maximum execution time (HH:MM:SS)
+#SBATCH --output=%x-%j.out          # output file name
+#SBATCH --account=six@cpu
+#SBATCH --partition=cpu_p1
+
+set -x -e
+
+source $six_ALL_CCFRWORK/start-tr13f-6B3-ml-t0
+export HF_DATASETS_OFFLINE=1
+export TRANSFORMERS_OFFLINE=1
+conda activate muennighoffmodelconv
+
+#CKPT_PATH=$six_ALL_CCFRSCRATCH/checkpoints/tr13-176B-ml-t0/checkpoints/xp3zzlossseq
+CKPT_PATH=$six_ALL_CCFRSCRATCH/checkpoints/tr13-176B-ml-t0/checkpoints/p31lossseq
+#CKPT_PATH=$six_ALL_CCFRSCRATCH/checkpoints/tr13-176B-ml-t0/checkpoints/xp3capmixnewcodelonglossseq
+
+CKPTS=(
+global_step249
+global_step498
+global_step747
+global_step996
+global_step1245
+global_step1494
+global_step1743
+global_step1992
+global_step2241
+)
+EXAMPLE_CKPT=$six_ALL_CCFRSCRATCH/commun/experiments/muennighoff/bloomckpt/176bt0/tr13-176b-ml-t0-lmtoks341b-t0toks13b-xp3capmixnewcodelonglossseq
+DUMP_PATH=$six_ALL_CCFRSCRATCH/commun/experiments/muennighoff/bloomckpt/176bt0
+
+#OUT_PREFIX=xp3zzlossseq_
+OUT_PREFIX=p31lossseq_
+#OUT_PREFIX=xp3capmixnewcodelonglossseq_
+
+TP=1
+
+### CONVERT ###
+
+
+for i in {0..8}; do
+CKPT=${CKPTS[$i]}
+echo "$i"
+echo "Running $CKPT"
+
+OUTPUTCKPT=$DUMP_PATH/"$OUT_PREFIX$CKPT"
+mkdir -p $OUTPUTCKPT
+python $six_ALL_CCFRSCRATCH/commun/experiments/muennighoff/bloomckpt/transformers_clone/src/transformers/models/bloom/convert_bloom_original_checkpoint_to_pytorch.py --pytorch_dump_folder_path $OUTPUTCKPT --bloom_checkpoint_path $CKPT_PATH/$CKPT --pretraining_tp $TP --bloom_config_file $EXAMPLE_CKPT/config.json --shard_model
+
+# Copy tokenizer.json etc
+cp -r $EXAMPLE_CKPT/*.json $OUTPUTCKPT/
+
+# Use model prior to finetuning
+#OUTPUTCKPT=/gpfsscratch/rech/six/commun/uan68tv-model-conversion/bloom
+
+eval_script="./eval_$i.slurm"
+cat <<EOT > $eval_script
+#!/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=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
+#SBATCH --array=0-155
+
+set -x -e
+
+source $six_ALL_CCFRWORK/start-py38-pt111
+conda activate thomas_t_zero_evaluation
+
+CHECKPOINT_PATH=$OUTPUTCKPT
+
+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_VAL=(
+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
+)
+
+
+DATASET_AND_CONFIG="\${DATASETS_AND_CONFIGS_VAL[\$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 split <<< "\${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" \
+        --split "\$split" \
+        --model_name_or_path "\$CHECKPOINT_PATH" \
+        --output_dir "\$OUTPUT_DIR" \
+        --per_device_eval_batch_size 4 \
+        --max_length 2048 \
+        --dtype bfloat16
+EOT
+
+sbatch $eval_script
+
+
+lm_eval_script="./lm_eval_$i.slurm"
+cat <<EOT > $lm_eval_script
+#!/bin/bash
+#SBATCH --job-name=lmeval
+#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
+#SBATCH --array=0-12
+
+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=$six_ALL_CCFRWORK/metrics
+export HF_DATASETS_OFFLINE=1
+export TRANSFORMERS_OFFLINE=1
+export TOKENIZERS_PARALLELISM=false
+
+# Converted transformer checkpoint
+MODEL_CKPT=$OUTPUTCKPT
+
+cd /gpfsscratch/rech/six/commun/experiments/muennighoff/lm-evaluation-harness
+
+
+DATASETS_AND_CONFIGS=(
+wmt14_fr_en,fr-en,"version-en-fr-target"
+wmt14_fr_en,fr-en,"a_good_translation-en-fr-target"
+wmt14_fr_en,fr-en,"a_good_translation-en-fr-source+target"
+wmt14_fr_en,fr-en,"xglm-en-fr-target"
+wmt14_fr_en,fr-en,"gpt3-en-fr"
+wmt14_fr_en,fr-en,"version-fr-en-target"
+wmt14_fr_en,fr-en,"a_good_translation-fr-en-target"
+wmt14_fr_en,fr-en,"a_good_translation-fr-en-source+target"
+wmt14_fr_en,fr-en,"xglm-fr-en-target"
+wmt14_fr_en,fr-en,"gpt3-fr-en"
+wmt14_hi_en,hi-en,"version-en-hi-target"
+wmt14_hi_en,hi-en,"a_good_translation-en-hi-target"
+wmt14_hi_en,hi-en,"a_good_translation-en-hi-source+target"
+wmt14_hi_en,hi-en,"xglm-en-hi-target"
+wmt14_hi_en,hi-en,"gpt-3-en-hi-target"
+wmt14_hi_en,hi-en,"version-hi-en-target"
+wmt14_hi_en,hi-en,"a_good_translation-hi-en-target"
+wmt14_hi_en,hi-en,"a_good_translation-hi-en-source+target"
+wmt14_hi_en,hi-en,"xglm-hi-en-target"
+wmt14_hi_en,hi-en,"gpt-3-hi-en-target"
+mlsum_es,"es","layman_summ_es"
+mlsum_es,"es","palm_prompt"
+mlsum_es,"es","summarise_this_in_es_few_sentences"
+)
+
+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=bfloat16" \
+    --device cuda \
+    --batch_size 4 \
+    --no_tracking \
+    --task_name "\$dataset_name" \
+    --template_names "\$template_name" \
+    --bootstrap_iters 10 \
+    --limit 3000
+
+mkdir -p "$OUTPUTCKPT/evaluation/\$dataset_name"
+mv /gpfsscratch/rech/six/commun/experiments/muennighoff/lm-evaluation-harness/outputs/*$CKPT*\$dataset_name* "$OUTPUTCKPT/evaluation/\$dataset_name/"
+
+echo "END TIME: $(date)"
+EOT
+
+sbatch $lm_eval_script
+
+done

evaluation/results/tr13/tzeroeval/convert_validation_1b3.slurm

@@ -0,0 +1,352 @@
+#!/bin/bash
+#SBATCH --job-name=ckpts
+#SBATCH --ntasks=1                   # number of MP tasks
+#SBATCH --nodes=1
+#SBATCH --cpus-per-task=40           # number of cores per tasks
+#SBATCH --hint=nomultithread         # we get physical cores not logical
+#SBATCH --time=20:00:00             # maximum execution time (HH:MM:SS)
+#SBATCH --output=%x-%j.out          # output file name
+#SBATCH --account=ajs@cpu
+#SBATCH --partition=cpu_p1
+
+set -x -e
+
+source $six_ALL_CCFRWORK/start-tr13f-6B3-ml-t0
+export HF_DATASETS_OFFLINE=1
+export TRANSFORMERS_OFFLINE=1
+conda activate muennighoffmodelconv
+
+CKPT_PATH=$six_ALL_CCFRSCRATCH/checkpoints/tr13b-1B3-ml-t0/checkpoints/xp3capmixnewcodelonglossseq
+#CKPT_PATH=$six_ALL_CCFRSCRATCH/checkpoints/tr13f-6B3-ml-t0/checkpoints/p31lossseq
+
+CKPTS=(
+global_step250
+global_step500
+global_step750
+global_step1000
+global_step1250
+global_step1500
+global_step1750
+global_step2000
+global_step2250
+global_step2500
+global_step2750
+global_step3000
+)
+EXAMPLE_CKPT=$six_ALL_CCFRSCRATCH/commun/experiments/muennighoff/bloomckpt/1b3/bloom-1b7
+DUMP_PATH=$six_ALL_CCFRSCRATCH/commun/experiments/muennighoff/bloomckpt/1b3t0
+OUT_PREFIX=xp3capmixlossseq_
+#OUT_PREFIX=p31lossseq
+
+TP=1
+
+### CONVERT ###
+
+
+for i in {0..11}; do
+CKPT=${CKPTS[$i]}
+echo "$i"
+echo "Running $CKPT"
+
+OUTPUTCKPT=$DUMP_PATH/"$OUT_PREFIX$CKPT"
+python $six_ALL_CCFRSCRATCH/commun/experiments/muennighoff/bloomckpt/transformers_clone/src/transformers/models/bloom/convert_bloom_original_checkpoint_to_pytorch.py --pytorch_dump_folder_path $OUTPUTCKPT --bloom_checkpoint_path $CKPT_PATH/$CKPT --pretraining_tp $TP --bloom_config_file $EXAMPLE_CKPT/config.json
+
+# Copy tokenizer.json etc
+cp -r $EXAMPLE_CKPT/*.json $OUTPUTCKPT/
+
+eval_script="./eval_$i.slurm"
+cat <<EOT > $eval_script
+#!/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 --time 5:00:00             # maximum execution time (HH:MM:SS)
+#SBATCH --output=%x-%j.out           # output file name
+#SBATCH --account=six@a100
+#SBATCH --array=0-168
+
+set -x -e
+
+source $six_ALL_CCFRWORK/start-py38-pt111
+conda activate muennighofflmevalgen
+
+CHECKPOINT_PATH=$OUTPUTCKPT
+
+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
+)
+
+DATASET_AND_CONFIG="\${DATASETS_AND_CONFIGS_VAL[\$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 split <<< "\${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" \
+        --split "\$split" \
+        --model_name_or_path "\$CHECKPOINT_PATH" \
+        --output_dir "\$OUTPUT_DIR" \
+        --per_device_eval_batch_size 4 \
+        --max_length 2048 \
+        --dtype float16
+EOT
+
+sbatch $eval_script
+
+
+lm_eval_script="./lm_eval_$i.slurm"
+cat <<EOT > $lm_eval_script
+#!/bin/bash
+#SBATCH --job-name=lmeval
+#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 --time 20:00:00             # maximum execution time (HH:MM:SS)
+#SBATCH --output=%x-%j.out           # output file name
+#SBATCH --account=six@a100
+#SBATCH --array=0-22
+
+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=$six_ALL_CCFRWORK/metrics
+export HF_DATASETS_OFFLINE=1
+export TRANSFORMERS_OFFLINE=1
+export TOKENIZERS_PARALLELISM=false
+
+# Converted transformer checkpoint
+MODEL_CKPT=$OUTPUTCKPT
+
+cd /gpfsscratch/rech/six/commun/experiments/muennighoff/lm-evaluation-harness
+
+
+DATASETS_AND_CONFIGS=(
+wmt14_fr_en,fr-en,"version-en-fr-target"
+wmt14_fr_en,fr-en,"a_good_translation-en-fr-target"
+wmt14_fr_en,fr-en,"a_good_translation-en-fr-source+target"
+wmt14_fr_en,fr-en,"xglm-en-fr-target"
+wmt14_fr_en,fr-en,"gpt3-en-fr"
+wmt14_fr_en,fr-en,"version-fr-en-target"
+wmt14_fr_en,fr-en,"a_good_translation-fr-en-target"
+wmt14_fr_en,fr-en,"a_good_translation-fr-en-source+target"
+wmt14_fr_en,fr-en,"xglm-fr-en-target"
+wmt14_fr_en,fr-en,"gpt3-fr-en"
+wmt14_hi_en,hi-en,"version-en-hi-target"
+wmt14_hi_en,hi-en,"a_good_translation-en-hi-target"
+wmt14_hi_en,hi-en,"a_good_translation-en-hi-source+target"
+wmt14_hi_en,hi-en,"xglm-en-hi-target"
+wmt14_hi_en,hi-en,"gpt-3-en-hi-target"
+wmt14_hi_en,hi-en,"version-hi-en-target"
+wmt14_hi_en,hi-en,"a_good_translation-hi-en-target"
+wmt14_hi_en,hi-en,"a_good_translation-hi-en-source+target"
+wmt14_hi_en,hi-en,"xglm-hi-en-target"
+wmt14_hi_en,hi-en,"gpt-3-hi-en-target"
+mlsum_es,"es","layman_summ_es"
+mlsum_es,"es","palm_prompt"
+mlsum_es,"es","summarise_this_in_es_few_sentences"
+)
+
+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 \
+    --limit 3000
+
+mkdir -p "$OUTPUTCKPT/evaluation/\$dataset_name"
+mv "outputs/*$CKPT*\$dataset_name*" "$OUTPUTCKPT/evaluation/\$dataset_name/"
+
+echo "END TIME: $(date)"
+EOT
+
+sbatch $lm_eval_script
+
+
+done

evaluation/results/tr13/tzeroeval/convert_validation_350m.slurm

@@ -0,0 +1,350 @@
+#!/bin/bash
+#SBATCH --job-name=ckpts
+#SBATCH --ntasks=1                   # number of MP tasks
+#SBATCH --nodes=1
+#SBATCH --cpus-per-task=40           # number of cores per tasks
+#SBATCH --hint=nomultithread         # we get physical cores not logical
+#SBATCH --time=20:00:00             # maximum execution time (HH:MM:SS)
+#SBATCH --output=%x-%j.out          # output file name
+#SBATCH --account=ajs@cpu
+#SBATCH --partition=cpu_p1
+
+set -x -e
+
+source $six_ALL_CCFRWORK/start-tr13f-6B3-ml-t0
+export HF_DATASETS_OFFLINE=1
+export TRANSFORMERS_OFFLINE=1
+conda activate muennighoffmodelconv
+
+CKPT_PATH=$six_ALL_CCFRSCRATCH/checkpoints/tr13e-350M-ml-t0/checkpoints/xp3capmixnewcodelonglossseq
+
+CKPTS=(
+global_step250
+global_step500
+global_step750
+global_step1000
+global_step1250
+global_step1500
+global_step1750
+global_step2000
+global_step2250
+global_step2500
+global_step2750
+global_step3000
+)
+EXAMPLE_CKPT=/gpfsssd/scratch/rech/six/commun/experiments/muennighoff/bloomckpt/350m/bloom-560m
+DUMP_PATH=$six_ALL_CCFRSCRATCH/commun/experiments/muennighoff/bloomckpt/350mt0
+OUT_PREFIX=xp3capmixnewcodelonglossseq
+
+TP=1
+
+### CONVERT ###
+
+
+for i in {0..12}; do
+CKPT=${CKPTS[$i]}
+echo "$i"
+echo "Running $CKPT"
+
+OUTPUTCKPT=$DUMP_PATH/"$OUT_PREFIX$CKPT"
+python $six_ALL_CCFRSCRATCH/commun/experiments/muennighoff/bloomckpt/transformers_clone/src/transformers/models/bloom/convert_bloom_original_checkpoint_to_pytorch.py --pytorch_dump_folder_path $OUTPUTCKPT --bloom_checkpoint_path $CKPT_PATH/$CKPT --pretraining_tp $TP --bloom_config_file $EXAMPLE_CKPT/config.json
+
+# Copy tokenizer.json etc
+cp -r $EXAMPLE_CKPT/*.json $OUTPUTCKPT/
+
+eval_script="./eval_$i.slurm"
+cat <<EOT > $eval_script
+#!/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 --time 5:00:00             # maximum execution time (HH:MM:SS)
+#SBATCH --output=%x-%j.out           # output file name
+#SBATCH --account=six@a100
+#SBATCH --array=0-168
+
+set -x -e
+
+source $six_ALL_CCFRWORK/start-py38-pt111
+conda activate muennighofflmevalgen
+
+CHECKPOINT_PATH=$OUTPUTCKPT
+
+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
+)
+
+DATASET_AND_CONFIG="\${DATASETS_AND_CONFIGS_VAL[\$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 split <<< "\${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" \
+        --split "\$split" \
+        --model_name_or_path "\$CHECKPOINT_PATH" \
+        --output_dir "\$OUTPUT_DIR" \
+        --per_device_eval_batch_size 4 \
+        --max_length 2048 \
+        --dtype float16
+EOT
+
+sbatch $eval_script
+
+
+lm_eval_script="./lm_eval_$i.slurm"
+cat <<EOT > $lm_eval_script
+#!/bin/bash
+#SBATCH --job-name=lmeval
+#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 --time 20:00:00             # maximum execution time (HH:MM:SS)
+#SBATCH --output=%x-%j.out           # output file name
+#SBATCH --account=six@a100
+#SBATCH --array=0-22
+
+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=$six_ALL_CCFRWORK/metrics
+export HF_DATASETS_OFFLINE=1
+export TRANSFORMERS_OFFLINE=1
+export TOKENIZERS_PARALLELISM=false
+
+# Converted transformer checkpoint
+MODEL_CKPT=$OUTPUTCKPT
+
+cd /gpfsscratch/rech/six/commun/experiments/muennighoff/lm-evaluation-harness
+
+
+DATASETS_AND_CONFIGS=(
+wmt14_fr_en,fr-en,"version-en-fr-target"
+wmt14_fr_en,fr-en,"a_good_translation-en-fr-target"
+wmt14_fr_en,fr-en,"a_good_translation-en-fr-source+target"
+wmt14_fr_en,fr-en,"xglm-en-fr-target"
+wmt14_fr_en,fr-en,"gpt3-en-fr"
+wmt14_fr_en,fr-en,"version-fr-en-target"
+wmt14_fr_en,fr-en,"a_good_translation-fr-en-target"
+wmt14_fr_en,fr-en,"a_good_translation-fr-en-source+target"
+wmt14_fr_en,fr-en,"xglm-fr-en-target"
+wmt14_fr_en,fr-en,"gpt3-fr-en"
+wmt14_hi_en,hi-en,"version-en-hi-target"
+wmt14_hi_en,hi-en,"a_good_translation-en-hi-target"
+wmt14_hi_en,hi-en,"a_good_translation-en-hi-source+target"
+wmt14_hi_en,hi-en,"xglm-en-hi-target"
+wmt14_hi_en,hi-en,"gpt-3-en-hi-target"
+wmt14_hi_en,hi-en,"version-hi-en-target"
+wmt14_hi_en,hi-en,"a_good_translation-hi-en-target"
+wmt14_hi_en,hi-en,"a_good_translation-hi-en-source+target"
+wmt14_hi_en,hi-en,"xglm-hi-en-target"
+wmt14_hi_en,hi-en,"gpt-3-hi-en-target"
+mlsum_es,"es","layman_summ_es"
+mlsum_es,"es","palm_prompt"
+mlsum_es,"es","summarise_this_in_es_few_sentences"
+)
+
+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 \
+    --limit 3000
+
+mkdir -p "$OUTPUTCKPT/evaluation/\$dataset_name"
+mv "outputs/*$CKPT*\$dataset_name*" "$OUTPUTCKPT/evaluation/\$dataset_name/"
+
+echo "END TIME: $(date)"
+EOT
+
+sbatch $lm_eval_script
+
+
+done

evaluation/results/tr13/tzeroeval/convert_validation_760m.slurm

@@ -0,0 +1,352 @@
+#!/bin/bash
+#SBATCH --job-name=ckpts
+#SBATCH --ntasks=1                   # number of MP tasks
+#SBATCH --nodes=1
+#SBATCH --cpus-per-task=40           # number of cores per tasks
+#SBATCH --hint=nomultithread         # we get physical cores not logical
+#SBATCH --time=20:00:00             # maximum execution time (HH:MM:SS)
+#SBATCH --output=%x-%j.out          # output file name
+#SBATCH --account=ajs@cpu
+#SBATCH --partition=cpu_p1
+
+set -x -e
+
+source $six_ALL_CCFRWORK/start-tr13f-6B3-ml-t0
+export HF_DATASETS_OFFLINE=1
+export TRANSFORMERS_OFFLINE=1
+conda activate muennighoffmodelconv
+
+CKPT_PATH=$six_ALL_CCFRSCRATCH/checkpoints/tr13d-760M-ml-t0/checkpoints/xp3capmixnewcodelonglossseq
+#CKPT_PATH=$six_ALL_CCFRSCRATCH/checkpoints/tr13f-6B3-ml-t0/checkpoints/p31lossseq
+
+CKPTS=(
+global_step250
+global_step500
+global_step750
+global_step1000
+global_step1250
+global_step1500
+global_step1750
+global_step2000
+global_step2250
+global_step2500
+global_step2750
+global_step3000
+)
+EXAMPLE_CKPT=$six_ALL_CCFRSCRATCH/commun/experiments/muennighoff/bloomckpt/760m/bloom-760m
+DUMP_PATH=$six_ALL_CCFRSCRATCH/commun/experiments/muennighoff/bloomckpt/760mt0
+OUT_PREFIX=xp3capmixlossseq_
+#OUT_PREFIX=p31lossseq
+
+TP=1
+
+### CONVERT ###
+
+
+for i in {0..11}; do
+CKPT=${CKPTS[$i]}
+echo "$i"
+echo "Running $CKPT"
+
+OUTPUTCKPT=$DUMP_PATH/"$OUT_PREFIX$CKPT"
+python $six_ALL_CCFRSCRATCH/commun/experiments/muennighoff/bloomckpt/transformers_clone/src/transformers/models/bloom/convert_bloom_original_checkpoint_to_pytorch.py --pytorch_dump_folder_path $OUTPUTCKPT --bloom_checkpoint_path $CKPT_PATH/$CKPT --pretraining_tp $TP --bloom_config_file $EXAMPLE_CKPT/config.json
+
+# Copy tokenizer.json etc
+cp -r $EXAMPLE_CKPT/*.json $OUTPUTCKPT/
+
+eval_script="./eval_$i.slurm"
+cat <<EOT > $eval_script
+#!/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 --time 5:00:00             # maximum execution time (HH:MM:SS)
+#SBATCH --output=%x-%j.out           # output file name
+#SBATCH --account=six@a100
+#SBATCH --array=0-168
+
+set -x -e
+
+source $six_ALL_CCFRWORK/start-py38-pt111
+conda activate thomas_t_zero_evaluation
+
+CHECKPOINT_PATH=$OUTPUTCKPT
+
+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
+)
+
+DATASET_AND_CONFIG="\${DATASETS_AND_CONFIGS_VAL[\$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 split <<< "\${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" \
+        --split "\$split" \
+        --model_name_or_path "\$CHECKPOINT_PATH" \
+        --output_dir "\$OUTPUT_DIR" \
+        --per_device_eval_batch_size 4 \
+        --max_length 2048 \
+        --dtype float16
+EOT
+
+sbatch $eval_script
+
+
+lm_eval_script="./lm_eval_$i.slurm"
+cat <<EOT > $lm_eval_script
+#!/bin/bash
+#SBATCH --job-name=lmeval
+#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 --time 20:00:00             # maximum execution time (HH:MM:SS)
+#SBATCH --output=%x-%j.out           # output file name
+#SBATCH --account=six@a100
+#SBATCH --array=0-22
+
+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=$six_ALL_CCFRWORK/metrics
+export HF_DATASETS_OFFLINE=1
+export TRANSFORMERS_OFFLINE=1
+export TOKENIZERS_PARALLELISM=false
+
+# Converted transformer checkpoint
+MODEL_CKPT=$OUTPUTCKPT
+
+cd /gpfsscratch/rech/six/commun/experiments/muennighoff/lm-evaluation-harness
+
+
+DATASETS_AND_CONFIGS=(
+wmt14_fr_en,fr-en,"version-en-fr-target"
+wmt14_fr_en,fr-en,"a_good_translation-en-fr-target"
+wmt14_fr_en,fr-en,"a_good_translation-en-fr-source+target"
+wmt14_fr_en,fr-en,"xglm-en-fr-target"
+wmt14_fr_en,fr-en,"gpt3-en-fr"
+wmt14_fr_en,fr-en,"version-fr-en-target"
+wmt14_fr_en,fr-en,"a_good_translation-fr-en-target"
+wmt14_fr_en,fr-en,"a_good_translation-fr-en-source+target"
+wmt14_fr_en,fr-en,"xglm-fr-en-target"
+wmt14_fr_en,fr-en,"gpt3-fr-en"
+wmt14_hi_en,hi-en,"version-en-hi-target"
+wmt14_hi_en,hi-en,"a_good_translation-en-hi-target"
+wmt14_hi_en,hi-en,"a_good_translation-en-hi-source+target"
+wmt14_hi_en,hi-en,"xglm-en-hi-target"
+wmt14_hi_en,hi-en,"gpt-3-en-hi-target"
+wmt14_hi_en,hi-en,"version-hi-en-target"
+wmt14_hi_en,hi-en,"a_good_translation-hi-en-target"
+wmt14_hi_en,hi-en,"a_good_translation-hi-en-source+target"
+wmt14_hi_en,hi-en,"xglm-hi-en-target"
+wmt14_hi_en,hi-en,"gpt-3-hi-en-target"
+mlsum_es,"es","layman_summ_es"
+mlsum_es,"es","palm_prompt"
+mlsum_es,"es","summarise_this_in_es_few_sentences"
+)
+
+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 \
+    --limit 3000
+
+mkdir -p "$OUTPUTCKPT/evaluation/\$dataset_name"
+mv "outputs/*$CKPT*\$dataset_name*" "$OUTPUTCKPT/evaluation/\$dataset_name/"
+
+echo "END TIME: $(date)"
+EOT
+
+sbatch $lm_eval_script
+
+
+done