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@@ -127,3 +127,4 @@ lm-evaluation-harness/wandb/run-20240605_110038-lwgnduuo/run-lwgnduuo.wandb filt
 lm-evaluation-harness/wandb/run-20240605_140919-mkdnls2x/run-mkdnls2x.wandb filter=lfs diff=lfs merge=lfs -text
 lm-evaluation-harness/wandb/run-20240605_093020-laxetjfu/run-laxetjfu.wandb filter=lfs diff=lfs merge=lfs -text
 lm-evaluation-harness/wandb/run-20240606_045127-vb760voe/run-vb760voe.wandb filter=lfs diff=lfs merge=lfs -text
+venv/lib/python3.10/site-packages/nvidia/cublas/lib/libcublasLt.so.12 filter=lfs diff=lfs merge=lfs -text

lm-evaluation-harness/lm_eval/tasks/blimp/README.md

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+# Task-name
+
+### Paper
+
+Title: `BLiMP: A Benchmark of Linguistic Minimal Pairs for English`
+Abstract: `https://arxiv.org/abs/1912.00582`
+
+BLiMP is a challenge set for evaluating what language models (LMs) know about
+major grammatical phenomena in English. BLiMP consists of 67 sub-datasets, each
+containing 1000 minimal pairs isolating specific contrasts in syntax, morphology,
+or semantics. The data is automatically generated according to expert-crafted
+grammars.
+
+Homepage: https://github.com/alexwarstadt/blimp
+
+
+### Citation
+
+```
+@article{warstadt2019blimp,
+    author = {Warstadt, Alex and Parrish, Alicia and Liu, Haokun and Mohananey, Anhad and Peng, Wei and Wang, Sheng-Fu and Bowman, Samuel R.},
+    title = {BLiMP: The Benchmark of Linguistic Minimal Pairs for English},
+    journal = {Transactions of the Association for Computational Linguistics},
+    volume = {8},
+    number = {},
+    pages = {377-392},
+    year = {2020},
+    doi = {10.1162/tacl\_a\_00321},
+    URL = {https://doi.org/10.1162/tacl_a_00321},
+    eprint = {https://doi.org/10.1162/tacl_a_00321},
+    abstract = { We introduce The Benchmark of Linguistic Minimal Pairs (BLiMP),1 a challenge set for evaluating the linguistic knowledge of language models (LMs) on major grammatical phenomena in English. BLiMP consists of 67 individual datasets, each containing 1,000 minimal pairs—that is, pairs of minimally different sentences that contrast in grammatical acceptability and isolate specific phenomenon in syntax, morphology, or semantics. We generate the data according to linguist-crafted grammar templates, and human aggregate agreement with the labels is 96.4\%. We evaluate n-gram, LSTM, and Transformer (GPT-2 and Transformer-XL) LMs by observing whether they assign a higher probability to the acceptable sentence in each minimal pair. We find that state-of-the-art models identify morphological contrasts related to agreement reliably, but they struggle with some subtle semantic and syntactic phenomena, such as negative polarity items and extraction islands. }
+}
+```
+
+### Subtasks
+
+List or describe tasks defined in this folder, and their names here:
+* `task_name`: `1-sentence description of what this particular task does`
+* `task_name2`: .....
+
+### Checklist
+
+For adding novel benchmarks/datasets to the library:
+* [ ] Is the task an existing benchmark in the literature?
+  * [ ] Have you referenced the original paper that introduced the task?
+  * [ ] If yes, does the original paper provide a reference implementation? If so, have you checked against the reference implementation and documented how to run such a test?
+
+
+If other tasks on this dataset are already supported:
+* [ ] Is the "Main" variant of this task clearly denoted?
+* [ ] Have you provided a short sentence in a README on what each new variant adds / evaluates?
+* [ ] Have you noted which, if any, published evaluation setups are matched by this variant?

lm-evaluation-harness/lm_eval/tasks/blimp/adjunct_island.yaml

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+# Generated by utils.py
+dataset_name: adjunct_island
+include: _template_yaml
+task: blimp_adjunct_island

lm-evaluation-harness/lm_eval/tasks/blimp/anaphor_gender_agreement.yaml

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+# Generated by utils.py
+dataset_name: anaphor_gender_agreement
+include: _template_yaml
+task: blimp_anaphor_gender_agreement

lm-evaluation-harness/lm_eval/tasks/blimp/anaphor_number_agreement.yaml

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+# Generated by utils.py
+dataset_name: anaphor_number_agreement
+include: _template_yaml
+task: blimp_anaphor_number_agreement

lm-evaluation-harness/lm_eval/tasks/blimp/animate_subject_passive.yaml

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+# Generated by utils.py
+dataset_name: animate_subject_passive
+include: _template_yaml
+task: blimp_animate_subject_passive

lm-evaluation-harness/lm_eval/tasks/blimp/complex_NP_island.yaml

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+# Generated by utils.py
+dataset_name: complex_NP_island
+include: _template_yaml
+task: blimp_complex_NP_island

lm-evaluation-harness/lm_eval/tasks/blimp/coordinate_structure_constraint_object_extraction.yaml

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+# Generated by utils.py
+dataset_name: coordinate_structure_constraint_object_extraction
+include: _template_yaml
+task: blimp_coordinate_structure_constraint_object_extraction

lm-evaluation-harness/lm_eval/tasks/blimp/determiner_noun_agreement_2.yaml

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+# Generated by utils.py
+dataset_name: determiner_noun_agreement_2
+include: _template_yaml
+task: blimp_determiner_noun_agreement_2

lm-evaluation-harness/lm_eval/tasks/blimp/determiner_noun_agreement_irregular_1.yaml

@@ -0,0 +1,4 @@
+# Generated by utils.py
+dataset_name: determiner_noun_agreement_irregular_1
+include: _template_yaml
+task: blimp_determiner_noun_agreement_irregular_1

lm-evaluation-harness/lm_eval/tasks/blimp/determiner_noun_agreement_irregular_2.yaml

@@ -0,0 +1,4 @@
+# Generated by utils.py
+dataset_name: determiner_noun_agreement_irregular_2
+include: _template_yaml
+task: blimp_determiner_noun_agreement_irregular_2

lm-evaluation-harness/lm_eval/tasks/blimp/determiner_noun_agreement_with_adj_2.yaml

@@ -0,0 +1,4 @@
+# Generated by utils.py
+dataset_name: determiner_noun_agreement_with_adj_2
+include: _template_yaml
+task: blimp_determiner_noun_agreement_with_adj_2

lm-evaluation-harness/lm_eval/tasks/blimp/determiner_noun_agreement_with_adj_irregular_1.yaml

@@ -0,0 +1,4 @@
+# Generated by utils.py
+dataset_name: determiner_noun_agreement_with_adj_irregular_1
+include: _template_yaml
+task: blimp_determiner_noun_agreement_with_adj_irregular_1

lm-evaluation-harness/lm_eval/tasks/blimp/determiner_noun_agreement_with_adjective_1.yaml

@@ -0,0 +1,4 @@
+# Generated by utils.py
+dataset_name: determiner_noun_agreement_with_adjective_1
+include: _template_yaml
+task: blimp_determiner_noun_agreement_with_adjective_1

lm-evaluation-harness/lm_eval/tasks/blimp/distractor_agreement_relative_clause.yaml

@@ -0,0 +1,4 @@
+# Generated by utils.py
+dataset_name: distractor_agreement_relative_clause
+include: _template_yaml
+task: blimp_distractor_agreement_relative_clause

lm-evaluation-harness/lm_eval/tasks/blimp/ellipsis_n_bar_2.yaml

@@ -0,0 +1,4 @@
+# Generated by utils.py
+dataset_name: ellipsis_n_bar_2
+include: _template_yaml
+task: blimp_ellipsis_n_bar_2

lm-evaluation-harness/lm_eval/tasks/blimp/existential_there_object_raising.yaml

@@ -0,0 +1,4 @@
+# Generated by utils.py
+dataset_name: existential_there_object_raising
+include: _template_yaml
+task: blimp_existential_there_object_raising

lm-evaluation-harness/lm_eval/tasks/blimp/existential_there_quantifiers_1.yaml

@@ -0,0 +1,4 @@
+# Generated by utils.py
+dataset_name: existential_there_quantifiers_1
+include: _template_yaml
+task: blimp_existential_there_quantifiers_1

lm-evaluation-harness/lm_eval/tasks/blimp/existential_there_quantifiers_2.yaml

@@ -0,0 +1,4 @@
+# Generated by utils.py
+dataset_name: existential_there_quantifiers_2
+include: _template_yaml
+task: blimp_existential_there_quantifiers_2

lm-evaluation-harness/lm_eval/tasks/blimp/expletive_it_object_raising.yaml

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+# Generated by utils.py
+dataset_name: expletive_it_object_raising
+include: _template_yaml
+task: blimp_expletive_it_object_raising

lm-evaluation-harness/lm_eval/tasks/blimp/inchoative.yaml

@@ -0,0 +1,4 @@
+# Generated by utils.py
+dataset_name: inchoative
+include: _template_yaml
+task: blimp_inchoative

lm-evaluation-harness/lm_eval/tasks/blimp/intransitive.yaml

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+# Generated by utils.py
+dataset_name: intransitive
+include: _template_yaml
+task: blimp_intransitive

lm-evaluation-harness/lm_eval/tasks/blimp/irregular_past_participle_adjectives.yaml

@@ -0,0 +1,4 @@
+# Generated by utils.py
+dataset_name: irregular_past_participle_adjectives
+include: _template_yaml
+task: blimp_irregular_past_participle_adjectives

lm-evaluation-harness/lm_eval/tasks/blimp/irregular_past_participle_verbs.yaml

@@ -0,0 +1,4 @@
+# Generated by utils.py
+dataset_name: irregular_past_participle_verbs
+include: _template_yaml
+task: blimp_irregular_past_participle_verbs

lm-evaluation-harness/lm_eval/tasks/blimp/irregular_plural_subject_verb_agreement_1.yaml

@@ -0,0 +1,4 @@
+# Generated by utils.py
+dataset_name: irregular_plural_subject_verb_agreement_1
+include: _template_yaml
+task: blimp_irregular_plural_subject_verb_agreement_1

lm-evaluation-harness/lm_eval/tasks/blimp/irregular_plural_subject_verb_agreement_2.yaml

@@ -0,0 +1,4 @@
+# Generated by utils.py
+dataset_name: irregular_plural_subject_verb_agreement_2
+include: _template_yaml
+task: blimp_irregular_plural_subject_verb_agreement_2

lm-evaluation-harness/lm_eval/tasks/blimp/left_branch_island_echo_question.yaml

@@ -0,0 +1,4 @@
+# Generated by utils.py
+dataset_name: left_branch_island_echo_question
+include: _template_yaml
+task: blimp_left_branch_island_echo_question

lm-evaluation-harness/lm_eval/tasks/blimp/matrix_question_npi_licensor_present.yaml

@@ -0,0 +1,4 @@
+# Generated by utils.py
+dataset_name: matrix_question_npi_licensor_present
+include: _template_yaml
+task: blimp_matrix_question_npi_licensor_present

lm-evaluation-harness/lm_eval/tasks/blimp/only_npi_scope.yaml

@@ -0,0 +1,4 @@
+# Generated by utils.py
+dataset_name: only_npi_scope
+include: _template_yaml
+task: blimp_only_npi_scope

lm-evaluation-harness/lm_eval/tasks/blimp/passive_1.yaml

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+# Generated by utils.py
+dataset_name: passive_1
+include: _template_yaml
+task: blimp_passive_1

lm-evaluation-harness/lm_eval/tasks/blimp/principle_A_case_2.yaml

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+# Generated by utils.py
+dataset_name: principle_A_case_2
+include: _template_yaml
+task: blimp_principle_A_case_2

lm-evaluation-harness/lm_eval/tasks/blimp/principle_A_domain_1.yaml

@@ -0,0 +1,4 @@
+# Generated by utils.py
+dataset_name: principle_A_domain_1
+include: _template_yaml
+task: blimp_principle_A_domain_1

lm-evaluation-harness/lm_eval/tasks/blimp/principle_A_reconstruction.yaml

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+# Generated by utils.py
+dataset_name: principle_A_reconstruction
+include: _template_yaml
+task: blimp_principle_A_reconstruction

lm-evaluation-harness/lm_eval/tasks/blimp/sentential_negation_npi_licensor_present.yaml

@@ -0,0 +1,4 @@
+# Generated by utils.py
+dataset_name: sentential_negation_npi_licensor_present
+include: _template_yaml
+task: blimp_sentential_negation_npi_licensor_present

lm-evaluation-harness/lm_eval/tasks/blimp/sentential_negation_npi_scope.yaml

@@ -0,0 +1,4 @@
+# Generated by utils.py
+dataset_name: sentential_negation_npi_scope
+include: _template_yaml
+task: blimp_sentential_negation_npi_scope

lm-evaluation-harness/lm_eval/tasks/blimp/sentential_subject_island.yaml

@@ -0,0 +1,4 @@
+# Generated by utils.py
+dataset_name: sentential_subject_island
+include: _template_yaml
+task: blimp_sentential_subject_island

lm-evaluation-harness/lm_eval/tasks/blimp/superlative_quantifiers_1.yaml

@@ -0,0 +1,4 @@
+# Generated by utils.py
+dataset_name: superlative_quantifiers_1
+include: _template_yaml
+task: blimp_superlative_quantifiers_1

lm-evaluation-harness/lm_eval/tasks/blimp/superlative_quantifiers_2.yaml

@@ -0,0 +1,4 @@
+# Generated by utils.py
+dataset_name: superlative_quantifiers_2
+include: _template_yaml
+task: blimp_superlative_quantifiers_2

lm-evaluation-harness/lm_eval/tasks/blimp/transitive.yaml

@@ -0,0 +1,4 @@
+# Generated by utils.py
+dataset_name: transitive
+include: _template_yaml
+task: blimp_transitive

lm-evaluation-harness/lm_eval/tasks/blimp/wh_questions_object_gap.yaml

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+# Generated by utils.py
+dataset_name: wh_questions_object_gap
+include: _template_yaml
+task: blimp_wh_questions_object_gap

lm-evaluation-harness/lm_eval/tasks/blimp/wh_questions_subject_gap.yaml

@@ -0,0 +1,4 @@
+# Generated by utils.py
+dataset_name: wh_questions_subject_gap
+include: _template_yaml
+task: blimp_wh_questions_subject_gap

lm-evaluation-harness/lm_eval/tasks/blimp/wh_questions_subject_gap_long_distance.yaml

@@ -0,0 +1,4 @@
+# Generated by utils.py
+dataset_name: wh_questions_subject_gap_long_distance
+include: _template_yaml
+task: blimp_wh_questions_subject_gap_long_distance

lm-evaluation-harness/lm_eval/tasks/blimp/wh_vs_that_no_gap.yaml

@@ -0,0 +1,4 @@
+# Generated by utils.py
+dataset_name: wh_vs_that_no_gap
+include: _template_yaml
+task: blimp_wh_vs_that_no_gap

lm-evaluation-harness/lm_eval/tasks/blimp/wh_vs_that_no_gap_long_distance.yaml

@@ -0,0 +1,4 @@
+# Generated by utils.py
+dataset_name: wh_vs_that_no_gap_long_distance
+include: _template_yaml
+task: blimp_wh_vs_that_no_gap_long_distance

lm-evaluation-harness/lm_eval/tasks/blimp/wh_vs_that_with_gap.yaml

@@ -0,0 +1,4 @@
+# Generated by utils.py
+dataset_name: wh_vs_that_with_gap
+include: _template_yaml
+task: blimp_wh_vs_that_with_gap

lm-evaluation-harness/lm_eval/tasks/eus_reading/README.md

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+# EusReading
+
+### Paper
+
+Title: Latxa: An Open Language Model and Evaluation Suite for Basque
+
+Abstract: https://arxiv.org/abs/2403.20266
+
+EusReading consists of 352 reading comprehension exercises (irakurmena) sourced from the set of past EGA exams from 1998 to 2008. Each test generally has 10 multiple-choice questions, with 4 choices and a single correct answer. These exercises are more challenging than Belebele due to the complexity and length of the input texts. As a result, EusReading is useful to measure long context understanding of models.
+
+Homepage: https://github.com/hitz-zentroa/latxa
+
+
+### Citation
+
+```
+@misc{etxaniz2024latxa,
+      title={Latxa: An Open Language Model and Evaluation Suite for Basque},
+      author={Julen Etxaniz and Oscar Sainz and Naiara Perez and Itziar Aldabe and German Rigau and Eneko Agirre and Aitor Ormazabal and Mikel Artetxe and Aitor Soroa},
+      year={2024},
+      eprint={2403.20266},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```
+
+### Groups and Tasks
+
+#### Groups
+
+There are no groups.
+
+#### Tasks
+
+* `eus_reading`: EusReading consists of 352 reading comprehension exercises (irakurmena) sourced from the set of past EGA exams from 1998 to 2008.
+
+### Checklist
+
+For adding novel benchmarks/datasets to the library:
+* [ ] Is the task an existing benchmark in the literature?
+  * [ ] Have you referenced the original paper that introduced the task?
+  * [ ] If yes, does the original paper provide a reference implementation? If so, have you checked against the reference implementation and documented how to run such a test?
+
+
+If other tasks on this dataset are already supported:
+* [ ] Is the "Main" variant of this task clearly denoted?
+* [ ] Have you provided a short sentence in a README on what each new variant adds / evaluates?
+* [ ] Have you noted which, if any, published evaluation setups are matched by this variant?

lm-evaluation-harness/lm_eval/tasks/eus_reading/eus_reading.yaml

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+dataset_path: HiTZ/EusReading
+dataset_name: default
+task: eus_reading
+doc_to_text: !function utils.doc_to_text_context
+doc_to_choice: !function utils.doc_to_choice
+validation_split: null
+test_split: test
+fewshot_split: test
+output_type: multiple_choice
+doc_to_target: answer
+metric_list:
+  - metric: acc
+    aggregation: mean
+    higher_is_better: true
+metadata:
+  version: 0.0

lm-evaluation-harness/lm_eval/tasks/eus_reading/utils.py

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+from typing import List
+
+
+letters = ["A", "B", "C", "D"]
+
+
+def doc_to_text_context(doc) -> str:
+    """
+    Converts a document to a formatted string.
+
+    Args:
+        doc (dict): A dictionary containing the document information.
+
+    Returns:
+        str: A formatted string containing the question and answer choices.
+    """
+    candidates = doc["candidates"]
+    num_choices = len(candidates)
+    if num_choices < 2:
+        raise ValueError("Invalid number of candidates")
+    choices = letters[:num_choices]
+    formatted_choices = "\n".join(
+        [f"{choice}: {candidates[i]}" for i, choice in enumerate(choices)]
+    )
+    return f"Pasartea: {doc['context']}\n\nGaldera: {doc['question']}\n{formatted_choices}\nErantzuna:"
+
+
+def doc_to_choice(doc) -> List[str]:
+    """
+    Returns the answer choices for a document.
+
+    Args:
+        doc (dict): A dictionary containing the document information.
+
+    Returns:
+        list: A list of strings containing the answer choices.
+    """
+    num_choices = len(doc["candidates"])
+    if num_choices < 2:
+        raise ValueError("Invalid number of candidates")
+    return letters[:num_choices]

lm-evaluation-harness/lm_eval/tasks/okapi/arc_multilingual/arc_de.yaml

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+include: _arc_yaml
+task: arc_de
+dataset_path: alexandrainst/m_arc
+dataset_name: de
+training_split: train
+validation_split: validation
+test_split: test

lm-evaluation-harness/lm_eval/tasks/okapi/arc_multilingual/arc_eu.yaml

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+include: _arc_yaml
+task: arc_eu
+dataset_path: alexandrainst/m_arc
+dataset_name: eu
+training_split: train
+validation_split: validation
+test_split: test