docs: improve consistency in punctuation of metric list (huggingface#605)

Co-authored-by: Clémentine Fourrier <[email protected]>

Author: mariagrandury

docs/source/metric-list.mdx

@@ -1,22 +1,22 @@
 # Metric List
 
-## Automatic metrics for multiple choice tasks
+## Automatic metrics for multiple-choice tasks
 
 These metrics use log-likelihood of the different possible targets.
-- `loglikelihood_acc`: Fraction of instances where the choice with the best logprob was correct - also exists in a faster version for tasks where the possible choices include only one token (`loglikelihood_acc_single_token`)
-- `loglikelihood_acc_norm`: Fraction of instances where the choice with the best logprob, normalized by sequence length, was correct - also exists in a faster version for tasks where the possible choices include only one token (`loglikelihood_acc_norm_single_token`)
-- `loglikelihood_acc_norm_nospace`: Fraction of instances where the choice with the best logprob, normalized by sequence length, was correct, with the first space ignored
-- `loglikelihood_f1`: Corpus level F1 score of the multichoice selection - also exists in a faster version for tasks where the possible choices include only one token (`loglikelihood_f1_single_token`)
-- `mcc`: Matthew's correlation coefficient (a measure of agreement between statistical distributions),
-- `recall_at_1`: Fraction of instances where the choice with the best logprob was correct - also exists in a faster version for tasks where the possible choices include only one token per choice (`recall_at_1_single_token`)
-- `recall_at_2`: Fraction of instances where the choice with the 2nd best logprob or better was correct  - also exists in a faster version for tasks where the possible choices include only one token per choice (`recall_at_2_single_token`)
-- `mrr`: Mean reciprocal rank, a measure of the quality of a ranking of choices ordered by correctness/relevance  - also exists in a faster version for tasks where the possible choices include only one token (`mrr_single_token`)
+- `loglikelihood_acc`: Fraction of instances where the choice with the best logprob was correct - also exists in a faster version for tasks where the possible choices include only one token (`loglikelihood_acc_single_token`).
+- `loglikelihood_acc_norm`: Fraction of instances where the choice with the best logprob, normalized by sequence length, was correct - also exists in a faster version for tasks where the possible choices include only one token (`loglikelihood_acc_norm_single_token`).
+- `loglikelihood_acc_norm_nospace`: Fraction of instances where the choice with the best logprob, normalized by sequence length, was correct, with the first space ignored.
+- `loglikelihood_f1`: Corpus level F1 score of the multichoice selection - also exists in a faster version for tasks where the possible choices include only one token (`loglikelihood_f1_single_token`).
+- `mcc`: Matthew's correlation coefficient (a measure of agreement between statistical distributions).
+- `recall_at_1`: Fraction of instances where the choice with the best logprob was correct - also exists in a faster version for tasks where the possible choices include only one token per choice (`recall_at_1_single_token`).
+- `recall_at_2`: Fraction of instances where the choice with the 2nd best logprob or better was correct - also exists in a faster version for tasks where the possible choices include only one token per choice (`recall_at_2_single_token`).
+- `mrr`: Mean reciprocal rank, a measure of the quality of a ranking of choices ordered by correctness/relevance - also exists in a faster version for tasks where the possible choices include only one token (`mrr_single_token`).
 - `target_perplexity`: Perplexity of the different choices available.
-- `acc_golds_likelihood`:: A bit different, it actually checks if the average logprob of a single target is above or below 0.5
-- `multi_f1_numeric`: Loglikelihood F1 score for multiple gold targets
+- `acc_golds_likelihood`: A bit different, it actually checks if the average logprob of a single target is above or below 0.5.
+- `multi_f1_numeric`: Loglikelihood F1 score for multiple gold targets.
 
 All these metrics also exist in a "single token" version (`loglikelihood_acc_single_token`, `loglikelihood_acc_norm_single_token`, `loglikelihood_f1_single_token`, `mcc_single_token`, `recall@2_single_token` and `mrr_single_token`). When the multichoice option compares only one token (ex: "A" vs "B" vs "C" vs "D", or "yes" vs "no"), using these metrics in the single token version will divide the time spent by the number of choices. Single token evals also include:
-- `multi_f1_numeric`: computes the f1 score of all possible choices and averages it.
+- `multi_f1_numeric`: Computes the f1 score of all possible choices and averages it.
 
 ## Automatic metrics for perplexity and language modeling
 These metrics use log-likelihood of prompt.
@@ -32,45 +32,45 @@ These metrics need the model to generate an output. They are therefore slower.
     - `exact_match`: Fraction of instances where the prediction matches the gold with the exception of the border whitespaces (= after a `strip` has been applied to both).
     - `quasi_exact_match`: Fraction of instances where the normalized prediction matches the normalized gold (normalization done on whitespace, articles, capitalization, ...). Other variations exist, with other normalizers, such as `quasi_exact_match_triviaqa`, which only normalizes the predictions after applying a strip to all sentences.
     - `prefix_exact_match`: Fraction of instances where the beginning of the prediction matches the gold at the exception of the border whitespaces (= after a `strip` has been applied to both).
-    - `prefix_quasi_exact_match`: Fraction of instances where the normalized beginning of the prediction matches the normalized gold (normalization done on whitespace, articles, capitalization, ...)
-    - `exact_match_indicator`: Exact match with some preceding context (before an indicator) removed
-    - `f1_score_quasi`: Average F1 score in terms of word overlap between the model output and gold, with both being normalized first
-    - `f1_score`:  Average F1 score in terms of word overlap between the model output and gold without normalisation
-    - `f1_score_macro`: Corpus level macro F1 score
-    - `f1_score_macro`: Corpus level micro F1 score
+    - `prefix_quasi_exact_match`: Fraction of instances where the normalized beginning of the prediction matches the normalized gold (normalization done on whitespace, articles, capitalization, ...).
+    - `exact_match_indicator`: Exact match with some preceding context (before an indicator) removed.
+    - `f1_score_quasi`: Average F1 score in terms of word overlap between the model output and gold, with both being normalized first.
+    - `f1_score`:  Average F1 score in terms of word overlap between the model output and gold without normalisation.
+    - `f1_score_macro`: Corpus level macro F1 score.
+    - `f1_score_macro`: Corpus level micro F1 score.
     - `maj_at_5` and `maj_at_8`: Model majority vote. Takes n (5 or 8) generations from the model and assumes the most frequent is the actual prediction.
 - Summarization:
-    - `rouge`: Average ROUGE score [(Lin, 2004)](https://aclanthology.org/W04-1013/)
+    - `rouge`: Average ROUGE score [(Lin, 2004)](https://aclanthology.org/W04-1013/).
     - `rouge1`: Average ROUGE score [(Lin, 2004)](https://aclanthology.org/W04-1013/) based on 1-gram overlap.
     - `rouge2`: Average ROUGE score [(Lin, 2004)](https://aclanthology.org/W04-1013/) based on 2-gram overlap.
     - `rougeL`: Average ROUGE score [(Lin, 2004)](https://aclanthology.org/W04-1013/) based on longest common subsequence overlap.
     - `rougeLsum`: Average ROUGE score [(Lin, 2004)](https://aclanthology.org/W04-1013/) based on longest common subsequence overlap.
-    - `rouge_t5` (BigBench): Corpus level ROUGE score for all available ROUGE metrics
+    - `rouge_t5` (BigBench): Corpus level ROUGE score for all available ROUGE metrics.
     - `faithfulness`: Faithfulness scores based on the SummaC method of [Laban et al. (2022)](https://aclanthology.org/2022.tacl-1.10/).
-    - `extractiveness`: Reports, based on [(Grusky et al., 2018)](https://aclanthology.org/N18-1065/)
+    - `extractiveness`: Reports, based on [(Grusky et al., 2018)](https://aclanthology.org/N18-1065/):
         - `summarization_coverage`: Extent to which the model-generated summaries are extractive fragments from the source document,
         - `summarization_density`: Extent to which the model-generated summaries are extractive summaries based on the source document,
         - `summarization_compression`: Extent to which the model-generated summaries are compressed relative to the source document.
     - `bert_score`: Reports the average BERTScore precision, recall, and f1 score [(Zhang et al., 2020)](https://openreview.net/pdf?id=SkeHuCVFDr) between model generation and gold summary.
-    - Translation
+- Translation:
     - `bleu`: Corpus level BLEU score [(Papineni et al., 2002)](https://aclanthology.org/P02-1040/) - uses the sacrebleu implementation.
     - `bleu_1`: Average sample BLEU score [(Papineni et al., 2002)](https://aclanthology.org/P02-1040/) based on 1-gram overlap - uses the nltk implementation.
     - `bleu_4`: Average sample BLEU score [(Papineni et al., 2002)](https://aclanthology.org/P02-1040/) based on 4-gram overlap - uses the nltk implementation.
     - `chrf`: Character n-gram matches f-score.
     - `ter`: Translation edit/error rate.
-- Copyright
+- Copyright:
     - `copyright`: Reports:
-        - `longest_common_prefix_length`: average length of longest common prefix between model generation and reference,
-        - `edit_distance`: average Levenshtein edit distance between model generation and reference,
-        - `edit_similarity`: average Levenshtein edit similarity (normalized by length of longer sequence) between model generation and reference.
+        - `longest_common_prefix_length`: Average length of longest common prefix between model generation and reference,
+        - `edit_distance`: Average Levenshtein edit distance between model generation and reference,
+        - `edit_similarity`: Average Levenshtein edit similarity (normalized by the length of longer sequence) between model generation and reference.
 - Math:
-    - `quasi_exact_match_math`: Fraction of instances where the normalized prediction matches the normalized gold (normalization done for math, where latex symbols, units, etc are removed)
-    - `maj_at_4_math`: Majority choice evaluation, using the math normalisation for the predictions and gold
-    - `quasi_exact_match_gsm8k`: Fraction of instances where the normalized prediction matches the normalized gold (normalization done for gsm8k, where latex symbols, units, etc are removed)
-    - `maj_at_8_gsm8k`: Majority choice evaluation, using the gsm8k normalisation for the predictions and gold
+    - `quasi_exact_match_math`: Fraction of instances where the normalized prediction matches the normalized gold (normalization done for math, where latex symbols, units, etc are removed).
+    - `maj_at_4_math`: Majority choice evaluation, using the math normalisation for the predictions and gold.
+    - `quasi_exact_match_gsm8k`: Fraction of instances where the normalized prediction matches the normalized gold (normalization done for gsm8k, where latex symbols, units, etc are removed).
+    - `maj_at_8_gsm8k`: Majority choice evaluation, using the gsm8k normalisation for the predictions and gold.
 
 ## LLM-as-Judge
-- `llm_judge_gpt3p5`: Can be used for any generative task, the model will be scored by a GPT3.5 model using the OpenAI API
-- `llm_judge_llama_3_405b`: Can be used for any generative task, the model will be scored by a Llama 3.405B model using the HuggingFace API
+- `llm_judge_gpt3p5`: Can be used for any generative task, the model will be scored by a GPT3.5 model using the OpenAI API.
+- `llm_judge_llama_3_405b`: Can be used for any generative task, the model will be scored by a Llama 3.405B model using the HuggingFace API.
 - `llm_judge_multi_turn_gpt3p5`: Can be used for any generative task, the model will be scored by a GPT3.5 model using the OpenAI API. It is used for multiturn tasks like mt-bench.
 - `llm_judge_multi_turn_llama_3_405b`: Can be used for any generative task, the model will be scored by a Llama 3.405B model using the HuggingFace API. It is used for multiturn tasks like mt-bench.