Fix typos in About and FAQ sections

src/display/about.py

@@ -35,7 +35,7 @@ We chose these benchmarks as they test a variety of reasoning and general knowle
 ### Results
 You can find:
 - detailed numerical results in the `results` Hugging Face dataset: https://huggingface.co/datasets/open-llm-leaderboard/results
-- details on the input/outputs for the models in the `details` of each model, that you can access by clicking the 📄 emoji after the model name
+- details on the input/outputs for the models in the `details` of each model, which you can access by clicking the 📄 emoji after the model name
 - community queries and running status in the `requests` Hugging Face dataset: https://huggingface.co/datasets/open-llm-leaderboard/requests
 
 If a model's name contains "Flagged", this indicates it has been flagged by the community, and should probably be ignored! Clicking the link will redirect you to the discussion about the model.
@@ -43,7 +43,7 @@ If a model's name contains "Flagged", this indicates it has been flagged by the
 ---------------------------
 
 ## REPRODUCIBILITY
-To reproduce our results, here is the commands you can run, using [this version](https://github.com/EleutherAI/lm-evaluation-harness/tree/b281b0921b636bc36ad05c0b0b0763bd6dd43463) of the Eleuther AI Harness:
+To reproduce our results, here are the commands you can run, using [this version](https://github.com/EleutherAI/lm-evaluation-harness/tree/b281b0921b636bc36ad05c0b0b0763bd6dd43463) of the Eleuther AI Harness:
 `python main.py --model=hf-causal-experimental --model_args="pretrained=<your_model>,use_accelerate=True,revision=<your_model_revision>"`
 ` --tasks=<task_list> --num_fewshot=<n_few_shot> --batch_size=1 --output_path=<output_path>`
 
@@ -56,7 +56,7 @@ python main.py --model=hf-causal-experimental \
     --output_path=<output_path>
 ```
 
-**Note:** we evaluate all models on a single node of 8 H100s, so the global batch batch size is 8 for each evaluation. If you don't use parallelism, adapt your batch size to fit.
+**Note:** We evaluate all models on a single node of 8 H100s, so the global batch size is 8 for each evaluation. If you don't use parallelism, adapt your batch size to fit.
 *You can expect results to vary slightly for different batch sizes because of padding.*
 
 The tasks and few shots parameters are:
@@ -73,7 +73,7 @@ Side note on the baseline scores:
 
 ---------------------------
 
-## RESSOURCES
+## RESOURCES
 
 ### Quantization
 To get more information about quantization, see:
@@ -95,10 +95,10 @@ FAQ_TEXT = """
 
 ## SUBMISSIONS
 My model requires `trust_remote_code=True`, can I submit it?
-- *We only support models that have been integrated in a stable version of the `transformers` library for automatic submission, as we don't want to run possibly unsafe code on our cluster.*
+- *We only support models that have been integrated into a stable version of the `transformers` library for automatic submission, as we don't want to run possibly unsafe code on our cluster.*
 
 What about models of type X? 
-- *We only support models that have been integrated in a stable version of the `transformers` library for automatic submission.*
+- *We only support models that have been integrated into a stable version of the `transformers` library for automatic submission.*
 
 How can I follow when my model is launched?
 - *You can look for its request file [here](https://huggingface.co/datasets/open-llm-leaderboard/requests) and follow the status evolution, or directly in the queues above the submit form.*
@@ -107,7 +107,7 @@ My model disappeared from all the queues, what happened?
 - *A model disappearing from all the queues usually means that there has been a failure. You can check if that is the case by looking for your model [here](https://huggingface.co/datasets/open-llm-leaderboard/requests).*
 
 What causes an evaluation failure?
-- *Most of the failures we get come from problems in the submissions (corrupted files, config problems, wrong parameters selected for eval ...), so we'll be grateful if you first make sure you have followed the steps in `About`. However, from time to time, we have failures on our side (hardware/node failures, problem with an update of our backend, connectivity problem ending up in the results not being saved, ...).*
+- *Most of the failures we get come from problems in the submissions (corrupted files, config problems, wrong parameters selected for eval ...), so we'll be grateful if you first make sure you have followed the steps in `About`. However, from time to time, we have failures on our side (hardware/node failures, problems with an update of our backend, connectivity problems ending up in the results not being saved, ...).*
 
 How can I report an evaluation failure?
 - *As we store the logs for all models, feel free to create an issue, **where you link to the requests file of your model** (look for it [here](https://huggingface.co/datasets/open-llm-leaderboard/requests/tree/main)), so we can investigate! If the model failed due to a problem on our side, we'll relaunch it right away!* 
@@ -124,10 +124,10 @@ What kind of information can I find?
 
 
 Why do models appear several times in the leaderboard? 
-- *We run evaluations with user selected precision and model commit. Sometimes, users submit specific models at different commits and at different precisions (for example, in float16 and 4bit to see how quantization affects performance). You should be able to verify this by displaying the `precision` and `model sha` columns in the display. If, however, you see models appearing several time with the same precision and hash commit, this is not normal.*
+- *We run evaluations with user-selected precision and model commit. Sometimes, users submit specific models at different commits and at different precisions (for example, in float16 and 4bit to see how quantization affects performance). You should be able to verify this by displaying the `precision` and `model sha` columns in the display. If, however, you see models appearing several times with the same precision and hash commit, this is not normal.*
 
 What is this concept of "flagging"?
-- *This mechanism allows user to report models that have unfair performance on the leaderboard. This contains several categories: exceedingly good results on the leaderboard because the model was (maybe accidentally) trained on the evaluation data, models that are copy of other models not atrributed properly, etc.*
+- *This mechanism allows users to report models that have unfair performance on the leaderboard. This contains several categories: exceedingly good results on the leaderboard because the model was (maybe accidentally) trained on the evaluation data, models that are copies of other models not attributed properly, etc.*
 
 My model has been flagged improperly, what can I do?
 - *Every flagged model has a discussion associated with it - feel free to plead your case there, and we'll see what to do together with the community.*
@@ -144,18 +144,18 @@ I need to rename my model, how can I do that?
 ---------------------------
 
 ## OTHER
-Why do you differentiate between pretrained, continously pretrained, fine-tuned, merges, etc ?
-- *These different models do not play in the same categories, and therefore need to be separated for fair comparision. Base pretrained models are the most interesting for the community, as they are usually good models to fine-tune later on - any jump in performance from a pretrained model represents a true improvement on the SOTA. 
-Fine tuned and IFT/RLHF/chat models usually have better performance, but the latter might be more sensitive to system prompts, which we do not cover at the moment in the Open LLM Leaderboard. 
-Merges and moerges have artificially inflated performance on test sets, which is not always explainable, and does not always apply to real world situations.*
+Why do you differentiate between pretrained, continuously pretrained, fine-tuned, merges, etc?
+- *These different models do not play in the same categories, and therefore need to be separated for fair comparison. Base pretrained models are the most interesting for the community, as they are usually good models to fine-tune later on - any jump in performance from a pretrained model represents a true improvement on the SOTA. 
+Fine-tuned and IFT/RLHF/chat models usually have better performance, but the latter might be more sensitive to system prompts, which we do not cover at the moment in the Open LLM Leaderboard. 
+Merges and moerges have artificially inflated performance on test sets, which is not always explainable, and does not always apply to real-world situations.*
 
 What should I use the leaderboard for?
-- *We recommend using the leaderboard for 3 use cases: 1) getting an idea of the state of open pretrained models, by looking only at the ranks and score of this category; 2) experimenting with different fine tuning methods, datasets, quantization techniques, etc, and comparing their score in a reproducible setup, and 3) checking the performance of a model of interest to you, wrt to other models of its category.*
+- *We recommend using the leaderboard for 3 use cases: 1) getting an idea of the state of open pretrained models, by looking only at the ranks and score of this category; 2) experimenting with different fine-tuning methods, datasets, quantization techniques, etc, and comparing their score in a reproducible setup, and 3) checking the performance of a model of interest to you, wrt to other models of its category.*
 
-Why don't you display closed source model scores? 
+Why don't you display closed-source model scores? 
 - *This is a leaderboard for Open models, both for philosophical reasons (openness is cool) and for practical reasons: we want to ensure that the results we display are accurate and reproducible, but 1) commercial closed models can change their API thus rendering any scoring at a given time incorrect 2) we re-run everything on our cluster to ensure all models are run on the same setup and you can't do that for these models.*
 
-I have an issue about accessing the leaderboard through the Gradio API
+I have an issue with accessing the leaderboard through the Gradio API
 - *Since this is not the recommended way to access the leaderboard, we won't provide support for this, but you can look at tools provided by the community for inspiration!*
 
 I have another problem, help!