Update README.md

README.md

@@ -16,9 +16,9 @@ base_model:
 
 ## Model Information
 
-This model is a continually pretrained version of the [google/gemma-2-2b](https://huggingface.co/google/gemma-2-2b) architecture, fine-tuned on extensive Bangla datasets. The primary goal of the continual pretraining was to enhance the model's ability to generate high-quality Bangla text. By extending the pretraining process specifically on Bangla data, the model has demonstrated superior performance in tasks related to Bangla language understanding evaluation benchmarks and text generation.
+This model is a continually pretrained version of the [google/gemma-2-2b](https://huggingface.co/google/gemma-2-2b) architecture, fine-tuned on extensive Bangla datasets. The primary goal of the continual pretraining was to enhance the model's ability to generate high-quality Bangla text. By extending the pretraining process specifically on Bangla data, the model has demonstrated superior performance in Bangla language understanding evaluation benchmarks and text generation tasks.
 
-**Model Architecture:** Gemma 2 is an auto-regressive language model that uses an optimized transformer architecture. 
+**Model Architecture:** Gemma 2 is an auto-regressive language model with optimized transformer architecture. 
 
 |  | Training Data | Params | Input modalities | Output modalities | Context Length | Token count |
 | :---- | :---- | :---- | :---- | :---- | :---- | :---- |
@@ -31,7 +31,7 @@ Below we share some code snippets on how to get quickly started with running the
 pip install -U transformers
 ```
 
-Then, copy the snippet from the section that is relevant for your usecase.
+Then, copy the snippet from the section that is relevant to your use case.
 
 #### Running with the `pipeline` API
 
@@ -59,17 +59,17 @@ print(response)
 
 ## Training Data
 
-**Overview:** We have collected a large Bangla raw dataset of text data from a wide variety of sources. Our collected data so far includes a mix of web documents, books, translated text, transliterated text, transcribe text, code-mixed text, conversations, and open sources raw data. The dataset is cleaned and filtered by different filtering criteria to ensure the quality of the data. Our collected data size is roughly around 268 GB. We separated __22GB__ data from that using a ratio of the data actual data size. Total trained tokens are __3B__ tokens.
+**Overview:** We have collected a large Bangla raw dataset of text data from a wide variety of sources. Our collected data so far includes a mix of web documents, books, translated text, transliterated text, transcribed text, code-mixed text, conversations, and open-source raw data. The dataset is cleaned and filtered by different filtering criteria to ensure the quality of the data. Our collected data size is roughly around 268 GB. We separated __22GB__ data from that using a ratio of the actual data size. Total trained tokens are __3B__ tokens.
 
 Data sources summary:
-- Web documents: Extract, clean, filter common crawl data
-- Books: Extract, clean, filter books data
+- Web documents: Extracted, clean, and filtered common crawl data
+- Books: Extracted, clean, filtered books data
 - Transcribed text: Used in-house Bangla ASR model to transcribe Bangla audio data
-- Translation data: We trained a Bangla-English translation LLM model and used it to translate English data to Bangla
-- Code-mixed data: We trained a Bangla-English code-mixed LLM model and used it to generate code-mixed data
+- Translation data: We trained an English-Bangla translation LLM model and used it to translate English data to Bangla
+- Code-mixed data: We trained an English-Bangla code-mixed LLM model and used it to generate code-mixed data
 - Transliteration data: We trained a Bangla-English transliteration LLM model and used it to generate transliterated data
 - Synthetic data: We generated synthetic data using a Bangla LLM model
-- Others: We scraped data from some selected websites, used open-sources data, and used some other data sources
+- Others: We scrapped some selected website data, used open-source data, and used some other data sources
 
 
 ## Benchmarks \- Bangla Text
@@ -81,7 +81,7 @@ We evaluated our pretrained models on both Bangla and English benchmark datasets
 
 #### Bangla Benchmark datasets
 We evaluated the models on the following datasets:
-- [Bangla MMLU](): A privated multiple choice questions datasets developed by Hishab curated from various sources.
+- [Bangla MMLU](): A private multiple choice question dataset developed by Hishab curated from various sources.
 - [CommonsenseQa Bangla](https://huggingface.co/datasets/hishab/commonsenseqa-bn): A Bangla translation of the CommonsenseQA dataset. The dataset was translated using a new method called Expressive Semantic Translation (EST), which combines Google Machine Translation with LLM-based rewriting modifications.
 - [OpenbookQA Bangla](https://huggingface.co/datasets/hishab/openbookqa-bn): A Bangla translation of the OpenbookQA dataset. The dataset was translated using a new method called Expressive Semantic Translation (EST), which combines Google Machine Translation with LLM-based rewriting modifications.
 - [Piqa Bangla](https://huggingface.co/datasets/hishab/piqa-bn): A Bangla translation of the Piqa dataset. The dataset was translated using a new method called Expressive Semantic Translation (EST), which combines Google Machine Translation with LLM-based rewriting modifications.
@@ -89,10 +89,10 @@ We evaluated the models on the following datasets:
 
 #### English Benchmark datasets
 - [MMLU](https://huggingface.co/datasets/cais/mmlu): This is a massive multitask test consisting of multiple-choice questions from various branches of knowledge. 
-- [CommonseQa](https://huggingface.co/datasets/tau/commonsense_qa): CommonsenseQA is a new multiple-choice question answering dataset that requires different types of commonsense knowledge to predict the correct answers .
+- [CommonseQa](https://huggingface.co/datasets/tau/commonsense_qa): CommonsenseQA is a new multiple-choice question-answering dataset that requires different types of commonsense knowledge to predict the correct answers .
 - [OpenbookQA](https://huggingface.co/datasets/allenai/openbookqa): OpenBookQA aims to promote research in advanced question-answering, probing a deeper understanding of both the topic (with salient facts summarized as an open book, also provided with the dataset) and the language it is expressed in.
 - [Piqa](https://huggingface.co/datasets/ybisk/piqa): The PIQA dataset focuses on physical commonsense reasoning, challenging AI to handle everyday situations requiring practical knowledge and unconventional solutions. Inspired by instructables.com, it aims to enhance AI's ability to understand and reason about physical interactions.
-- [BoolQ](https://huggingface.co/datasets/google/boolq): BoolQ is a question answering dataset for yes/no questions containing 15942 examples. These questions are naturally occurring ---they are generated in unprompted and unconstrained settings. Each example is a triplet of (question, passage, answer), with the title of the page as optional additional context. The text-pair classification setup is similar to existing natural language inference tasks.
+- [BoolQ](https://huggingface.co/datasets/google/boolq): BoolQ is a question-answer dataset for yes/no questions containing 15942 examples. These questions are naturally occurring. They are generated in unprompted and unconstrained settings. Each example is a triplet of (question, passage, answer), with the title of the page as optional additional context. The text-pair classification setup is similar to existing natural language inference tasks.
 
 ### Evaluation Results