Fix typos

README.md

@@ -32,7 +32,7 @@ Explanations to key concepts in ML
 | [UniLMv2](https://ritvik19.medium.com/papers-explained-unilmv2-5a044ca7c525) | February 2020 | Utilizes a pseudo-masked language model (PMLM) for both autoencoding and partially autoregressive language modeling tasks,significantly advancing the capabilities of language models in diverse NLP tasks. |
 | [FastBERT](https://ritvik19.medium.com/papers-explained-37-fastbert-5bd246c1b432) | April 2020 | A speed-tunable encoder with adaptive inference time having branches at each transformer output to enable early outputs. |
 | [MobileBERT](https://ritvik19.medium.com/papers-explained-36-mobilebert-933abbd5aaf1) | April 2020 | Compressed and faster version of the BERT, featuring bottleneck structures, optimized attention mechanisms, and knowledge transfer. |
-| [Longformer](https://ritvik19.medium.com/papers-explained-38-longformer-9a08416c532e) | April 2020 | Introduces a linearly scalable attention mechanism, allowing handling texts of exteded length. |
+| [Longformer](https://ritvik19.medium.com/papers-explained-38-longformer-9a08416c532e) | April 2020 | Introduces a linearly scalable attention mechanism, allowing handling texts of extended length. |
 | [GPT 3](https://ritvik19.medium.com/papers-explained-66-gpt-3-352f5a1b397) | May 2020 | Demonstrates that scaling up language models greatly improves task-agnostic, few-shot performance. |
 | [DeBERTa](https://ritvik19.medium.com/papers-explained-08-deberta-a808d9b2c52d) | June 2020 | Enhances BERT and RoBERTa through disentangled attention mechanisms, an enhanced mask decoder, and virtual adversarial training. |
 | [DeBERTa v2](https://ritvik19.medium.com/papers-explained-08-deberta-a808d9b2c52d#f5e1) | June 2020 | Enhanced version of the DeBERTa featuring a new vocabulary, nGiE integration, optimized attention mechanisms, additional model sizes, and improved tokenization. |
@@ -42,7 +42,7 @@ Explanations to key concepts in ML
 | [FLAN](https://ritvik19.medium.com/papers-explained-46-flan-1c5e0d5db7c9) | September 2021 | An instruction-tuned language model developed through finetuning on various NLP datasets described by natural language instructions. |
 | [T0](https://ritvik19.medium.com/papers-explained-74-t0-643a53079fe) | October 2021 | A fine tuned encoder-decoder model on a multitask mixture covering a wide variety of tasks, attaining strong zero-shot performance on several standard datasets. |
 | [WebGPT](https://ritvik19.medium.com/papers-explained-123-webgpt-5bb0dd646b32) | December 2021 | A fine-tuned GPT-3 model utilizing text-based web browsing, trained via imitation learning and human feedback, enhancing its ability to answer long-form questions with factual accuracy. |
-| [Gopher](https://ritvik19.medium.com/papers-explained-47-gopher-2e71bbef9e87) | December 2021 | Provides a comprehensive analysis of the performance of various Transformer models across different scales upto 280B on 152 tasks. |
+| [Gopher](https://ritvik19.medium.com/papers-explained-47-gopher-2e71bbef9e87) | December 2021 | Provides a comprehensive analysis of the performance of various Transformer models across different scales up to 280B on 152 tasks. |
 | [LaMDA](https://ritvik19.medium.com/papers-explained-76-lamda-a580ebba1ca2) | January 2022 | Transformer based models specialized for dialog, which are pre-trained on public dialog data and web text. |
 | [Instruct GPT](https://ritvik19.medium.com/papers-explained-48-instructgpt-e9bcd51f03ec) | March 2022 | Fine-tuned GPT using supervised learning (instruction tuning) and reinforcement learning from human feedback to align with user intent. |
 | [CodeGen](https://ritvik19.medium.com/papers-explained-125-codegen-a6bae5c1f7b5) | March 2022 | An LLM trained for program synthesis using input-output examples and natural language descriptions. |
@@ -91,7 +91,7 @@ Explanations to key concepts in ML
 | [OLMo](https://ritvik19.medium.com/papers-explained-98-olmo-fdc358326f9b) | February 2024 | A state-of-the-art, truly open language model and framework that includes training data, code, and tools for building, studying, and advancing language models. |
 | [Orca Math](https://ritvik19.medium.com/papers-explained-163-orca-math-ae6a157ce48d) | February 2024 | A fine tuned Mistral-7B that excels at math problems without external tools, utilizing a high-quality synthetic dataset of 200K problems created through multi-agent collaboration and an iterative learning process that involves practicing problem-solving, receiving feedback, and learning from preference pairs incorporating the model's solutions and feedback. |
 | [Gemma](https://ritvik19.medium.com/papers-explained-106-gemma-ca2b449321ac) | February 2024 | A family of 2B and 7B, state-of-the-art language models based on Google's Gemini models, offering advancements in language understanding, reasoning, and safety. |
-| [Aya 101](https://ritvik19.medium.com/papers-explained-aya-101-d813ba17b83a) | Februray 2024 | A massively multilingual generative language model that follows instructions in 101 languages,trained by finetuning mT5. |
+| [Aya 101](https://ritvik19.medium.com/papers-explained-aya-101-d813ba17b83a) | February 2024 | A massively multilingual generative language model that follows instructions in 101 languages,trained by finetuning mT5. |
 | [Hawk, Griffin](https://ritvik19.medium.com/papers-explained-131-hawk-griffin-dfc8c77f5dcd) |  February 2024 | Introduces Real Gated Linear Recurrent Unit Layer that forms the core of the new recurrent block, replacing Multi-Query Attention for better efficiency and scalability |
 | [WRAP](https://ritvik19.medium.com/papers-explained-118-wrap-e563e009fe56) | March 2024 | Uses an off-the-shelf instruction-tuned model prompted to paraphrase documents on the web in specific styles to jointly pre-train LLMs on real and synthetic rephrases. |
 | [DBRX](https://ritvik19.medium.com/papers-explained-119-dbrx-17c61739983c) | March 2024 | A 132B open, general-purpose fine grained Sparse MoE LLM surpassing GPT-3.5 and competitive with Gemini 1.0 Pro. |