Update README.md

fix typos.

README.md

@@ -18,10 +18,10 @@ We opensource **Qwen-7B** and **Qwen-7B-Chat** on both **🤖 ModelScope** and *
 
 Qwen-7B is the 7B-parameter version of the large language model series, Qwen (abbr. Tongyi Qianwen), proposed by Alibaba Cloud. Qwen-7B is a Transformer-based large language model, which is pretrained on a large volume of data, including web texts, books, codes, etc. Additionally, based on the pretrained Qwen-7B, we release Qwen-7B-Chat, a large-model-based AI assistant, which is trained with alignment techniques. The features of the Qwen-7B series include:
 
-1. **Trained with high-quality pretraining data**. We have pretrained Qwen-7B on a self-constructed large-scale high-quality dataset of over 2.2 trillion tokens. The dataset includes plain texts and codes, and it covers a wide range of domains, including general domain data and professional damain data.
+1. **Trained with high-quality pretraining data**. We have pretrained Qwen-7B on a self-constructed large-scale high-quality dataset of over 2.2 trillion tokens. The dataset includes plain texts and codes, and it covers a wide range of domains, including general domain data and professional domain data.
 2. **Strong performance**. In comparison with the models of the similar model size, we outperform the competitors on a series of benchmark datasets, which evaluates natural language understanding, mathematics, coding, etc.
 3. **Better support of languages**. Our tokenizer, based on a large vocabulary of over 150K tokens, is a more efficient one compared with other tokenizers. It is friendly to many languages, and it is helpful for users to further finetune Qwen-7B for the extension of understanding a certain language.
-4. **Support of 8K Context Length**. Both Qwen-7B and Qwen-7B-Chat supports the context length of 8K, which allows inputs with long contexts.
+4. **Support of 8K Context Length**. Both Qwen-7B and Qwen-7B-Chat support the context length of 8K, which allows inputs with long contexts.
 5. **Support of Plugins**. Qwen-7B-Chat is trained with plugin-related alignment data, and thus it is capable of using tools, including APIs, models, databases, etc., and it is capable of playing as an agent.
 
 ## News
@@ -30,7 +30,7 @@ Qwen-7B is the 7B-parameter version of the large language model series, Qwen (ab
 
 ## Performance
 
-In general, Qwen-7B outperforms the baseline models of a similar model size, and even outperform larger models of around 13B parameters, on a series of benchmark datasets, e.g., MMLU, C-Eval, GSM8K, HumanEval, and WMT22, etc., which evaluate the models' capabilities on natural language understanding, mathematic problem solving, coding, etc. See the results below.
+In general, Qwen-7B outperforms the baseline models of a similar model size, and even outperforms larger models of around 13B parameters, on a series of benchmark datasets, e.g., MMLU, C-Eval, GSM8K, HumanEval, and WMT22, etc., which evaluate the models' capabilities on natural language understanding, mathematic problem solving, coding, etc. See the results below.
 
 | Model             | MMLU           |         C-Eval |          GSM8K |      HumanEval |  WMT22 (en-zh) |
 | :---------------- | -------------: | -------------: | -------------: | -------------: | -------------: |
@@ -215,7 +215,7 @@ Additionally, we provide experimental results to show its capabilities of playin
 
 ## Long-Context Understanding
 
-To extend the context length and break the botteneck of training sequence length, we introduce several techniques, including NTK-aware interpolation, window attention, LogN attention scaling, to extend the context length to over 8K tokens. We conduct language modeling experiments on the arXiv dataset with the PPL evaluation and find that Qwen-7B can reach outstanding performance in the scenario of long context. Results are demonstrated below:
+To extend the context length and break the bottleneck of training sequence length, we introduce several techniques, including NTK-aware interpolation, window attention, and LogN attention scaling, to extend the context length to over 8K tokens. We conduct language modeling experiments on the arXiv dataset with the PPL evaluation and find that Qwen-7B can reach outstanding performance in the scenario of long context. Results are demonstrated below:
 
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