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Several small spelling errors in README

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@@ -104,14 +104,14 @@ The project aims to train sentence embedding models on very large sentence level
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  contrastive learning objective. We used the pretrained [`nreimers/MiniLM-L6-H384-uncased`](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) model and fine-tuned in on a
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  1B sentence pairs dataset. We use a contrastive learning objective: given a sentence from the pair, the model should predict which out of a set of randomly sampled other sentences, was actually paired with it in our dataset.
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- We developped this model during the
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  [Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104),
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- organized by Hugging Face. We developped this model as part of the project:
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  [Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train-the-best-sentence-embedding-model-ever-with-1b-training-pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well as intervention from Googles Flax, JAX, and Cloud team member about efficient deep learning frameworks.
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  ## Intended uses
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- Our model is intented to be used as a sentence and short paragraph encoder. Given an input text, it ouptuts a vector which captures
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  the semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks.
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  By default, input text longer than 256 word pieces is truncated.
@@ -130,7 +130,7 @@ We then apply the cross entropy loss by comparing with true pairs.
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  #### Hyper parameters
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- We trained ou model on a TPU v3-8. We train the model during 100k steps using a batch size of 1024 (128 per TPU core).
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  We use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with
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  a 2e-5 learning rate. The full training script is accessible in this current repository: `train_script.py`.
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  contrastive learning objective. We used the pretrained [`nreimers/MiniLM-L6-H384-uncased`](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) model and fine-tuned in on a
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  1B sentence pairs dataset. We use a contrastive learning objective: given a sentence from the pair, the model should predict which out of a set of randomly sampled other sentences, was actually paired with it in our dataset.
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+ We developed this model during the
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  [Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104),
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+ organized by Hugging Face. We developed this model as part of the project:
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  [Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train-the-best-sentence-embedding-model-ever-with-1b-training-pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well as intervention from Googles Flax, JAX, and Cloud team member about efficient deep learning frameworks.
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  ## Intended uses
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+ Our model is intended to be used as a sentence and short paragraph encoder. Given an input text, it outputs a vector which captures
115
  the semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks.
116
 
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  By default, input text longer than 256 word pieces is truncated.
 
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  #### Hyper parameters
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+ We trained our model on a TPU v3-8. We train the model during 100k steps using a batch size of 1024 (128 per TPU core).
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  We use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with
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  a 2e-5 learning rate. The full training script is accessible in this current repository: `train_script.py`.
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