sentence-transformers
/

all-MiniLM-L6-v2

@@ -104,14 +104,14 @@ The project aims to train sentence embedding models on very large sentence level
 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
 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.
-We developped this model during the
 [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),
-organized by Hugging Face. We developped this model as part of the project:
 [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.
 ## Intended uses
-Our model is intented to be used as a sentence and short paragraph encoder. Given an input text, it ouptuts a vector which captures
 the semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks.
 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.
 #### Hyper parameters
-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).
 We use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with
 a 2e-5 learning rate. The full training script is accessible in this current repository: `train_script.py`.

 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
 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.
+We developed this model during the
 [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),
+organized by Hugging Face. We developed this model as part of the project:
 [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.
 ## Intended uses
+Our model is intended to be used as a sentence and short paragraph encoder. Given an input text, it outputs a vector which captures
 the semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks.
 By default, input text longer than 256 word pieces is truncated.
 #### Hyper parameters
+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).
 We use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with
 a 2e-5 learning rate. The full training script is accessible in this current repository: `train_script.py`.