Update README.md

README.md

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 ---
 language:
 - en
+license: apache-2.0
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 # Text Classification Toxicity
 
-This model is a fined-tuned version of [nreimers/MiniLMv2-L6-H384-distilled-from-BERT-Large](https://huggingface.co/nreimers/MiniLMv2-L6-H384-distilled-from-BERT-Large) on the on the [Jigsaw 1st Kaggle competition](https://www.kaggle.com/competitions/jigsaw-toxic-comment-classification-challenge) dataset using [unitary/toxic-bert](https://huggingface.co/unitary/toxic-bert) as teacher model.
+This model is a fined-tuned version of [MiniLMv2-L6-H384](https://huggingface.co/nreimers/MiniLMv2-L6-H384-distilled-from-BERT-Large) on the on the [Jigsaw 1st Kaggle competition](https://www.kaggle.com/competitions/jigsaw-toxic-comment-classification-challenge) dataset using [unitary/toxic-bert](https://huggingface.co/unitary/toxic-bert) as teacher model.
 The original unquantized model can be found [here](https://huggingface.co/minuva/MiniLMv2-toxic-jigsaw-lite).
 
 The model contains two labels only (toxicity and severe toxicity). For the model with all labels refer to this [page](https://huggingface.co/minuva/MiniLMv2-toxic-jigsaw)
@@ -142,5 +143,4 @@ The following hyperparameters were used during training:
 
 # Deployment
 
-Check this [repository](https://github.com/minuva/toxicity-prediction-serverless) to see how to easily deploy this model in a serverless environment with fast CPU inference and light resource utilization.
-
+Check our [fast-nlp-text-toxicity repository](https://github.com/minuva/fast-nlp-text-toxicity) for a FastAPI and ONNX based server to deploy this model on CPU devices.