Updated readme

README.md

@@ -5,7 +5,7 @@ language:
 
 # Text Classification GoEmotions
 
-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 quantized version in ONNX format can be found [here](https://huggingface.co/minuva/MiniLMv2-toxic-jigsaw-onnx). 
 
 The model with two labels only (toxicity and severe toxicity) is [here](https://huggingface.co/minuva/MiniLMv2-toxic-jigsaw-lite)
@@ -40,5 +40,5 @@ 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 out [fast-nlp-text-toxicity repository](https://github.com/minuva/fast-nlp-text-toxicity) for a FastAPI based server to deploy this model in CPU devices.