Update README.md

README.md

@@ -6,14 +6,14 @@ 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.
-The quantized version in ONNX format can be found [here](minuva/MiniLMv2-toxic-jijgsaw-onnx). The model with two labels only (toxicity and severe toxicity) is [here](minuva/MiniLMv2-toxic-jijgsaw-lite-onnx)
+The quantized version in ONNX format can be found [here](minuva/MiniLMv2-toxic-jigsaw-onnx). The model with two labels only (toxicity and severe toxicity) is [here](minuva/MiniLMv2-toxic-jigsaw-lite-onnx)
 
 # Load the Model
 
 ```py
 from transformers import pipeline
 
-pipe = pipeline(model='minuva/MiniLMv2-toxic-jijgsaw', task='text-classification')
+pipe = pipeline(model='minuva/MiniLMv2-toxic-jigsaw', task='text-classification')
 pipe("This is pure trash")
 # [{'label': 'toxic', 'score': 0.9383478164672852}]
 ```
@@ -34,7 +34,7 @@ The following hyperparameters were used during training:
 
 | Teacher (params)    |   Student (params)     | Set  (metric)     | Score (teacher)    |    Score (student)      |
 |--------------------|-------------|----------|--------| --------|
-| unitary/toxic-bert (110M) |  MiniLMv2-toxic-jijgsaw (23M)  | Test (ROC_AUC)  | 0.98636 |  0.98600 |
+| unitary/toxic-bert (110M) |  MiniLMv2-toxic-jigsaw (23M)  | Test (ROC_AUC)  | 0.98636 |  0.98600 |
 
 # Deployment