Upload README.md

README.md

@@ -9,16 +9,16 @@ license: other
 
 # PolymerNER
 
-This model is a fine-tuned version of the MaterialsBERT model on a dataset of 638 abstracts and contains a linear layer on top of MaterialsBERT to predict the entity type of each token. The entity types predicted by this model are POLYMER, POLYMER\_FAMILY, ORGANIC, INORGANIC, MONOMER, PROP\_NAME, PROP\_VALUE, MATERIAL\_AMOUNT
-This named entity recognition (NER) model was introduced in [this](https://www.nature.com/articles/s41524-023-01003-w) paper. Refer to this paper for a more detailed description of the entity types and performance metrics of this model. As MaterialsBERT is uncased, the NER model is also uncased. 
+This model is a fine-tuned version of the MaterialsBERT model on a dataset of 638 abstracts and contains a linear layer on top of MaterialsBERT to predict the entity type of each token. The entity types predicted by this model are POLYMER, POLYMER\_FAMILY, ORGANIC, INORGANIC, MONOMER, PROP\_NAME, PROP\_VALUE, MATERIAL\_AMOUNT. 
+This named entity recognition (NER) model was introduced in [this](https://www.nature.com/articles/s41524-023-01003-w) paper. Refer to the paper for a more detailed description of the entity types and performance metrics of the model. As MaterialsBERT is uncased, the NER model is also uncased. 
 
 ## Intended uses & limitations
 
-You can use the model for sequence labeling/entity tagging tasks on materials science text. The training, validation and test data for this model consisted of abstracts related to polymers. The entities tagged by this model however are general and can be used with any materials science text to tag the entity types defined in the ontology of this model.
+You can use the model for sequence labeling/entity tagging tasks on materials science text. The training, validation and test data for the model consisted of abstracts related to polymers. The entities tagged by the model however are general and can be used with any materials science text to tag the entity types defined in the ontology of the model.
 
 ## How to Use
 
-Here is how to use this model to tag entities given some text:
+Here is how to use the model to tag entities given some text:
 
 ```python
 from transformers import AutoModelForTokenClassification, AutoTokenizer, pipeline