Update README.md

README.md

@@ -3,4 +3,33 @@ license: mit
 language:
 - en
 library_name: transformers
----
+---
+# Model Card for Model ID carolanderson/roberta-base-food-ner
+
+## Model Details
+### Model Description
+Model for tagging mentions of food in the text of recipes. Trained by fine tuning RoBERTa base on a set of about 300 hand-labeled recipes derived from [this dataset from Kaggle.](https://www.kaggle.com/hugodarwood/epirecipes). Achieves an F1 score 0f 0.96 on the custom validation set. 
+
+- **Developed by:** Carol Anderson
+- **Shared by:** Carol Anderson
+- **Language(s) (NLP):** English
+- **License:** MIT
+- **Finetuned from model:** [roberta-base](https://huggingface.co/roberta-base)
+
+### Model Sources
+- **Repository:** [carolmanderson/food](https://github.com/carolmanderson/food/tree/master)
+- **Demo:** [food-ner](https://huggingface.co/spaces/carolanderson/food-ner)
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+```
+from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline
+model = AutoModelForTokenClassification.from_pretrained('carolanderson/roberta-base-food-ner')
+tokenizer = AutoTokenizer.from_pretrained("roberta-base", add_prefix_space=True)
+nlp = pipeline("ner", model=model, tokenizer=tokenizer)
+example = "Saute the onions in olive oil until browned."
+results = nlp(example, aggregation_strategy="first")
+
+```