Update README.md

README.md

@@ -2,20 +2,21 @@
 base_model: mbrede/amc_setfit
 library_name: setfit
 metrics:
-- accuracy
+- f1
 pipeline_tag: text-classification
 tags:
 - setfit
 - sentence-transformers
 - text-classification
-- generated_from_setfit_trainer
 widget: []
 inference: true
+license: mit
+language: ["multilingual"]
 ---
 
 # SetFit with mbrede/amc_setfit
 
-This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [mbrede/amc_setfit](https://huggingface.co/mbrede/amc_setfit) as the Sentence Transformer embedding model. A OneVsRestClassifier instance is used for classification.
+This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses a finetuned version of [intfloat/multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) as the Sentence Transformer embedding model. A OneVsRestClassifier instance with SGDClassifier estimators is used for classification.
 
 The model has been trained using an efficient few-shot learning technique that involves:
 
@@ -26,11 +27,11 @@ The model has been trained using an efficient few-shot learning technique that i
 
 ### Model Description
 - **Model Type:** SetFit
-- **Sentence Transformer body:** [mbrede/amc_setfit](https://huggingface.co/mbrede/amc_setfit)
+- **Sentence Transformer body:** [intfloat/multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large)
 - **Classification head:** a OneVsRestClassifier instance
 - **Maximum Sequence Length:** 512 tokens
-- **Number of Classes:** 4 classes
-<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
+- **Number of Classes:** 4 classes (nAch: "ach", nAff: "aff", nPow: "pow", Null: "null")
+- **Training Dataset:** [Labeled PSE-stories published by Schönbrodt and colleagues](http://dx.doi.org/10.23668/psycharchives.2738)
 <!-- - **Language:** Unknown -->
 <!-- - **License:** Unknown -->