up readme Stance-Tw

README.md

@@ -8,7 +8,7 @@ language:
   - en
   
 model-index:
-- name: BEtMan-Tw
+- name: Stance-Tw
   results:
   - task:
       type: stance-classification             # Required. Example: automatic-speech-recognition
@@ -26,7 +26,7 @@ model-index:
 <!-- This model card has been generated automatically according to the information Keras had access to. You should
 probably proofread and complete it, then remove this comment. -->
 
-# BEtMan-Tw
+# Stance-Tw
 
 This model is a fine-tuned version of [j-hartmann/sentiment-roberta-large-english-3-classes](https://huggingface.co/j-hartmann/sentiment-roberta-large-english-3-classes) to predict 3 categories of author stance (attack, support, neutral) towards an entity mentioned in the text.
 
@@ -39,7 +39,7 @@ This model is a fine-tuned version of [j-hartmann/sentiment-roberta-large-englis
 # Model usage
 from transformers import pipeline
 
-model_path = "eevvgg/BEtMan-Tw"
+model_path = "eevvgg/Stance-Tw"
 cls_task = pipeline(task = "text-classification", model = model_path, tokenizer = model_path)#,  device=0 
 
 sequence = ['his rambling has no clear ideas behind it', 
@@ -81,8 +81,8 @@ It achieves the following results on the evaluation set:
 
               precision    recall  f1-score   support
 
-           0      0.762     0.770     0.766       200
-           1      0.759     0.775     0.767       191
-           2      0.769     0.714     0.741        84
+           neutral       0.762     0.770     0.766       200
+           positive      0.759     0.775     0.767       191
+           negative      0.769     0.714     0.741        84