create model card 2nd version

README.md

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+---
+tags:
+  - text
+  - stance
+  - classification
+
+language:
+  - en
+  
+model-index:
+- name: BEtMan-Tw
+  results:
+  - task:
+      type: stance-classification             # Required. Example: automatic-speech-recognition
+      name: Text Classification            # Optional. Example: Speech Recognition
+    dataset:
+      type: stance          # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
+      name: stance          # Required. A pretty name for the dataset. Example: Common Voice (French)
+    metrics:
+      - type: f1          
+        value: 75.8       
+      - type: accuracy          
+        value: 76.2     
+---
+
+<!-- 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. -->
+
+# BERTweet_EmotAn6
+
+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.
+
+```
+# Model usage
+from transformers import pipeline
+
+model_path = "eevvgg/BEtMan-Tw"
+cls_task = pipeline(task = "text-classification", model = model_path, tokenizer = model_path)#,  device=0 
+
+sequence = ['his rambling has no clear ideas behind it', 
+            'That has nothing to do with medical care',
+            "Turns around and shows how qualified she is because of her political career.",
+            'She has very little to gain by speaking too much']
+            
+result = cls_task(sequence)
+
+labels = [i['label'] for i in result]
+
+labels # ['attack', 'neutral', 'support', 'attack']
+                                        
+```
+
+## Intended uses & limitations
+
+Classification in short text up to 200 tokens (maxlen).
+
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- optimizer: {'name': 'Adam', 'learning_rate': 4e-5, 'decay': 0.01}
+
+
+Trained for 3 epochs, mini-batch size of 8.
+- loss: 0.719
+
+## Evaluation data
+
+It achieves the following results on the evaluation set:
+
+- macro f1-score: 0.758 
+- weighted f1-score: 0.762 
+- accuracy: 0.762
+
+              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
+
+