Update README.md

README.md

@@ -2,15 +2,31 @@
 tags: autonlp
 language: en
 widget:
-- text: "I love AutoNLP 🤗"
+- text: "Worry is a down payment on a problem you may never have'. Joyce Meyer.  #motivation #leadership #worry"
 datasets:
-- philschmid/autonlp-data-tweet_eval_vs_comprehend
+- banking77
+model-index:
+- name: RoBERTa-Banking77
+  results:
+  - task: 
+      name: Sentiment Analysis
+      type: sentimenty-analysis
+    dataset:
+      name: "Tweet eval" 
+      type: tweeteval
+    metrics:
+       - name: Accuracy
+         type: accuracy
+         value: 80.59
+       - name: Macro F1
+         type: macro-f1
+         value: 78.17
+       - name: Weighted F1
+         type: weighted-f1
+         value: 80.11
 ---
-
-# Model Trained Using AutoNLP
-
+# `DistilBERT-tweet-eval-emotion` trained using autoNLP
 - Problem type: Multi-class Classification
-- Model ID: 3092245
 
 ## Validation Metrics
 
@@ -32,19 +48,19 @@ datasets:
 You can use cURL to access this model:
 
 ```
-$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/models/philschmid/autonlp-tweet_eval_vs_comprehend-3092245
+$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "Worry is a down payment on a problem you may never have'. Joyce Meyer.  #motivation #leadership #worry"}' https://api-inference.huggingface.co/models/philschmid/autonlp-tweet_eval_vs_comprehend-3092245
 ```
 
 Or Python API:
 
-```
-from transformers import AutoModelForSequenceClassification, AutoTokenizer
-
-model = AutoModelForSequenceClassification.from_pretrained("philschmid/autonlp-tweet_eval_vs_comprehend-3092245", use_auth_token=True)
+```py
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
 
-tokenizer = AutoTokenizer.from_pretrained("philschmid/autonlp-tweet_eval_vs_comprehend-3092245", use_auth_token=True)
+model_id = 'philschmid/DistilBERT-tweet-eval-emotion'
 
-inputs = tokenizer("I love AutoNLP", return_tensors="pt")
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForSequenceClassification.from_pretrained(model_id)
 
-outputs = model(**inputs)
+classifier = pipeline('text-classification', tokenizer=tokenizer, model=model)
+classifier("Worry is a down payment on a problem you may never have'. Joyce Meyer.  #motivation #leadership #worry")
 ```