Update README.md

README.md

@@ -1,16 +1,22 @@
 ---
 tags:
-- autotrain
 - text-regression
+- sadness
+- emotion
+- emotion intensity
 language:
 - unk
 widget:
-- text: "I love AutoTrain"
+- text: I'm feeling down
 datasets:
-- garrettbaber/autotrain-data-twitter-roberta-base-sadness-intensity
+- SemEval-2018-Task-1-Text-Regression-Task
 co2_eq_emissions:
   emissions: 0.025884770512937715
 ---
+# twitter-roberta-base-sadness-intensity
+This model is a fine-tuned version of cardiffnlp/twitter-roberta-base-2022-154m on the SemEval 2018 - Task 1 Affect in Tweets (subtask: El-reg / text regression).
+
+    Warning: Hosted inference API produces inaccurate values
 
 # Model Trained Using AutoTrain
 
@@ -32,7 +38,7 @@ co2_eq_emissions:
 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 AutoTrain"}' https://api-inference.huggingface.co/models/garrettbaber/autotrain-twitter-roberta-base-sadness-intensity-72772139027
+$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I'm feeling down"}' https://api-inference.huggingface.co/models/garrettbaber/twitter-roberta-base-sadness-intensity
 ```
 
 Or Python API:
@@ -40,11 +46,11 @@ Or Python API:
 ```
 from transformers import AutoModelForSequenceClassification, AutoTokenizer
 
-model = AutoModelForSequenceClassification.from_pretrained("garrettbaber/autotrain-twitter-roberta-base-sadness-intensity-72772139027", use_auth_token=True)
+model = AutoModelForSequenceClassification.from_pretrained("garrettbaber/twitter-roberta-base-sadness-intensity")
 
-tokenizer = AutoTokenizer.from_pretrained("garrettbaber/autotrain-twitter-roberta-base-sadness-intensity-72772139027", use_auth_token=True)
+tokenizer = AutoTokenizer.from_pretrained("garrettbaber/twitter-roberta-base-sadness-intensity")
 
-inputs = tokenizer("I love AutoTrain", return_tensors="pt")
+inputs = tokenizer("I'm feeling down", return_tensors="pt")
 
 outputs = model(**inputs)
 ```