Update README.md

README.md

@@ -32,6 +32,9 @@ widget:
   example_title: Bullish Stock Tweet 2
 - text: Italian companies braced for more political uncertainty
   example_title: Bearish News
+#model-index:
+#- name: FinTwitBERT-sentiment
+#  results:
 ---
 
 # FinTwitBERT-sentiment
@@ -53,34 +56,19 @@ For a comprehensive overview, including the training setup and analysis of the m
 Using [HuggingFace's transformers library](https://huggingface.co/docs/transformers/index) the model and tokenizers can be converted into a pipeline for text classification.
 
 ```python
-from transformers import BertForSequenceClassification, AutoTokenizer, pipeline
+from transformers import pipeline
 
-model = BertForSequenceClassification.from_pretrained(
-    "StephanAkkerman/FinTwitBERT-sentiment",
-    num_labels=3,
-    id2label={0: "NEUTRAL", 1: "BULLISH", 2: "BEARISH"},
-    label2id={"NEUTRAL": 0, "BULLISH": 1, "BEARISH": 2},
+# Create a sentiment analysis pipeline
+pipe = pipeline(
+    "sentiment-analysis",
+    model="StephanAkkerman/FinTwitBERT-sentiment",
+    tokenizer="StephanAkkerman/FinTwitBERT-sentiment",
 )
-model.config.problem_type = "single_label_classification"
-tokenizer = AutoTokenizer.from_pretrained(
-    "StephanAkkerman/FinTwitBERT-sentiment"
-)
-model.eval()
-pipeline = pipeline(
-    "text-classification", model=model, tokenizer=tokenizer
-)
-
-# Sentences we want the sentiment for
-sentence = ["Nice 9% pre market move for $para, pump my calls Uncle Buffett 🤑"]
 
 # Get the predicted sentiment
-print(pipeline(sentence))
+print(pipe("Nice 9% pre market move for $para, pump my calls Uncle Buffett 🤑"))
 ```
 
-## Training
-
-The model was trained with the following parameters:
-
 ## Citing & Authors
 
 If you use FinTwitBERT or FinTwitBERT-sentiment in your research, please cite us as follows, noting that both authors contributed equally to this work: