Moritz-Pfeifer commited on
Commit
899892d
·
1 Parent(s): 8f36530

Update README.md

Browse files
Files changed (1) hide show
  1. README.md +10 -10
README.md CHANGED
@@ -6,22 +6,22 @@ widget:
6
  ---
7
  ## CentralBankRoBERTa
8
 
9
- CentralBankRoBERTA is a large language model. It combines an economic agent classifier that distinguishes five basic macroeconomic agents with a binary [sentiment classifier](Moritz-Pfeifer/CentralBankRoBERTa-sentiment-classifier) that identifies the emotional content of sentences in central bank communications.
10
 
11
  #### Overview
12
 
13
- The AudienceClassifier model is designed to classify the target audience of a given text. It can determine whether the text is adressing **households**, **firms**, **the financial sector**, **the government** or **the central bank** itself. This model is based on the RoBERTa architecture and has been fine-tuned on a diverse and extensive dataset to provide accurate predictions.
14
 
15
  #### Intended Use
16
 
17
- The AudienceClassifier model is intended to be used for the analysis of central bank communications where content categorization based on target audiences is essential.
18
 
19
  #### Performance
20
 
21
- - Accuracy: 93%
22
- - F1 Score: 0.93
23
- - Precision: 0.93
24
- - Recall: 0.93
25
 
26
  ### Usage
27
 
@@ -31,8 +31,8 @@ You can use these models in your own applications by leveraging the Hugging Face
31
  from transformers import pipeline
32
 
33
  # Load the AudienceClassifier model
34
- audience_classifier = pipeline("text-classification", model="Moritz-Pfeifer/CentralBankRoBERTa-audience-classifier")
35
 
36
  # Perform audience classification
37
- audience_result = audience_classifier("We used our liquidity tools to make funding available to banks that might need it.")
38
- print("Audience Classification:", audience_result[0]['label'])
 
6
  ---
7
  ## CentralBankRoBERTa
8
 
9
+ CentralBankRoBERTA is a large language model. It combines an economic [agent classifier](Moritz-Pfeifer/CentralBankRoBERTa-audience-classifier) that distinguishes five basic macroeconomic agents with a binary sentiment classifier that identifies the emotional content of sentences in central bank communications.
10
 
11
  #### Overview
12
 
13
+ The SentimentClassifier model is designed to detect whether a given sentence is positive or negative for either **households**, **firms**, **the financial sector** or **the government**. This model is based on the RoBERTa architecture and has been fine-tuned on a diverse and extensive dataset to provide accurate predictions.
14
 
15
  #### Intended Use
16
 
17
+ The AudienceClassifier model is intended to be used for the analysis of central bank communications where sentiment analysis is essential.
18
 
19
  #### Performance
20
 
21
+ - Accuracy: 88%
22
+ - F1 Score: 0.88
23
+ - Precision: 0.88
24
+ - Recall: 0.88
25
 
26
  ### Usage
27
 
 
31
  from transformers import pipeline
32
 
33
  # Load the AudienceClassifier model
34
+ audience_classifier = pipeline("sentiment-classification", model="Moritz-Pfeifer/CentralBankRoBERTa-sentiment-classifier")
35
 
36
  # Perform audience classification
37
+ sentinemnt_result = audience_classifier("The early effects of our policy tightening are also becoming visible, especially in sectors like manufacturing and construction that are more sensitive to interest rate changes.")
38
+ print("Sentiment Classification:", sentinemnt_result[0]['label'])