finbert-tone

1.6M
204
512
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109M
2 languages
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by
yiyanghkust
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OTHER
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1.6M downloads
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Edge AI:
Mobile
Laptop
Server
1GB+ RAM
Mobile
Laptop
Server
Quick Summary

--- language: "en" tags: - financial-sentiment-analysis - sentiment-analysis widget: - text: "growth is strong and we have plenty of liquidity" ---

Device Compatibility

Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
1GB+ RAM

Code Examples

How to usepythontransformers
from transformers import BertTokenizer, BertForSequenceClassification
from transformers import pipeline

finbert = BertForSequenceClassification.from_pretrained('yiyanghkust/finbert-tone',num_labels=3)
tokenizer = BertTokenizer.from_pretrained('yiyanghkust/finbert-tone')

nlp = pipeline("sentiment-analysis", model=finbert, tokenizer=tokenizer)

sentences = ["there is a shortage of capital, and we need extra financing",  
             "growth is strong and we have plenty of liquidity", 
             "there are doubts about our finances", 
             "profits are flat"]
results = nlp(sentences)
print(results)  #LABEL_0: neutral; LABEL_1: positive; LABEL_2: negative
How to usepythontransformers
from transformers import BertTokenizer, BertForSequenceClassification
from transformers import pipeline

finbert = BertForSequenceClassification.from_pretrained('yiyanghkust/finbert-tone',num_labels=3)
tokenizer = BertTokenizer.from_pretrained('yiyanghkust/finbert-tone')

nlp = pipeline("sentiment-analysis", model=finbert, tokenizer=tokenizer)

sentences = ["there is a shortage of capital, and we need extra financing",  
             "growth is strong and we have plenty of liquidity", 
             "there are doubts about our finances", 
             "profits are flat"]
results = nlp(sentences)
print(results)  #LABEL_0: neutral; LABEL_1: positive; LABEL_2: negative
How to usepythontransformers
from transformers import BertTokenizer, BertForSequenceClassification
from transformers import pipeline

finbert = BertForSequenceClassification.from_pretrained('yiyanghkust/finbert-tone',num_labels=3)
tokenizer = BertTokenizer.from_pretrained('yiyanghkust/finbert-tone')

nlp = pipeline("sentiment-analysis", model=finbert, tokenizer=tokenizer)

sentences = ["there is a shortage of capital, and we need extra financing",  
             "growth is strong and we have plenty of liquidity", 
             "there are doubts about our finances", 
             "profits are flat"]
results = nlp(sentences)
print(results)  #LABEL_0: neutral; LABEL_1: positive; LABEL_2: negative
How to usepythontransformers
from transformers import BertTokenizer, BertForSequenceClassification
from transformers import pipeline

finbert = BertForSequenceClassification.from_pretrained('yiyanghkust/finbert-tone',num_labels=3)
tokenizer = BertTokenizer.from_pretrained('yiyanghkust/finbert-tone')

nlp = pipeline("sentiment-analysis", model=finbert, tokenizer=tokenizer)

sentences = ["there is a shortage of capital, and we need extra financing",  
             "growth is strong and we have plenty of liquidity", 
             "there are doubts about our finances", 
             "profits are flat"]
results = nlp(sentences)
print(results)  #LABEL_0: neutral; LABEL_1: positive; LABEL_2: negative
How to usepythontransformers
from transformers import BertTokenizer, BertForSequenceClassification
from transformers import pipeline

finbert = BertForSequenceClassification.from_pretrained('yiyanghkust/finbert-tone',num_labels=3)
tokenizer = BertTokenizer.from_pretrained('yiyanghkust/finbert-tone')

nlp = pipeline("sentiment-analysis", model=finbert, tokenizer=tokenizer)

sentences = ["there is a shortage of capital, and we need extra financing",  
             "growth is strong and we have plenty of liquidity", 
             "there are doubts about our finances", 
             "profits are flat"]
results = nlp(sentences)
print(results)  #LABEL_0: neutral; LABEL_1: positive; LABEL_2: negative
How to usepythontransformers
from transformers import BertTokenizer, BertForSequenceClassification
from transformers import pipeline

finbert = BertForSequenceClassification.from_pretrained('yiyanghkust/finbert-tone',num_labels=3)
tokenizer = BertTokenizer.from_pretrained('yiyanghkust/finbert-tone')

nlp = pipeline("sentiment-analysis", model=finbert, tokenizer=tokenizer)

sentences = ["there is a shortage of capital, and we need extra financing",  
             "growth is strong and we have plenty of liquidity", 
             "there are doubts about our finances", 
             "profits are flat"]
results = nlp(sentences)
print(results)  #LABEL_0: neutral; LABEL_1: positive; LABEL_2: negative
How to usepythontransformers
from transformers import BertTokenizer, BertForSequenceClassification
from transformers import pipeline

finbert = BertForSequenceClassification.from_pretrained('yiyanghkust/finbert-tone',num_labels=3)
tokenizer = BertTokenizer.from_pretrained('yiyanghkust/finbert-tone')

nlp = pipeline("sentiment-analysis", model=finbert, tokenizer=tokenizer)

sentences = ["there is a shortage of capital, and we need extra financing",  
             "growth is strong and we have plenty of liquidity", 
             "there are doubts about our finances", 
             "profits are flat"]
results = nlp(sentences)
print(results)  #LABEL_0: neutral; LABEL_1: positive; LABEL_2: negative
How to usepythontransformers
from transformers import BertTokenizer, BertForSequenceClassification
from transformers import pipeline

finbert = BertForSequenceClassification.from_pretrained('yiyanghkust/finbert-tone',num_labels=3)
tokenizer = BertTokenizer.from_pretrained('yiyanghkust/finbert-tone')

nlp = pipeline("sentiment-analysis", model=finbert, tokenizer=tokenizer)

sentences = ["there is a shortage of capital, and we need extra financing",  
             "growth is strong and we have plenty of liquidity", 
             "there are doubts about our finances", 
             "profits are flat"]
results = nlp(sentences)
print(results)  #LABEL_0: neutral; LABEL_1: positive; LABEL_2: negative

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