Instructions to use cungnlp/FT-BERT-Task3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cungnlp/FT-BERT-Task3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="cungnlp/FT-BERT-Task3")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cungnlp/FT-BERT-Task3") model = AutoModelForSequenceClassification.from_pretrained("cungnlp/FT-BERT-Task3") - Notebooks
- Google Colab
- Kaggle
- 0 - positive;
1 - negative;
2 - neutral
- Mô hình phân lớp văn bản (tiếng Anh) : các câu đánh giá về một vấn đề trong doanh nghiệp như doanh thu, chi phí, ...
- example :
- (negative - label_1) : Net sales fell by 5 % from the previous accounting period .
- (positive - label_0) : With the new production plant the company would increase its capacity to meet the expected increase in demand and would improve the use of raw materials and therefore increase the production profitability
- (neutral - label_2) : Around 250 of these reductions will be implemented through pension arrangements .
0 - positive; 1 - negative; 2 - neutral
Mô hình phân lớp văn bản (tiếng Anh) : các câu đánh giá về một vấn đề trong doanh nghiệp như doanh thu, chi phí, ...
example :
(negative - label_1) : Net sales fell by 5 % from the previous accounting period .
(positive - label_0) : With the new production plant the company would increase its capacity to meet the expected increase in demand and would improve the use of raw materials and therefore increase the production profitability
(neutral - label_2) : Around 250 of these reductions will be implemented through pension arrangements .
- Downloads last month
- 6