Instructions to use nlptown/bert-base-multilingual-uncased-sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nlptown/bert-base-multilingual-uncased-sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nlptown/bert-base-multilingual-uncased-sentiment")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nlptown/bert-base-multilingual-uncased-sentiment") model = AutoModelForSequenceClassification.from_pretrained("nlptown/bert-base-multilingual-uncased-sentiment", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Request some information about the model
Hello, I'm a student in University of Science, Vietnam National University Ho Chi Minh City.
I am currently utilizing your pretrained model for my thesis about Group Recommender System, where it plays a crucial role. I'm reaching out to request more information about the model to enhance the depth of my research.
Could you please provide details on the following:
Dataset Details: Information about the dataset used for training, including source, size, and preprocessing steps.
Fine-tuning Process: Insights into the fine-tuning process, training duration, and any specific strategies applied.
Evaluation Metrics: Metrics used for evaluating the model during both training and fine-tuning.
Model Architecture: Details about the model architecture would be greatly beneficial.
Understanding these aspects will significantly contribute to the effectiveness of my thesis. I appreciate your time and any information you can share.
I would really appreciate having that too...