Instructions to use alanwang2001/BERT-sentiment-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alanwang2001/BERT-sentiment-lora with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="alanwang2001/BERT-sentiment-lora")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("alanwang2001/BERT-sentiment-lora") model = AutoModelForSequenceClassification.from_pretrained("alanwang2001/BERT-sentiment-lora") - Notebooks
- Google Colab
- Kaggle
BERT-sentiment-lora
This model is a fine-tuned version of google-bert/bert-base-uncased for 3-class movie review sentiment classification.
Labels
0โ negative (โ1)1โ mixed (0)2โ positive (+1)
Training details
- Architecture: BertForSequenceClassification
- Hidden size: 768, Layers: 12, Heads: 12
- Framework: Transformers 5.3.0
- Downloads last month
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Model tree for alanwang2001/BERT-sentiment-lora
Base model
google-bert/bert-base-uncased