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
metadata
library_name: transformers
license: apache-2.0
base_model: google-bert/bert-base-uncased
tags:
- bert
- sentiment-analysis
- text-classification
- transformers
pipeline_tag: text-classification
BERT-sentiment-lora
This model is a fine-tuned version of google-bert/bert-base-uncased for 3-class movie review sentiment classification.
Labels
0→ negative (−1)1→ mixed (0)2→ positive (+1)
Training details
- Architecture: BertForSequenceClassification
- Hidden size: 768, Layers: 12, Heads: 12
- Framework: Transformers 5.3.0