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
| 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](https://huggingface.co/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 | |