Text Classification
Transformers
Safetensors
PyTorch
English
bert
sentiment-analysis
healthcare
lifestyle
fine-tuned
Eval Results (legacy)
Instructions to use keanteng/bert-sentiment-wqd7005 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use keanteng/bert-sentiment-wqd7005 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="keanteng/bert-sentiment-wqd7005")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("keanteng/bert-sentiment-wqd7005") model = AutoModelForSequenceClassification.from_pretrained("keanteng/bert-sentiment-wqd7005", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9dc021d3772b924b8685515e4e2e13a84acd5a40ea5b295126408ec30e9f8ab1
- Size of remote file:
- 1.34 GB
- SHA256:
- f7bb9202850e093fed1291507239359333b97b8c0ee06ddad35805b518e7ca05
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