Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use DunnBC22/bert-base-uncased-Twitter_Sentiment_Analysis_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DunnBC22/bert-base-uncased-Twitter_Sentiment_Analysis_v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="DunnBC22/bert-base-uncased-Twitter_Sentiment_Analysis_v2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("DunnBC22/bert-base-uncased-Twitter_Sentiment_Analysis_v2") model = AutoModelForSequenceClassification.from_pretrained("DunnBC22/bert-base-uncased-Twitter_Sentiment_Analysis_v2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bd94ea07d368664de846e8ed2bf3ac6f8597ca5950d39593c1b140337fddcc5e
- Size of remote file:
- 3.63 kB
- SHA256:
- 70232632fb788d07ab759d6a2e2b4b1c32f2b5d11a80a20cb5ace04d25501911
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