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:
- 9b6aef9474ef909face9889b00ded4b0068c4f26a18699cb5304ab7d6a7813dc
- Size of remote file:
- 438 MB
- SHA256:
- 08b7cc3505184ad6af6b1823e84b212987f1262cc72b7d927e1ea6b61dd6fc6c
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