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
Adding `safetensors` variant of this model
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by SFconvertbot - opened
- model.safetensors +3 -0
model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:ba8cc1e4ba2df76003ae4bfbb508abcbffb3776f6f56eabe08c6e77a17e976f1
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size 437965908
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