Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use slickdata/finetuned-Sentiment-classfication-BERT-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use slickdata/finetuned-Sentiment-classfication-BERT-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="slickdata/finetuned-Sentiment-classfication-BERT-model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("slickdata/finetuned-Sentiment-classfication-BERT-model") model = AutoModelForSequenceClassification.from_pretrained("slickdata/finetuned-Sentiment-classfication-BERT-model") - Notebooks
- Google Colab
- Kaggle
Training in progress, step 500
Browse files
pytorch_model.bin
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runs/Jul16_00-50-38_bb4e2871c101/events.out.tfevents.1689468757.bb4e2871c101.1380.0
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training_args.bin
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