Text Classification
Transformers
PyTorch
English
bert
sentiment
sentiment-analysis
text-embeddings-inference
Instructions to use SivaResearch/DSM_509_Assignment_Sentiment_analysis_BERT_SIVA_GOWTHAM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SivaResearch/DSM_509_Assignment_Sentiment_analysis_BERT_SIVA_GOWTHAM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SivaResearch/DSM_509_Assignment_Sentiment_analysis_BERT_SIVA_GOWTHAM")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("SivaResearch/DSM_509_Assignment_Sentiment_analysis_BERT_SIVA_GOWTHAM") model = AutoModelForSequenceClassification.from_pretrained("SivaResearch/DSM_509_Assignment_Sentiment_analysis_BERT_SIVA_GOWTHAM") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
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by SFconvertbot - opened
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