Text Classification
Transformers
Safetensors
English
bert
sentiment analysis
text classification
news
reviews
text-embeddings-inference
Instructions to use mervp/SentimentBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mervp/SentimentBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mervp/SentimentBERT")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mervp/SentimentBERT") model = AutoModelForSequenceClassification.from_pretrained("mervp/SentimentBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 69c529f2051ea351ad2bdef7d45bff8a26286229291153c3d4a3eeaf2e00e984
- Size of remote file:
- 438 MB
- SHA256:
- bf6bf7c8c2645eba53ae47b6013eca03cc35f5d05184d82af1f2fabc373edfba
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