Text Classification
Transformers
Safetensors
English
bert
sentiment-analysis
tinybert
imdb
text-embeddings-inference
Instructions to use Harsha901/tinybert-imdb-sentiment-analysis-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Harsha901/tinybert-imdb-sentiment-analysis-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Harsha901/tinybert-imdb-sentiment-analysis-model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Harsha901/tinybert-imdb-sentiment-analysis-model") model = AutoModelForSequenceClassification.from_pretrained("Harsha901/tinybert-imdb-sentiment-analysis-model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c44e184a6e37a223daf98c720de0c9709df43a5f054f5feb3b0178eddb0157be
- Size of remote file:
- 5.3 kB
- SHA256:
- 50dd879eb8780b119b7386fc972dc23d4cc6fcf93cf5051fc69c7965e51cb262
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.