Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use gokuls/BERT-tiny-sst2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gokuls/BERT-tiny-sst2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="gokuls/BERT-tiny-sst2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("gokuls/BERT-tiny-sst2") model = AutoModelForSequenceClassification.from_pretrained("gokuls/BERT-tiny-sst2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f5ebbdb93d740053f0573f4dc2e88ace106168cbd436c7f8c15444b4f8f4c992
- Size of remote file:
- 3.38 kB
- SHA256:
- 510f961a2eb5dc939054e10d5edbd6420650da1d09fe2fb0f95c6b2aa5eca285
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.