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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("gokuls/BERT-tiny-sst2") model = AutoModelForSequenceClassification.from_pretrained("gokuls/BERT-tiny-sst2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from gokuls/BERT-tiny-sst2: direct link, hf CLI and curl.
- Browser
- Download file 17.6 MB
-
https://huggingface.co/gokuls/BERT-tiny-sst2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://gokuls/BERT-tiny-sst2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/gokuls/BERT-tiny-sst2/resolve/main/pytorch_model.bin
17.6 MB
- Xet hash:
- 282fe9b9e0ed77ca3d700eebb4252b384f8659495dd3496672abf4f909c7bf0f
- Size of remote file:
- 17.6 MB
- SHA256:
- 13cdda851f81a03ca0e49ec735fa380893d39e253a331431ee2c423d29ecda63
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