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 training_args.bin from gokuls/BERT-tiny-sst2: direct link, hf CLI and curl.
- Browser
- Download file 3.38 kB
-
https://huggingface.co/gokuls/BERT-tiny-sst2/resolve/main/training_args.bin
- Command line
-
hf download hf://gokuls/BERT-tiny-sst2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/gokuls/BERT-tiny-sst2/resolve/main/training_args.bin
3.38 kB
- Xet hash:
- f5ebbdb93d740053f0573f4dc2e88ace106168cbd436c7f8c15444b4f8f4c992
- Size of remote file:
- 3.38 kB
- SHA256:
- 510f961a2eb5dc939054e10d5edbd6420650da1d09fe2fb0f95c6b2aa5eca285
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