Text Classification
Transformers
Safetensors
PyTorch
bert
sentiment-analysis
sst2
glue
text-embeddings-inference
Instructions to use mi55th/bert-sst2-nesterov with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mi55th/bert-sst2-nesterov with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mi55th/bert-sst2-nesterov")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mi55th/bert-sst2-nesterov") model = AutoModelForSequenceClassification.from_pretrained("mi55th/bert-sst2-nesterov", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from mi55th/bert-sst2-nesterov: direct link, hf CLI and curl.
- Browser
- Download file 711 kB
-
https://huggingface.co/mi55th/bert-sst2-nesterov/resolve/main/tokenizer.json
- Command line
-
hf download hf://mi55th/bert-sst2-nesterov/tokenizer.json
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curl -L -o tokenizer.json https://huggingface.co/mi55th/bert-sst2-nesterov/resolve/main/tokenizer.json
711 kB
File too large to display, you can check the raw version instead.