Instructions to use yuzc19/bert-base-uncased-data-influence-model-lambada with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yuzc19/bert-base-uncased-data-influence-model-lambada with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="yuzc19/bert-base-uncased-data-influence-model-lambada")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("yuzc19/bert-base-uncased-data-influence-model-lambada") model = AutoModelForSequenceClassification.from_pretrained("yuzc19/bert-base-uncased-data-influence-model-lambada", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from yuzc19/bert-base-uncased-data-influence-model-lambada: direct link, hf CLI and curl.
- Browser
- Download file 711 kB
-
https://huggingface.co/yuzc19/bert-base-uncased-data-influence-model-lambada/resolve/refs%2Fpr%2F1/tokenizer.json
- Command line
-
hf download hf://yuzc19/bert-base-uncased-data-influence-model-lambada@refs/pr/1/tokenizer.json
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curl -L -o tokenizer.json https://huggingface.co/yuzc19/bert-base-uncased-data-influence-model-lambada/resolve/refs%2Fpr%2F1/tokenizer.json
711 kB
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