Instructions to use textattack/bert-base-uncased-QQP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use textattack/bert-base-uncased-QQP with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="textattack/bert-base-uncased-QQP")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("textattack/bert-base-uncased-QQP") model = AutoModelForSequenceClassification.from_pretrained("textattack/bert-base-uncased-QQP", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download eval_results_qqp.txt from textattack/bert-base-uncased-QQP: direct link, hf CLI and curl.
- Browser
- Download file 140 Bytes
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https://huggingface.co/textattack/bert-base-uncased-QQP/resolve/main/eval_results_qqp.txt
- Command line
-
hf download hf://textattack/bert-base-uncased-QQP/eval_results_qqp.txt
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curl -L -o eval_results_qqp.txt https://huggingface.co/textattack/bert-base-uncased-QQP/resolve/main/eval_results_qqp.txt
140 Bytes
| eval_loss = 0.24866826211314508 | |
| eval_acc = 0.9090774177590898 | |
| eval_f1 = 0.8781813361611877 | |
| eval_acc_and_f1 = 0.8936293769601387 | |
| epoch = 3.0 | |