Instructions to use connectivity/bert_ft_qqp-5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use connectivity/bert_ft_qqp-5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="connectivity/bert_ft_qqp-5")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("connectivity/bert_ft_qqp-5") model = AutoModelForSequenceClassification.from_pretrained("connectivity/bert_ft_qqp-5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
Upload qqp_eval@34110steps_bert_ft_qqp-5.json with git-lfs
819f339 -
Upload paws_eval@34110steps_bert_ft_qqp-5.json
c5ec9ca -
Upload paws_eval@25000steps_bert_ft_qqp-5.json
16cd7ff -
Upload paws_eval@15000steps_bert_ft_qqp-5.json
6e58c28 -
Added training logs
c906e74 -
Saving weights and logs of step 34110
dab395a -
Saving weights and logs of step 30000
2a5e1a6 -
Saving weights and logs of step 25000
18b75f6 -
Saving weights and logs of step 20000
39a7004 -
Saving weights and logs of step 15000
11c152f -
Saving tokenizer
a18bff9 -
initial commit
31e3887