Instructions to use connectivity/bert_ft_qqp-24 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use connectivity/bert_ft_qqp-24 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="connectivity/bert_ft_qqp-24")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("connectivity/bert_ft_qqp-24") model = AutoModelForSequenceClassification.from_pretrained("connectivity/bert_ft_qqp-24", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
Upload qqp_eval@34110steps_bert_ft_qqp-24.json with git-lfs
a0e8b13 -
Upload paws_eval@34110steps_bert_ft_qqp-24.json
c50872e -
Upload paws_eval@25000steps_bert_ft_qqp-24.json
d47656e -
Upload paws_eval@15000steps_bert_ft_qqp-24.json
98bf7d3 -
Added training logs
c004fbd -
Saving weights and logs of step 34110
7082947 -
Saving weights and logs of step 30000
ff36c41 -
Saving weights and logs of step 25000
5a3bf8d -
Saving weights and logs of step 20000
905cfd0 -
Saving weights and logs of step 15000
514be5a -
Saving tokenizer
38d6d91 -
initial commit
a7e1502