Instructions to use connectivity/bert_ft_qqp-65 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use connectivity/bert_ft_qqp-65 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="connectivity/bert_ft_qqp-65")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("connectivity/bert_ft_qqp-65") model = AutoModelForSequenceClassification.from_pretrained("connectivity/bert_ft_qqp-65", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
Upload qqp_eval@34110steps_bert_ft_qqp-65.json with git-lfs
a0948cb -
Upload paws_eval@34110steps_bert_ft_qqp-65.json
e10eaed -
Upload paws_eval@25000steps_bert_ft_qqp-65.json
709c10f -
Upload paws_eval@15000steps_bert_ft_qqp-65.json
843ca05 -
Added training logs
08db929 -
Saving weights and logs of step 34110
9437821 -
Saving weights and logs of step 30000
e5b15de -
Saving weights and logs of step 25000
99ffe2c -
Saving weights and logs of step 20000
a9710fb -
Saving weights and logs of step 15000
ed10085 -
Saving tokenizer
56a73ee -
initial commit
3a26b71