Instructions to use Jeevesh8/std_0pnt2_bert_ft_cola-76 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/std_0pnt2_bert_ft_cola-76 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/std_0pnt2_bert_ft_cola-76")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/std_0pnt2_bert_ft_cola-76") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/std_0pnt2_bert_ft_cola-76", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
Added training logs
b43a5bb -
Saving weights and logs of step 1603
114a148 -
Saving weights and logs of step 1600
31b3795 -
Saving weights and logs of step 1500
6e86276 -
Saving weights and logs of step 1400
82af8ad -
Saving weights and logs of step 1300
550a61f -
Saving weights and logs of step 1200
8d66934 -
Saving weights and logs of step 1100
535e1e8 -
Saving weights and logs of step 1000
80cd168 -
Saving weights and logs of step 900
b377563 -
Saving weights and logs of step 800
4b2d38c -
Saving weights and logs of step 700
15c684a -
Saving weights and logs of step 600
cf7dfaf -
Saving weights and logs of step 500
36deabb -
Saving weights and logs of step 400
f164f50 -
Saving weights and logs of step 300
db20fc1 -
Saving weights and logs of step 200
f46c630 -
Saving weights and logs of step 100
5b0204e -
Saving weights and logs of step 0
4d5e4c7 -
Saving tokenizer
5eb6abd -
initial commit
0d4be12