This is a [multilingual BERT](https://huggingface.co/bert-base-multilingual-cased) model fine-tuned on 4000 examples of the 
[NoReC dataset](https://github.com/ltgoslo/norec) where examples with score 1/2 were marked as negative and 5/6 were marked as positive. 
The model was fine-tuned for 2 epochs with the following parameters:
- learning_rate = 3e-05
- warmup_ratio = 0.1
- optim = 'adamw_hf'
- weight_decay = 0.1
{"license": "mit"}
transformers pytorch bert text-classification license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us 

This is a multilingual BERT model fine-tuned on 4000 examples of the 
NoReC dataset where examples with score 1/2 were marked as negative and 5/6 were marked as positive. 
The model was fine-tuned for 2 epochs with the following parameters:
- learning_rate = 3e-05
- warmup_ratio = 0.1
- optim = 'adamw_hf'
- weight_decay = 0.1
TAGS
#transformers #pytorch #bert #text-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us 

TAGS
#transformers #pytorch #bert #text-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us 
