This model is a fine-tuned version of distilbert/distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.6309
Accuracy: 0.6958

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0005
train_batch_size: 16
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 10
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.6618	0.24	10	0.6505	0.6725
0.6357	0.48	20	0.6373	0.6497
0.6457	0.72	30	0.6226	0.6725
0.646	0.96	40	0.6437	0.6778
0.6448	1.2	50	0.7565	0.6287
0.6339	1.44	60	0.6365	0.6655
0.6207	1.68	70	0.6694	0.6778
0.6217	1.92	80	0.6351	0.6340
0.5928	2.16	90	0.7245	0.6497
0.5938	2.4	100	0.6739	0.6497
0.5873	2.63	110	0.6811	0.6357
0.5442	2.87	120	0.6774	0.6375

Framework versions

Transformers 4.38.2
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.15.2

DarshanDeshpande/distilbert_social_reasoning_reward_model

distilbert_social_reasoning_reward_model

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

DarshanDeshpande/
distilbert_social_reasoning_reward_model