metadata

tags:
  - generated_from_trainer
datasets:
  - massive
metrics:
  - accuracy
model-index:
  - name: hbertv2-Massive-intent_48_emb_compress
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: massive
          type: massive
          config: en-US
          split: validation
          args: en-US
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.8489916379734382

hbertv2-Massive-intent_48_emb_compress

This model is a fine-tuned version of gokuls/bert_12_layer_model_v2_complete_training_new_emb_compress_48 on the massive dataset. It achieves the following results on the evaluation set:

Loss: 0.9335
Accuracy: 0.8490

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: 5e-05
train_batch_size: 64
eval_batch_size: 64
seed: 33
distributed_type: multi-GPU
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
2.2651	1.0	180	1.2606	0.6606
1.0649	2.0	360	1.0114	0.7383
0.7609	3.0	540	0.8142	0.7959
0.5705	4.0	720	0.8234	0.7919
0.4182	5.0	900	0.7970	0.8013
0.3164	6.0	1080	0.7823	0.8141
0.233	7.0	1260	0.8068	0.8151
0.1623	8.0	1440	0.8618	0.8234
0.1208	9.0	1620	0.8545	0.8239
0.0823	10.0	1800	0.9072	0.8288
0.0508	11.0	1980	0.8755	0.8431
0.0329	12.0	2160	0.9474	0.8318
0.0181	13.0	2340	0.9236	0.8436
0.0084	14.0	2520	0.9424	0.8470
0.0045	15.0	2700	0.9335	0.8490

Framework versions

Transformers 4.30.2
Pytorch 1.14.0a0+410ce96
Datasets 2.13.0
Tokenizers 0.13.3